Merge remote-tracking branch 'origin/mobile_face' into mobile_face

2024-04-12 15:11:15 +05:30 · 2024-04-12 15:11:15 +05:30 · 21451efa6b
commit 21451efa6b
parent 481410f183 259787ff41
90 changed files with 701 additions and 17309 deletions
--- a/desktop/docs/dependencies.md
+++ b/desktop/docs/dependencies.md
@ -1,8 +1,8 @@
 # Dependencies

-* [Electron](#electron)
-* [Dev dependencies](#dev)
-* [Functionality](#functionality)
+-   [Electron](#electron)
+-   [Dev dependencies](#dev)
+-   [Functionality](#functionality)

 ## Electron

@ -114,8 +114,8 @@ available on the host machine, and is not bundled with our app.
 AI/ML runtime. It powers both natural language searches (using CLIP) and face
 detection (using YOLO).

-[jpeg-js](https://github.com/jpeg-js/jpeg-js#readme) is used for decoding
-JPEG data into raw RGB bytes before passing it to ONNX.
+[jpeg-js](https://github.com/jpeg-js/jpeg-js#readme) is used for decoding JPEG
+data into raw RGB bytes before passing it to ONNX.

 html-entities is used by the bundled clip-bpe-ts tokenizer for CLIP.

--- a/desktop/src/main/ipc.ts
+++ b/desktop/src/main/ipc.ts
@ -36,13 +36,14 @@ import {
    updateAndRestart,
    updateOnNextRestart,
 } from "./services/app-update";
-import { clipImageEmbedding, clipTextEmbedding } from "./services/clip";
 import { runFFmpegCmd } from "./services/ffmpeg";
 import { getDirFiles } from "./services/fs";
 import {
    convertToJPEG,
    generateImageThumbnail,
 } from "./services/imageProcessor";
+import { clipImageEmbedding, clipTextEmbedding } from "./services/ml-clip";
+import { detectFaces, faceEmbedding } from "./services/ml-face";
 import {
    clearStores,
    encryptionKey,
@ -146,6 +147,14 @@ export const attachIPCHandlers = () => {
        clipTextEmbedding(text),
    );

+    ipcMain.handle("detectFaces", (_, input: Float32Array) =>
+        detectFaces(input),
+    );
+
+    ipcMain.handle("faceEmbedding", (_, input: Float32Array) =>
+        faceEmbedding(input),
+    );
+
    // - File selection

    ipcMain.handle("selectDirectory", () => selectDirectory());
--- a/desktop/src/main/services/ml-clip.ts
+++ b/desktop/src/main/services/ml-clip.ts
@ -1,19 +1,13 @@
 /**
- * @file Compute CLIP embeddings
+ * @file Compute CLIP embeddings for images and text.
 *
- * @see `web/apps/photos/src/services/clip-service.ts` for more details. This
- * file implements the Node.js implementation of the actual embedding
- * computation. By doing it in the Node.js layer, we can use the binary ONNX
- * runtimes which are 10-20x faster than the WASM based web ones.
+ * The embeddings are computed using ONNX runtime, with CLIP as the model.
 *
- * The embeddings are computed using ONNX runtime. The model itself is not
- * shipped with the app but is downloaded on demand.
+ * @see `web/apps/photos/src/services/clip-service.ts` for more details.
 */
-import { app, net } from "electron/main";
 import { existsSync } from "fs";
 import jpeg from "jpeg-js";
 import fs from "node:fs/promises";
-import path from "node:path";
 import * as ort from "onnxruntime-node";
 import Tokenizer from "../../thirdparty/clip-bpe-ts/mod";
 import { CustomErrors } from "../../types/ipc";
@ -21,6 +15,12 @@ import { writeStream } from "../fs";
 import log from "../log";
 import { generateTempFilePath } from "../temp";
 import { deleteTempFile } from "./ffmpeg";
+import {
+    createInferenceSession,
+    downloadModel,
+    modelPathDownloadingIfNeeded,
+    modelSavePath,
+} from "./ml";

 const textModelName = "clip-text-vit-32-uint8.onnx";
 const textModelByteSize = 64173509; // 61.2 MB
@ -28,55 +28,20 @@ const textModelByteSize = 64173509; // 61.2 MB
 const imageModelName = "clip-image-vit-32-float32.onnx";
 const imageModelByteSize = 351468764; // 335.2 MB

-/** Return the path where the given {@link modelName} is meant to be saved */
-const modelSavePath = (modelName: string) =>
-    path.join(app.getPath("userData"), "models", modelName);
-
-const downloadModel = async (saveLocation: string, name: string) => {
-    // `mkdir -p` the directory where we want to save the model.
-    const saveDir = path.dirname(saveLocation);
-    await fs.mkdir(saveDir, { recursive: true });
-    // Download
-    log.info(`Downloading CLIP model from ${name}`);
-    const url = `https://models.ente.io/${name}`;
-    const res = await net.fetch(url);
-    if (!res.ok) throw new Error(`Failed to fetch ${url}: HTTP ${res.status}`);
-    // Save
-    await writeStream(saveLocation, res.body);
-    log.info(`Downloaded CLIP model ${name}`);
-};
-
-let activeImageModelDownload: Promise<void> | undefined;
+let activeImageModelDownload: Promise<string> | undefined;

 const imageModelPathDownloadingIfNeeded = async () => {
    try {
-        const modelPath = modelSavePath(imageModelName);
        if (activeImageModelDownload) {
            log.info("Waiting for CLIP image model download to finish");
            await activeImageModelDownload;
        } else {
-            if (!existsSync(modelPath)) {
-                log.info("CLIP image model not found, downloading");
-                activeImageModelDownload = downloadModel(
-                    modelPath,
-                    imageModelName,
-                );
-                await activeImageModelDownload;
-            } else {
-                const localFileSize = (await fs.stat(modelPath)).size;
-                if (localFileSize !== imageModelByteSize) {
-                    log.error(
-                        `CLIP image model size ${localFileSize} does not match the expected size, downloading again`,
-                    );
-                    activeImageModelDownload = downloadModel(
-                        modelPath,
-                        imageModelName,
-                    );
-                    await activeImageModelDownload;
-                }
-            }
+            activeImageModelDownload = modelPathDownloadingIfNeeded(
+                imageModelName,
+                imageModelByteSize,
+            );
+            return await activeImageModelDownload;
        }
-        return modelPath;
    } finally {
        activeImageModelDownload = undefined;
    }
@ -84,6 +49,8 @@ const imageModelPathDownloadingIfNeeded = async () => {

 let textModelDownloadInProgress = false;

+/* TODO(MR): use the generic method. Then we can remove the exports for the
+   internal details functions that we use here */
 const textModelPathDownloadingIfNeeded = async () => {
    if (textModelDownloadInProgress)
        throw Error(CustomErrors.MODEL_DOWNLOAD_PENDING);
@ -123,13 +90,6 @@ const textModelPathDownloadingIfNeeded = async () => {
    return modelPath;
 };

-const createInferenceSession = async (modelPath: string) => {
-    return await ort.InferenceSession.create(modelPath, {
-        intraOpNumThreads: 1,
-        enableCpuMemArena: false,
-    });
-};
-
 let imageSessionPromise: Promise<any> | undefined;

 const onnxImageSession = async () => {
@ -174,7 +134,7 @@ const clipImageEmbedding_ = async (jpegFilePath: string) => {
    const results = await imageSession.run(feeds);
    log.debug(
        () =>
-            `CLIP image embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
+            `onnx/clip image embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
    );
    const imageEmbedding = results["output"].data; // Float32Array
    return normalizeEmbedding(imageEmbedding);
@ -281,7 +241,7 @@ export const clipTextEmbedding = async (text: string) => {
    const results = await imageSession.run(feeds);
    log.debug(
        () =>
-            `CLIP text embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
+            `onnx/clip text embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
    );
    const textEmbedding = results["output"].data;
    return normalizeEmbedding(textEmbedding);
--- a/desktop/src/main/services/ml-face.ts
+++ b/desktop/src/main/services/ml-face.ts
@ -0,0 +1,108 @@
+/**
+ * @file Various face recognition related tasks.
+ *
+ * - Face detection with the YOLO model.
+ * - Face embedding with the MobileFaceNet model.
+ *
+ * The runtime used is ONNX.
+ */
+import * as ort from "onnxruntime-node";
+import log from "../log";
+import { createInferenceSession, modelPathDownloadingIfNeeded } from "./ml";
+
+const faceDetectionModelName = "yolov5s_face_640_640_dynamic.onnx";
+const faceDetectionModelByteSize = 30762872; // 29.3 MB
+
+const faceEmbeddingModelName = "mobilefacenet_opset15.onnx";
+const faceEmbeddingModelByteSize = 5286998; // 5 MB
+
+let activeFaceDetectionModelDownload: Promise<string> | undefined;
+
+const faceDetectionModelPathDownloadingIfNeeded = async () => {
+    try {
+        if (activeFaceDetectionModelDownload) {
+            log.info("Waiting for face detection model download to finish");
+            await activeFaceDetectionModelDownload;
+        } else {
+            activeFaceDetectionModelDownload = modelPathDownloadingIfNeeded(
+                faceDetectionModelName,
+                faceDetectionModelByteSize,
+            );
+            return await activeFaceDetectionModelDownload;
+        }
+    } finally {
+        activeFaceDetectionModelDownload = undefined;
+    }
+};
+
+let _faceDetectionSession: Promise<ort.InferenceSession> | undefined;
+
+const faceDetectionSession = async () => {
+    if (!_faceDetectionSession) {
+        _faceDetectionSession =
+            faceDetectionModelPathDownloadingIfNeeded().then((modelPath) =>
+                createInferenceSession(modelPath),
+            );
+    }
+    return _faceDetectionSession;
+};
+
+let activeFaceEmbeddingModelDownload: Promise<string> | undefined;
+
+const faceEmbeddingModelPathDownloadingIfNeeded = async () => {
+    try {
+        if (activeFaceEmbeddingModelDownload) {
+            log.info("Waiting for face embedding model download to finish");
+            await activeFaceEmbeddingModelDownload;
+        } else {
+            activeFaceEmbeddingModelDownload = modelPathDownloadingIfNeeded(
+                faceEmbeddingModelName,
+                faceEmbeddingModelByteSize,
+            );
+            return await activeFaceEmbeddingModelDownload;
+        }
+    } finally {
+        activeFaceEmbeddingModelDownload = undefined;
+    }
+};
+
+let _faceEmbeddingSession: Promise<ort.InferenceSession> | undefined;
+
+const faceEmbeddingSession = async () => {
+    if (!_faceEmbeddingSession) {
+        _faceEmbeddingSession =
+            faceEmbeddingModelPathDownloadingIfNeeded().then((modelPath) =>
+                createInferenceSession(modelPath),
+            );
+    }
+    return _faceEmbeddingSession;
+};
+
+export const detectFaces = async (input: Float32Array) => {
+    const session = await faceDetectionSession();
+    const t = Date.now();
+    const feeds = {
+        input: new ort.Tensor("float32", input, [1, 3, 640, 640]),
+    };
+    const results = await session.run(feeds);
+    log.debug(() => `onnx/yolo face detection took ${Date.now() - t} ms`);
+    return results["output"].data;
+};
+
+export const faceEmbedding = async (input: Float32Array) => {
+    // Dimension of each face (alias)
+    const mobileFaceNetFaceSize = 112;
+    // Smaller alias
+    const z = mobileFaceNetFaceSize;
+    // Size of each face's data in the batch
+    const n = Math.round(input.length / (z * z * 3));
+    const inputTensor = new ort.Tensor("float32", input, [n, z, z, 3]);
+
+    const session = await faceEmbeddingSession();
+    const t = Date.now();
+    const feeds = { img_inputs: inputTensor };
+    const results = await session.run(feeds);
+    log.debug(() => `onnx/yolo face embedding took ${Date.now() - t} ms`);
+    // TODO: What's with this type? It works in practice, but double check.
+    return (results.embeddings as unknown as any)["cpuData"]; // as Float32Array;
+};
--- a/desktop/src/main/services/ml.ts
+++ b/desktop/src/main/services/ml.ts
@ -0,0 +1,79 @@
+/**
+ * @file AI/ML related functionality.
+ *
+ * @see also `ml-clip.ts`, `ml-face.ts`.
+ *
+ * The ML runtime we use for inference is [ONNX](https://onnxruntime.ai). Models
+ * for various tasks are not shipped with the app but are downloaded on demand.
+ *
+ * The primary reason for doing these tasks in the Node.js layer is so that we
+ * can use the binary ONNX runtime which is 10-20x faster than the WASM based
+ * web one.
+ */
+import { app, net } from "electron/main";
+import { existsSync } from "fs";
+import fs from "node:fs/promises";
+import path from "node:path";
+import * as ort from "onnxruntime-node";
+import { writeStream } from "../fs";
+import log from "../log";
+
+/**
+ * Download the model named {@link modelName} if we don't already have it.
+ *
+ * Also verify that the size of the model we get matches {@expectedByteSize} (if
+ * not, redownload it).
+ *
+ * @returns the path to the model on the local machine.
+ */
+export const modelPathDownloadingIfNeeded = async (
+    modelName: string,
+    expectedByteSize: number,
+) => {
+    const modelPath = modelSavePath(modelName);
+
+    if (!existsSync(modelPath)) {
+        log.info("CLIP image model not found, downloading");
+        await downloadModel(modelPath, modelName);
+    } else {
+        const size = (await fs.stat(modelPath)).size;
+        if (size !== expectedByteSize) {
+            log.error(
+                `The size ${size} of model ${modelName} does not match the expected size, downloading again`,
+            );
+            await downloadModel(modelPath, modelName);
+        }
+    }
+
+    return modelPath;
+};
+
+/** Return the path where the given {@link modelName} is meant to be saved */
+export const modelSavePath = (modelName: string) =>
+    path.join(app.getPath("userData"), "models", modelName);
+
+export const downloadModel = async (saveLocation: string, name: string) => {
+    // `mkdir -p` the directory where we want to save the model.
+    const saveDir = path.dirname(saveLocation);
+    await fs.mkdir(saveDir, { recursive: true });
+    // Download
+    log.info(`Downloading ML model from ${name}`);
+    const url = `https://models.ente.io/${name}`;
+    const res = await net.fetch(url);
+    if (!res.ok) throw new Error(`Failed to fetch ${url}: HTTP ${res.status}`);
+    // Save
+    await writeStream(saveLocation, res.body);
+    log.info(`Downloaded CLIP model ${name}`);
+};
+
+/**
+ * Crete an ONNX {@link InferenceSession} with some defaults.
+ */
+export const createInferenceSession = async (modelPath: string) => {
+    return await ort.InferenceSession.create(modelPath, {
+        // Restrict the number of threads to 1
+        intraOpNumThreads: 1,
+        // Be more conservative with RAM usage
+        enableCpuMemArena: false,
+    });
+};
--- a/desktop/src/preload.ts
+++ b/desktop/src/preload.ts
@ -143,6 +143,12 @@ const clipImageEmbedding = (jpegImageData: Uint8Array): Promise<Float32Array> =>
 const clipTextEmbedding = (text: string): Promise<Float32Array> =>
    ipcRenderer.invoke("clipTextEmbedding", text);

+const detectFaces = (input: Float32Array): Promise<Float32Array> =>
+    ipcRenderer.invoke("detectFaces", input);
+
+const faceEmbedding = (input: Float32Array): Promise<Float32Array> =>
+    ipcRenderer.invoke("faceEmbedding", input);
+
 // - File selection

 // TODO: Deprecated - use dialogs on the renderer process itself
@ -322,6 +328,8 @@ contextBridge.exposeInMainWorld("electron", {
    // - ML
    clipImageEmbedding,
    clipTextEmbedding,
+    detectFaces,
+    faceEmbedding,

    // - File selection
    selectDirectory,
--- a/mobile/ios/Podfile.lock
+++ b/mobile/ios/Podfile.lock
@ -9,6 +9,8 @@ PODS:
  - connectivity_plus (0.0.1):
    - Flutter
    - ReachabilitySwift
+  - dart_ui_isolate (0.0.1):
+    - Flutter
  - device_info_plus (0.0.1):
    - Flutter
  - file_saver (0.0.1):
@ -18,7 +20,7 @@ PODS:
  - Firebase/Messaging (10.22.0):
    - Firebase/CoreOnly
    - FirebaseMessaging (~> 10.22.0)
-  - firebase_core (2.27.0):
+  - firebase_core (2.29.0):
    - Firebase/CoreOnly (= 10.22.0)
    - Flutter
  - firebase_messaging (14.7.19):
@ -62,8 +64,6 @@ PODS:
  - flutter_inappwebview/Core (0.0.1):
    - Flutter
    - OrderedSet (~> 5.0)
-  - flutter_isolate (0.0.1):
-    - Flutter
  - flutter_local_notifications (0.0.1):
    - Flutter
  - flutter_native_splash (0.0.1):
@ -235,6 +235,7 @@ DEPENDENCIES:
  - battery_info (from `.symlinks/plugins/battery_info/ios`)
  - bonsoir_darwin (from `.symlinks/plugins/bonsoir_darwin/darwin`)
  - connectivity_plus (from `.symlinks/plugins/connectivity_plus/ios`)
+  - dart_ui_isolate (from `.symlinks/plugins/dart_ui_isolate/ios`)
  - device_info_plus (from `.symlinks/plugins/device_info_plus/ios`)
  - file_saver (from `.symlinks/plugins/file_saver/ios`)
  - firebase_core (from `.symlinks/plugins/firebase_core/ios`)
@ -244,7 +245,6 @@ DEPENDENCIES:
  - flutter_email_sender (from `.symlinks/plugins/flutter_email_sender/ios`)
  - flutter_image_compress (from `.symlinks/plugins/flutter_image_compress/ios`)
  - flutter_inappwebview (from `.symlinks/plugins/flutter_inappwebview/ios`)
-  - flutter_isolate (from `.symlinks/plugins/flutter_isolate/ios`)
  - flutter_local_notifications (from `.symlinks/plugins/flutter_local_notifications/ios`)
  - flutter_native_splash (from `.symlinks/plugins/flutter_native_splash/ios`)
  - flutter_secure_storage (from `.symlinks/plugins/flutter_secure_storage/ios`)
@ -276,7 +276,6 @@ DEPENDENCIES:
  - shared_preferences_foundation (from `.symlinks/plugins/shared_preferences_foundation/darwin`)
  - sqflite (from `.symlinks/plugins/sqflite/darwin`)
  - sqlite3_flutter_libs (from `.symlinks/plugins/sqlite3_flutter_libs/ios`)
-  - tflite_flutter (from `.symlinks/plugins/tflite_flutter/ios`)
  - uni_links (from `.symlinks/plugins/uni_links/ios`)
  - url_launcher_ios (from `.symlinks/plugins/url_launcher_ios/ios`)
  - video_player_avfoundation (from `.symlinks/plugins/video_player_avfoundation/darwin`)
@ -306,8 +305,6 @@ SPEC REPOS:
    - Sentry
    - SentryPrivate
    - sqlite3
-    - TensorFlowLiteC
-    - TensorFlowLiteSwift
    - Toast

 EXTERNAL SOURCES:
@ -319,6 +316,8 @@ EXTERNAL SOURCES:
    :path: ".symlinks/plugins/bonsoir_darwin/darwin"
  connectivity_plus:
    :path: ".symlinks/plugins/connectivity_plus/ios"
+  dart_ui_isolate:
+    :path: ".symlinks/plugins/dart_ui_isolate/ios"
  device_info_plus:
    :path: ".symlinks/plugins/device_info_plus/ios"
  file_saver:
@ -337,8 +336,6 @@ EXTERNAL SOURCES:
    :path: ".symlinks/plugins/flutter_image_compress/ios"
  flutter_inappwebview:
    :path: ".symlinks/plugins/flutter_inappwebview/ios"
-  flutter_isolate:
-    :path: ".symlinks/plugins/flutter_isolate/ios"
  flutter_local_notifications:
    :path: ".symlinks/plugins/flutter_local_notifications/ios"
  flutter_native_splash:
@ -401,8 +398,6 @@ EXTERNAL SOURCES:
    :path: ".symlinks/plugins/sqflite/darwin"
  sqlite3_flutter_libs:
    :path: ".symlinks/plugins/sqlite3_flutter_libs/ios"
-  tflite_flutter:
-    :path: ".symlinks/plugins/tflite_flutter/ios"
  uni_links:
    :path: ".symlinks/plugins/uni_links/ios"
  url_launcher_ios:
@ -421,10 +416,11 @@ SPEC CHECKSUMS:
  battery_info: 09f5c9ee65394f2291c8c6227bedff345b8a730c
  bonsoir_darwin: 127bdc632fdc154ae2f277a4d5c86a6212bc75be
  connectivity_plus: 07c49e96d7fc92bc9920617b83238c4d178b446a
+  dart_ui_isolate: d5bcda83ca4b04f129d70eb90110b7a567aece14
  device_info_plus: c6fb39579d0f423935b0c9ce7ee2f44b71b9fce6
  file_saver: 503e386464dbe118f630e17b4c2e1190fa0cf808
  Firebase: 797fd7297b7e1be954432743a0b3f90038e45a71
-  firebase_core: 100945864b4aedce3cfef0c62ab864858bf013cf
+  firebase_core: aaadbddb3cb2ee3792b9804f9dbb63e5f6f7b55c
  firebase_messaging: e65050bf9b187511d80ea3a4de7cf5573d2c7543
  FirebaseCore: 0326ec9b05fbed8f8716cddbf0e36894a13837f7
  FirebaseCoreInternal: bcb5acffd4ea05e12a783ecf835f2210ce3dc6af
@ -435,8 +431,7 @@ SPEC CHECKSUMS:
  flutter_email_sender: 02d7443217d8c41483223627972bfdc09f74276b
  flutter_image_compress: 5a5e9aee05b6553048b8df1c3bc456d0afaac433
  flutter_inappwebview: 3d32228f1304635e7c028b0d4252937730bbc6cf
-  flutter_isolate: 0edf5081826d071adf21759d1eb10ff5c24503b5
-  flutter_local_notifications: 0c0b1ae97e741e1521e4c1629a459d04b9aec743
+  flutter_local_notifications: 4cde75091f6327eb8517fa068a0a5950212d2086
  flutter_native_splash: 52501b97d1c0a5f898d687f1646226c1f93c56ef
  flutter_secure_storage: 23fc622d89d073675f2eaa109381aefbcf5a49be
  flutter_sodium: c84426b4de738514b5b66cfdeb8a06634e72fe0b
@ -477,7 +472,7 @@ SPEC CHECKSUMS:
  Sentry: ebc12276bd17613a114ab359074096b6b3725203
  sentry_flutter: 88ebea3f595b0bc16acc5bedacafe6d60c12dcd5
  SentryPrivate: d651efb234cf385ec9a1cdd3eff94b5e78a0e0fe
-  share_plus: 056a1e8ac890df3e33cb503afffaf1e9b4fbae68
+  share_plus: 8875f4f2500512ea181eef553c3e27dba5135aad
  shared_preferences_foundation: b4c3b4cddf1c21f02770737f147a3f5da9d39695
  sqflite: 673a0e54cc04b7d6dba8d24fb8095b31c3a99eec
  sqlite3: 73b7fc691fdc43277614250e04d183740cb15078
@ -492,4 +487,4 @@ SPEC CHECKSUMS:

 PODFILE CHECKSUM: c1a8f198a245ed1f10e40b617efdb129b021b225

-COCOAPODS: 1.15.2
+COCOAPODS: 1.14.3
--- a/mobile/ios/Runner.xcodeproj/project.pbxproj
+++ b/mobile/ios/Runner.xcodeproj/project.pbxproj
@ -192,7 +192,7 @@
 			isa = PBXProject;
 			attributes = {
 				LastSwiftUpdateCheck = 1520;
-				LastUpgradeCheck = 1430;
+				LastUpgradeCheck = 1510;
 				ORGANIZATIONNAME = "The Chromium Authors";
 				TargetAttributes = {
 					97C146ED1CF9000F007C117D = {
@ -295,13 +295,13 @@
 				"${BUILT_PRODUCTS_DIR}/battery_info/battery_info.framework",
 				"${BUILT_PRODUCTS_DIR}/bonsoir_darwin/bonsoir_darwin.framework",
 				"${BUILT_PRODUCTS_DIR}/connectivity_plus/connectivity_plus.framework",
+				"${BUILT_PRODUCTS_DIR}/dart_ui_isolate/dart_ui_isolate.framework",
 				"${BUILT_PRODUCTS_DIR}/device_info_plus/device_info_plus.framework",
 				"${BUILT_PRODUCTS_DIR}/file_saver/file_saver.framework",
 				"${BUILT_PRODUCTS_DIR}/fk_user_agent/fk_user_agent.framework",
 				"${BUILT_PRODUCTS_DIR}/flutter_email_sender/flutter_email_sender.framework",
 				"${BUILT_PRODUCTS_DIR}/flutter_image_compress/flutter_image_compress.framework",
 				"${BUILT_PRODUCTS_DIR}/flutter_inappwebview/flutter_inappwebview.framework",
-				"${BUILT_PRODUCTS_DIR}/flutter_isolate/flutter_isolate.framework",
 				"${BUILT_PRODUCTS_DIR}/flutter_local_notifications/flutter_local_notifications.framework",
 				"${BUILT_PRODUCTS_DIR}/flutter_native_splash/flutter_native_splash.framework",
 				"${BUILT_PRODUCTS_DIR}/flutter_secure_storage/flutter_secure_storage.framework",
@ -380,13 +380,13 @@
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/battery_info.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/bonsoir_darwin.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/connectivity_plus.framework",
+				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/dart_ui_isolate.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/device_info_plus.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/file_saver.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/fk_user_agent.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/flutter_email_sender.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/flutter_image_compress.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/flutter_inappwebview.framework",
-				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/flutter_isolate.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/flutter_local_notifications.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/flutter_native_splash.framework",
 				"${TARGET_BUILD_DIR}/${FRAMEWORKS_FOLDER_PATH}/flutter_secure_storage.framework",
--- a/mobile/ios/Runner.xcodeproj/xcshareddata/xcschemes/Runner.xcscheme
+++ b/mobile/ios/Runner.xcodeproj/xcshareddata/xcschemes/Runner.xcscheme
@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <Scheme
-   LastUpgradeVersion = "1430"
+   LastUpgradeVersion = "1510"
   version = "1.3">
   <BuildAction
      parallelizeBuildables = "YES"
--- a/mobile/lib/services/machine_learning/face_ml/person/person_service.dart
+++ b/mobile/lib/services/machine_learning/face_ml/person/person_service.dart
@ -170,7 +170,13 @@ class PersonService {
        faceCount += cluster.faces.length;
        for (var faceId in cluster.faces) {
          if (faceIdToClusterID.containsKey(faceId)) {
-            throw Exception("Face $faceId is already assigned to a cluster");
+            final otherPersonID = clusterToPersonID[faceIdToClusterID[faceId]!];
+            if (otherPersonID != e.id) {
+              final otherPerson = await getPerson(otherPersonID!);
+              throw Exception(
+                "Face $faceId is already assigned to person $otherPersonID (${otherPerson!.data.name}) and person ${e.id} (${personData.name})",
+              );
+            }
          }
          faceIdToClusterID[faceId] = cluster.id;
        }
--- a/mobile/lib/services/machine_learning/semantic_search/embedding_store.dart
+++ b/mobile/lib/services/machine_learning/semantic_search/embedding_store.dart
@ -27,25 +27,33 @@ class EmbeddingStore {

  late SharedPreferences _preferences;

-  Completer<void>? _syncStatus;
+  Completer<bool>? _remoteSyncStatus;

  Future<void> init() async {
    _preferences = await SharedPreferences.getInstance();
  }

-  Future<void> pullEmbeddings(Model model) async {
-    if (_syncStatus != null) {
-      return _syncStatus!.future;
+  Future<bool> pullEmbeddings(Model model) async {
+    if (_remoteSyncStatus != null) {
+      return _remoteSyncStatus!.future;
    }
-    _syncStatus = Completer();
-    var remoteEmbeddings = await _getRemoteEmbeddings(model);
-    await _storeRemoteEmbeddings(remoteEmbeddings.embeddings);
-    while (remoteEmbeddings.hasMore) {
-      remoteEmbeddings = await _getRemoteEmbeddings(model);
+    _remoteSyncStatus = Completer();
+    try {
+      var remoteEmbeddings = await _getRemoteEmbeddings(model);
      await _storeRemoteEmbeddings(remoteEmbeddings.embeddings);
+      while (remoteEmbeddings.hasMore) {
+        remoteEmbeddings = await _getRemoteEmbeddings(model);
+        await _storeRemoteEmbeddings(remoteEmbeddings.embeddings);
+      }
+      _remoteSyncStatus!.complete(true);
+      _remoteSyncStatus = null;
+      return true;
+    } catch (e, s) {
+      _logger.severe("failed to fetch & store remote embeddings", e, s);
+      _remoteSyncStatus!.complete(false);
+      _remoteSyncStatus = null;
+      return false;
    }
-    _syncStatus!.complete();
-    _syncStatus = null;
  }

  Future<void> pushEmbeddings() async {
@ -132,7 +140,8 @@ class EmbeddingStore {
        remoteEmbeddings.add(embedding);
      }
    } catch (e, s) {
-      _logger.severe(e, s);
+      _logger.warning("Fetching embeddings failed", e, s);
+      rethrow;
    }

    _logger.info("${remoteEmbeddings.length} embeddings fetched");
--- a/mobile/lib/services/machine_learning/semantic_search/semantic_search_service.dart
+++ b/mobile/lib/services/machine_learning/semantic_search/semantic_search_service.dart
@ -49,9 +49,10 @@ class SemanticSearchService {
  bool _hasInitialized = false;
  bool _isComputingEmbeddings = false;
  bool _isSyncing = false;
-  Future<List<EnteFile>>? _ongoingRequest;
  List<Embedding> _cachedEmbeddings = <Embedding>[];
-  PendingQuery? _nextQuery;
+  Future<(String, List<EnteFile>)>? _searchScreenRequest;
+  String? _latestPendingQuery;
+
  Completer<void> _mlController = Completer<void>();

  get hasInitialized => _hasInitialized;
@ -125,37 +126,40 @@ class SemanticSearchService {
      return;
    }
    _isSyncing = true;
-    await EmbeddingStore.instance.pullEmbeddings(_currentModel);
-    await _backFill();
+    final fetchCompleted =
+        await EmbeddingStore.instance.pullEmbeddings(_currentModel);
+    if (fetchCompleted) {
+      await _backFill();
+    }
    _isSyncing = false;
  }

-  Future<List<EnteFile>> search(String query) async {
+  // searchScreenQuery should only be used for the user initiate query on the search screen.
+  // If there are multiple call tho this method, then for all the calls, the result will be the same as the last query.
+  Future<(String, List<EnteFile>)> searchScreenQuery(String query) async {
    if (!LocalSettings.instance.hasEnabledMagicSearch() ||
        !_frameworkInitialization.isCompleted) {
-      return [];
+      return (query, <EnteFile>[]);
    }
-    if (_ongoingRequest == null) {
-      _ongoingRequest = _getMatchingFiles(query).then((result) {
-        _ongoingRequest = null;
-        if (_nextQuery != null) {
-          final next = _nextQuery;
-          _nextQuery = null;
-          search(next!.query).then((nextResult) {
-            next.completer.complete(nextResult);
-          });
-        }
-
-        return result;
-      });
-      return _ongoingRequest!;
+    // If there's an ongoing request, just update the last query and return its future.
+    if (_searchScreenRequest != null) {
+      _latestPendingQuery = query;
+      return _searchScreenRequest!;
    } else {
-      // If there's an ongoing request, create or replace the nextCompleter.
-      _logger.info("Queuing query $query");
-      await _nextQuery?.completer.future
-          .timeout(const Duration(seconds: 0)); // Cancels the previous future.
-      _nextQuery = PendingQuery(query, Completer<List<EnteFile>>());
-      return _nextQuery!.completer.future;
+      // No ongoing request, start a new search.
+      _searchScreenRequest = _getMatchingFiles(query).then((result) {
+        // Search completed, reset the ongoing request.
+        _searchScreenRequest = null;
+        // If there was a new query during the last search, start a new search with the last query.
+        if (_latestPendingQuery != null) {
+          final String newQuery = _latestPendingQuery!;
+          _latestPendingQuery = null; // Reset last query.
+          // Recursively call search with the latest query.
+          return searchScreenQuery(newQuery);
+        }
+        return (query, result);
+      });
+      return _searchScreenRequest!;
    }
  }

@ -431,13 +435,6 @@ class QueryResult {
  QueryResult(this.id, this.score);
 }

-class PendingQuery {
-  final String query;
-  final Completer<List<EnteFile>> completer;
-
-  PendingQuery(this.query, this.completer);
-}
-
 class IndexStatus {
  final int indexedItems, pendingItems;

--- a/mobile/lib/services/notification_service.dart
+++ b/mobile/lib/services/notification_service.dart
@ -23,9 +23,8 @@ class NotificationService {
  Future<void> init(
    void Function(
      NotificationResponse notificationResponse,
-    )
-        onNotificationTapped,
-        SharedPreferences preferences,
+    ) onNotificationTapped,
+    SharedPreferences preferences,
  ) async {
    _preferences = preferences;
    const androidSettings = AndroidInitializationSettings('notification_icon');
@ -72,7 +71,7 @@ class NotificationService {
      result = await _notificationsPlugin
          .resolvePlatformSpecificImplementation<
              AndroidFlutterLocalNotificationsPlugin>()
-          ?.requestPermission();
+          ?.requestNotificationsPermission();
    }
    if (result != null) {
      await _preferences.setBool(keyGrantedNotificationPermission, result);
--- a/mobile/lib/services/search_service.dart
+++ b/mobile/lib/services/search_service.dart
@ -986,8 +986,16 @@ class SearchService {
    String query,
  ) async {
    final List<GenericSearchResult> searchResults = [];
-    final files = await SemanticSearchService.instance.search(query);
-    if (files.isNotEmpty) {
+    late List<EnteFile> files;
+    late String resultForQuery;
+    try {
+      (resultForQuery, files) =
+          await SemanticSearchService.instance.searchScreenQuery(query);
+    } catch (e, s) {
+      _logger.severe("Error occurred during magic search", e, s);
+      return searchResults;
+    }
+    if (files.isNotEmpty && resultForQuery == query) {
      searchResults.add(GenericSearchResult(ResultType.magic, query, files));
    }
    return searchResults;
--- a/mobile/pubspec.lock
+++ b/mobile/pubspec.lock
@ -13,10 +13,10 @@ packages:
    dependency: transitive
    description:
      name: _flutterfire_internals
-      sha256: "4eec93681221723a686ad580c2e7d960e1017cf1a4e0a263c2573c2c6b0bf5cd"
+      sha256: "0cb43f83f36ba8cb20502dee0c205e3f3aafb751732d724aeac3f2e044212cc2"
      url: "https://pub.dev"
    source: hosted
-    version: "1.3.25"
+    version: "1.3.29"
  adaptive_theme:
    dependency: "direct main"
    description:
@ -350,18 +350,18 @@ packages:
    dependency: transitive
    description:
      name: coverage
-      sha256: "595a29b55ce82d53398e1bcc2cba525d7bd7c59faeb2d2540e9d42c390cfeeeb"
+      sha256: "8acabb8306b57a409bf4c83522065672ee13179297a6bb0cb9ead73948df7c76"
      url: "https://pub.dev"
    source: hosted
-    version: "1.6.4"
+    version: "1.7.2"
  cross_file:
    dependency: "direct main"
    description:
      name: cross_file
-      sha256: fedaadfa3a6996f75211d835aaeb8fede285dae94262485698afd832371b9a5e
+      sha256: "55d7b444feb71301ef6b8838dbc1ae02e63dd48c8773f3810ff53bb1e2945b32"
      url: "https://pub.dev"
    source: hosted
-    version: "0.3.3+8"
+    version: "0.3.4+1"
  crypto:
    dependency: "direct main"
    description:
@ -534,10 +534,10 @@ packages:
    dependency: transitive
    description:
      name: extended_image_library
-      sha256: "9b55fc5ebc65fad984de66b8f177a1bef2a84d79203c9c213f75ff83c2c29edd"
+      sha256: c9caee8fe9b6547bd41c960c4f2d1ef8e34321804de6a1777f1d614a24247ad6
      url: "https://pub.dev"
    source: hosted
-    version: "4.0.1"
+    version: "4.0.4"
  fade_indexed_stack:
    dependency: "direct main"
    description:
@ -582,10 +582,10 @@ packages:
    dependency: transitive
    description:
      name: file
-      sha256: "1b92bec4fc2a72f59a8e15af5f52cd441e4a7860b49499d69dfa817af20e925d"
+      sha256: "5fc22d7c25582e38ad9a8515372cd9a93834027aacf1801cf01164dac0ffa08c"
      url: "https://pub.dev"
    source: hosted
-    version: "6.1.4"
+    version: "7.0.0"
  file_saver:
    dependency: "direct main"
    description:
@ -599,10 +599,10 @@ packages:
    dependency: "direct main"
    description:
      name: firebase_core
-      sha256: "53316975310c8af75a96e365f9fccb67d1c544ef0acdbf0d88bbe30eedd1c4f9"
+      sha256: a864d1b6afd25497a3b57b016886d1763df52baaa69758a46723164de8d187fe
      url: "https://pub.dev"
    source: hosted
-    version: "2.27.0"
+    version: "2.29.0"
  firebase_core_platform_interface:
    dependency: transitive
    description:
@ -615,10 +615,10 @@ packages:
    dependency: transitive
    description:
      name: firebase_core_web
-      sha256: c8e1d59385eee98de63c92f961d2a7062c5d9a65e7f45bdc7f1b0b205aab2492
+      sha256: c8b02226e548f35aace298e2bb2e6c24e34e8a203d614e742bb1146e5a4ad3c8
      url: "https://pub.dev"
    source: hosted
-    version: "2.11.5"
+    version: "2.15.0"
  firebase_messaging:
    dependency: "direct main"
    description:
@ -631,18 +631,18 @@ packages:
    dependency: transitive
    description:
      name: firebase_messaging_platform_interface
-      sha256: f7a9d74ff7fc588a924f6b2eaeaa148b0db521b13a9db55f6ad45864fa98c06e
+      sha256: "80b4ccf20066b0579ebc88d4678230a5f53ab282fe040e31671af745db1588f9"
      url: "https://pub.dev"
    source: hosted
-    version: "4.5.27"
+    version: "4.5.31"
  firebase_messaging_web:
    dependency: transitive
    description:
      name: firebase_messaging_web
-      sha256: fc21e771166860c55b103701c5ac7cdb2eec28897b97c42e6e5703cbedf9e02e
+      sha256: "9224aa4db1ce6f08d96a82978453d37e9980204a20e410a11d9b774b24c6841c"
      url: "https://pub.dev"
    source: hosted
-    version: "3.6.8"
+    version: "3.8.1"
  fixnum:
    dependency: transitive
    description:
@ -753,26 +753,26 @@ packages:
    dependency: "direct main"
    description:
      name: flutter_local_notifications
-      sha256: f222919a34545931e47b06000836b5101baeffb0e6eb5a4691d2d42851740dd9
+      sha256: f9a05409385b77b06c18f200a41c7c2711ebf7415669350bb0f8474c07bd40d1
      url: "https://pub.dev"
    source: hosted
-    version: "12.0.4"
+    version: "17.0.0"
  flutter_local_notifications_linux:
    dependency: transitive
    description:
      name: flutter_local_notifications_linux
-      sha256: "3c6d6db334f609a92be0c0915f40871ec56f5d2adf01e77ae364162c587c0ca8"
+      sha256: "33f741ef47b5f63cc7f78fe75eeeac7e19f171ff3c3df054d84c1e38bedb6a03"
      url: "https://pub.dev"
    source: hosted
-    version: "2.0.0"
+    version: "4.0.0+1"
  flutter_local_notifications_platform_interface:
    dependency: transitive
    description:
      name: flutter_local_notifications_platform_interface
-      sha256: "5ec1feac5f7f7d9266759488bc5f76416152baba9aa1b26fe572246caa00d1ab"
+      sha256: "7cf643d6d5022f3baed0be777b0662cce5919c0a7b86e700299f22dc4ae660ef"
      url: "https://pub.dev"
    source: hosted
-    version: "6.0.0"
+    version: "7.0.0+1"
  flutter_localizations:
    dependency: "direct main"
    description: flutter
@ -1013,10 +1013,10 @@ packages:
    dependency: "direct main"
    description:
      name: http
-      sha256: a2bbf9d017fcced29139daa8ed2bba4ece450ab222871df93ca9eec6f80c34ba
+      sha256: "761a297c042deedc1ffbb156d6e2af13886bb305c2a343a4d972504cd67dd938"
      url: "https://pub.dev"
    source: hosted
-    version: "1.2.0"
+    version: "1.2.1"
  http_client_helper:
    dependency: transitive
    description:
@ -1190,6 +1190,30 @@ packages:
      url: "https://pub.dev"
    source: hosted
    version: "0.9.1"
+  leak_tracker:
+    dependency: transitive
+    description:
+      name: leak_tracker
+      sha256: "78eb209deea09858f5269f5a5b02be4049535f568c07b275096836f01ea323fa"
+      url: "https://pub.dev"
+    source: hosted
+    version: "10.0.0"
+  leak_tracker_flutter_testing:
+    dependency: transitive
+    description:
+      name: leak_tracker_flutter_testing
+      sha256: b46c5e37c19120a8a01918cfaf293547f47269f7cb4b0058f21531c2465d6ef0
+      url: "https://pub.dev"
+    source: hosted
+    version: "2.0.1"
+  leak_tracker_testing:
+    dependency: transitive
+    description:
+      name: leak_tracker_testing
+      sha256: a597f72a664dbd293f3bfc51f9ba69816f84dcd403cdac7066cb3f6003f3ab47
+      url: "https://pub.dev"
+    source: hosted
+    version: "2.0.1"
  like_button:
    dependency: "direct main"
    description:
@ -1290,18 +1314,18 @@ packages:
    dependency: transitive
    description:
      name: matcher
-      sha256: "1803e76e6653768d64ed8ff2e1e67bea3ad4b923eb5c56a295c3e634bad5960e"
+      sha256: d2323aa2060500f906aa31a895b4030b6da3ebdcc5619d14ce1aada65cd161cb
      url: "https://pub.dev"
    source: hosted
-    version: "0.12.16"
+    version: "0.12.16+1"
  material_color_utilities:
    dependency: transitive
    description:
      name: material_color_utilities
-      sha256: "9528f2f296073ff54cb9fee677df673ace1218163c3bc7628093e7eed5203d41"
+      sha256: "0e0a020085b65b6083975e499759762399b4475f766c21668c4ecca34ea74e5a"
      url: "https://pub.dev"
    source: hosted
-    version: "0.5.0"
+    version: "0.8.0"
  media_extension:
    dependency: "direct main"
    description:
@ -1386,10 +1410,10 @@ packages:
    dependency: transitive
    description:
      name: meta
-      sha256: a6e590c838b18133bb482a2745ad77c5bb7715fb0451209e1a7567d416678b8e
+      sha256: d584fa6707a52763a52446f02cc621b077888fb63b93bbcb1143a7be5a0c0c04
      url: "https://pub.dev"
    source: hosted
-    version: "1.10.0"
+    version: "1.11.0"
  mgrs_dart:
    dependency: transitive
    description:
@ -1541,10 +1565,10 @@ packages:
    dependency: "direct main"
    description:
      name: path
-      sha256: "8829d8a55c13fc0e37127c29fedf290c102f4e40ae94ada574091fe0ff96c917"
+      sha256: "087ce49c3f0dc39180befefc60fdb4acd8f8620e5682fe2476afd0b3688bb4af"
      url: "https://pub.dev"
    source: hosted
-    version: "1.8.3"
+    version: "1.9.0"
  path_drawing:
    dependency: transitive
    description:
@ -1709,10 +1733,10 @@ packages:
    dependency: transitive
    description:
      name: platform
-      sha256: ae68c7bfcd7383af3629daafb32fb4e8681c7154428da4febcff06200585f102
+      sha256: "12220bb4b65720483f8fa9450b4332347737cf8213dd2840d8b2c823e47243ec"
      url: "https://pub.dev"
    source: hosted
-    version: "3.1.2"
+    version: "3.1.4"
  plugin_platform_interface:
    dependency: transitive
    description:
@ -1749,10 +1773,10 @@ packages:
    dependency: transitive
    description:
      name: process
-      sha256: "53fd8db9cec1d37b0574e12f07520d582019cb6c44abf5479a01505099a34a09"
+      sha256: "21e54fd2faf1b5bdd5102afd25012184a6793927648ea81eea80552ac9405b32"
      url: "https://pub.dev"
    source: hosted
-    version: "4.2.4"
+    version: "5.0.2"
  proj4dart:
    dependency: transitive
    description:
@ -1901,26 +1925,10 @@ packages:
    dependency: "direct main"
    description:
      name: share_plus
-      sha256: f582d5741930f3ad1bf0211d358eddc0508cc346e5b4b248bd1e569c995ebb7a
+      sha256: fb5319f3aab4c5dda5ebb92dca978179ba21f8c783ee4380910ef4c1c6824f51
      url: "https://pub.dev"
    source: hosted
-    version: "4.5.3"
-  share_plus_linux:
-    dependency: transitive
-    description:
-      name: share_plus_linux
-      sha256: dc32bf9f1151b9864bb86a997c61a487967a08f2e0b4feaa9a10538712224da4
-      url: "https://pub.dev"
-    source: hosted
-    version: "3.0.1"
-  share_plus_macos:
-    dependency: transitive
-    description:
-      name: share_plus_macos
-      sha256: "44daa946f2845045ecd7abb3569b61cd9a55ae9cc4cbec9895b2067b270697ae"
-      url: "https://pub.dev"
-    source: hosted
-    version: "3.0.1"
+    version: "8.0.3"
  share_plus_platform_interface:
    dependency: transitive
    description:
@ -1929,22 +1937,6 @@ packages:
      url: "https://pub.dev"
    source: hosted
    version: "3.4.0"
-  share_plus_web:
-    dependency: transitive
-    description:
-      name: share_plus_web
-      sha256: eaef05fa8548b372253e772837dd1fbe4ce3aca30ea330765c945d7d4f7c9935
-      url: "https://pub.dev"
-    source: hosted
-    version: "3.1.0"
-  share_plus_windows:
-    dependency: transitive
-    description:
-      name: share_plus_windows
-      sha256: "3a21515ae7d46988d42130cd53294849e280a5de6ace24bae6912a1bffd757d4"
-      url: "https://pub.dev"
-    source: hosted
-    version: "3.0.1"
  shared_preferences:
    dependency: "direct main"
    description:
@ -1989,10 +1981,10 @@ packages:
    dependency: transitive
    description:
      name: shared_preferences_web
-      sha256: "7b15ffb9387ea3e237bb7a66b8a23d2147663d391cafc5c8f37b2e7b4bde5d21"
+      sha256: "9aee1089b36bd2aafe06582b7d7817fd317ef05fc30e6ba14bff247d0933042a"
      url: "https://pub.dev"
    source: hosted
-    version: "2.2.2"
+    version: "2.3.0"
  shared_preferences_windows:
    dependency: transitive
    description:
@ -2402,10 +2394,10 @@ packages:
    dependency: transitive
    description:
      name: url_launcher_web
-      sha256: fff0932192afeedf63cdd50ecbb1bc825d31aed259f02bb8dba0f3b729a5e88b
+      sha256: "3692a459204a33e04bc94f5fb91158faf4f2c8903281ddd82915adecdb1a901d"
      url: "https://pub.dev"
    source: hosted
-    version: "2.2.3"
+    version: "2.3.0"
  url_launcher_windows:
    dependency: transitive
    description:
@ -2491,10 +2483,10 @@ packages:
    dependency: transitive
    description:
      name: vm_service
-      sha256: c538be99af830f478718b51630ec1b6bee5e74e52c8a802d328d9e71d35d2583
+      sha256: b3d56ff4341b8f182b96aceb2fa20e3dcb336b9f867bc0eafc0de10f1048e957
      url: "https://pub.dev"
    source: hosted
-    version: "11.10.0"
+    version: "13.0.0"
  volume_controller:
    dependency: transitive
    description:
@ -2539,10 +2531,10 @@ packages:
    dependency: transitive
    description:
      name: web
-      sha256: afe077240a270dcfd2aafe77602b4113645af95d0ad31128cc02bce5ac5d5152
+      sha256: "97da13628db363c635202ad97068d47c5b8aa555808e7a9411963c533b449b27"
      url: "https://pub.dev"
    source: hosted
-    version: "0.3.0"
+    version: "0.5.1"
  web_socket_channel:
    dependency: transitive
    description:
@ -2555,10 +2547,10 @@ packages:
    dependency: transitive
    description:
      name: webdriver
-      sha256: "3c923e918918feeb90c4c9fdf1fe39220fa4c0e8e2c0fffaded174498ef86c49"
+      sha256: "003d7da9519e1e5f329422b36c4dcdf18d7d2978d1ba099ea4e45ba490ed845e"
      url: "https://pub.dev"
    source: hosted
-    version: "3.0.2"
+    version: "3.0.3"
  webkit_inspection_protocol:
    dependency: transitive
    description:
@ -2611,10 +2603,10 @@ packages:
    dependency: transitive
    description:
      name: xdg_directories
-      sha256: bd512f03919aac5f1313eb8249f223bacf4927031bf60b02601f81f687689e86
+      sha256: faea9dee56b520b55a566385b84f2e8de55e7496104adada9962e0bd11bcff1d
      url: "https://pub.dev"
    source: hosted
-    version: "0.2.0+3"
+    version: "1.0.4"
  xml:
    dependency: transitive
    description:
@ -2648,5 +2640,5 @@ packages:
    source: hosted
    version: "3.1.2"
 sdks:
-  dart: ">=3.2.5 <4.0.0"
-  flutter: ">=3.16.6"
+  dart: ">=3.3.0 <4.0.0"
+  flutter: ">=3.19.0"
--- a/mobile/pubspec.yaml
+++ b/mobile/pubspec.yaml
@ -79,7 +79,7 @@ dependencies:
  flutter_image_compress: ^1.1.0
  flutter_inappwebview: ^5.8.0
  flutter_launcher_icons: ^0.13.1
-  flutter_local_notifications: ^12.0.4
+  flutter_local_notifications: ^17.0.0
  flutter_localizations:
    sdk: flutter
  flutter_map: ^5.0.0
@ -145,7 +145,7 @@ dependencies:
  scrollable_positioned_list: ^0.3.5
  sentry: ^7.9.0
  sentry_flutter: ^7.9.0
-  share_plus: ^4.0.10
+  share_plus: ^8.0.3
  shared_preferences: ^2.0.5
  simple_cluster: ^0.3.0
  sqflite: ^2.3.0
--- a/web/apps/photos/package.json
+++ b/web/apps/photos/package.json
@ -10,14 +10,7 @@
        "@ente/shared": "*",
        "@mui/x-date-pickers": "^5.0.0-alpha.6",
        "@stripe/stripe-js": "^1.13.2",
-        "@tensorflow-models/coco-ssd": "^2.2.2",
-        "@tensorflow/tfjs-backend-cpu": "^4.10.0",
-        "@tensorflow/tfjs-backend-webgl": "^4.9.0",
-        "@tensorflow/tfjs-converter": "^4.10.0",
-        "@tensorflow/tfjs-core": "^4.10.0",
-        "@tensorflow/tfjs-tflite": "0.0.1-alpha.7",
        "bip39": "^3.0.4",
-        "blazeface-back": "^0.0.9",
        "bs58": "^5.0.0",
        "chrono-node": "^2.2.6",
        "date-fns": "^2",
--- a/web/apps/photos/public/js/tesseract/tesseract-core.wasm.js
+++ b/web/apps/photos/public/js/tesseract/tesseract-core.wasm.js
--- a/web/apps/photos/public/js/tesseract/worker.min.js
+++ b/web/apps/photos/public/js/tesseract/worker.min.js
--- a/web/apps/photos/public/js/tfjs/tfjs-backend-wasm-simd.wasm
+++ b/web/apps/photos/public/js/tfjs/tfjs-backend-wasm-simd.wasm
--- a/web/apps/photos/public/js/tfjs/tfjs-backend-wasm-threaded-simd.wasm
+++ b/web/apps/photos/public/js/tfjs/tfjs-backend-wasm-threaded-simd.wasm
--- a/web/apps/photos/public/js/tfjs/tfjs-backend-wasm.wasm
+++ b/web/apps/photos/public/js/tfjs/tfjs-backend-wasm.wasm
--- a/web/apps/photos/public/js/tflite/tflite_web_api_cc.js
+++ b/web/apps/photos/public/js/tflite/tflite_web_api_cc.js
--- a/web/apps/photos/public/js/tflite/tflite_web_api_cc.wasm
+++ b/web/apps/photos/public/js/tflite/tflite_web_api_cc.wasm
--- a/web/apps/photos/public/js/tflite/tflite_web_api_cc_simd.js
+++ b/web/apps/photos/public/js/tflite/tflite_web_api_cc_simd.js
--- a/web/apps/photos/public/js/tflite/tflite_web_api_cc_simd.wasm
+++ b/web/apps/photos/public/js/tflite/tflite_web_api_cc_simd.wasm
--- a/web/apps/photos/public/js/tflite/tflite_web_api_cc_simd_threaded.js
+++ b/web/apps/photos/public/js/tflite/tflite_web_api_cc_simd_threaded.js
--- a/web/apps/photos/public/js/tflite/tflite_web_api_cc_simd_threaded.wasm
+++ b/web/apps/photos/public/js/tflite/tflite_web_api_cc_simd_threaded.wasm
--- a/web/apps/photos/public/js/tflite/tflite_web_api_cc_simd_threaded.worker.js
+++ b/web/apps/photos/public/js/tflite/tflite_web_api_cc_simd_threaded.worker.js
@ -1 +0,0 @@
-"use strict";var Module={};var initializedJS=false;function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};function moduleLoaded(){}self.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}tflite_web_api_ModuleFactory(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();if(!initializedJS){Module["___embind_register_native_and_builtin_types"]();initializedJS=true}try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["PThread"].threadExit(result)}}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};
--- a/web/apps/photos/public/js/tflite/tflite_web_api_cc_threaded.js
+++ b/web/apps/photos/public/js/tflite/tflite_web_api_cc_threaded.js
--- a/web/apps/photos/public/js/tflite/tflite_web_api_cc_threaded.wasm
+++ b/web/apps/photos/public/js/tflite/tflite_web_api_cc_threaded.wasm
--- a/web/apps/photos/public/js/tflite/tflite_web_api_cc_threaded.worker.js
+++ b/web/apps/photos/public/js/tflite/tflite_web_api_cc_threaded.worker.js
@ -1 +0,0 @@
-"use strict";var Module={};var initializedJS=false;function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};function moduleLoaded(){}self.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}tflite_web_api_ModuleFactory(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();if(!initializedJS){Module["___embind_register_native_and_builtin_types"]();initializedJS=true}try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["PThread"].threadExit(result)}}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};
--- a/web/apps/photos/public/models/blazeface/back/group1-shard1of1.bin
+++ b/web/apps/photos/public/models/blazeface/back/group1-shard1of1.bin
--- a/web/apps/photos/public/models/blazeface/back/model.json
+++ b/web/apps/photos/public/models/blazeface/back/model.json
--- a/web/apps/photos/public/models/imagescene/group1-shard1of7.bin
+++ b/web/apps/photos/public/models/imagescene/group1-shard1of7.bin
--- a/web/apps/photos/public/models/imagescene/group1-shard2of7.bin
+++ b/web/apps/photos/public/models/imagescene/group1-shard2of7.bin
--- a/web/apps/photos/public/models/imagescene/group1-shard3of7.bin
+++ b/web/apps/photos/public/models/imagescene/group1-shard3of7.bin
--- a/web/apps/photos/public/models/imagescene/group1-shard4of7.bin
+++ b/web/apps/photos/public/models/imagescene/group1-shard4of7.bin
--- a/web/apps/photos/public/models/imagescene/group1-shard5of7.bin
+++ b/web/apps/photos/public/models/imagescene/group1-shard5of7.bin
--- a/web/apps/photos/public/models/imagescene/group1-shard6of7.bin
+++ b/web/apps/photos/public/models/imagescene/group1-shard6of7.bin
--- a/web/apps/photos/public/models/imagescene/group1-shard7of7.bin
+++ b/web/apps/photos/public/models/imagescene/group1-shard7of7.bin
--- a/web/apps/photos/public/models/imagescene/model.json
+++ b/web/apps/photos/public/models/imagescene/model.json
--- a/web/apps/photos/public/models/imagescene/sceneMap.json
+++ b/web/apps/photos/public/models/imagescene/sceneMap.json
@ -1,32 +0,0 @@
-{
-    "0": "waterfall",
-    "1": "snow",
-    "2": "landscape",
-    "3": "underwater",
-    "4": "architecture",
-    "5": "sunset / sunrise",
-    "6": "blue sky",
-    "7": "cloudy sky",
-    "8": "greenery",
-    "9": "autumn leaves",
-    "10": "portrait",
-    "11": "flower",
-    "12": "night shot",
-    "13": "stage concert",
-    "14": "fireworks",
-    "15": "candle light",
-    "16": "neon lights",
-    "17": "indoor",
-    "18": "backlight",
-    "19": "text documents",
-    "20": "qr images",
-    "21": "group portrait",
-    "22": "computer screens",
-    "23": "kids",
-    "24": "dog",
-    "25": "cat",
-    "26": "macro",
-    "27": "food",
-    "28": "beach",
-    "29": "mountain"
-}
--- a/web/apps/photos/public/models/mobilefacenet/mobilefacenet.tflite
+++ b/web/apps/photos/public/models/mobilefacenet/mobilefacenet.tflite
--- a/web/apps/photos/public/models/ssdmobilenet/group1-shard1of7
+++ b/web/apps/photos/public/models/ssdmobilenet/group1-shard1of7
--- a/web/apps/photos/public/models/ssdmobilenet/group1-shard2of7
+++ b/web/apps/photos/public/models/ssdmobilenet/group1-shard2of7
--- a/web/apps/photos/public/models/ssdmobilenet/group1-shard3of7
+++ b/web/apps/photos/public/models/ssdmobilenet/group1-shard3of7
--- a/web/apps/photos/public/models/ssdmobilenet/group1-shard4of7
+++ b/web/apps/photos/public/models/ssdmobilenet/group1-shard4of7
--- a/web/apps/photos/public/models/ssdmobilenet/group1-shard5of7
+++ b/web/apps/photos/public/models/ssdmobilenet/group1-shard5of7
--- a/web/apps/photos/public/models/ssdmobilenet/group1-shard6of7
+++ b/web/apps/photos/public/models/ssdmobilenet/group1-shard6of7
--- a/web/apps/photos/public/models/ssdmobilenet/group1-shard7of7
+++ b/web/apps/photos/public/models/ssdmobilenet/group1-shard7of7
--- a/web/apps/photos/public/models/ssdmobilenet/model.json
+++ b/web/apps/photos/public/models/ssdmobilenet/model.json
--- a/web/apps/photos/public/models/ssdmobilenet/weights_manifest.json
+++ b/web/apps/photos/public/models/ssdmobilenet/weights_manifest.json
--- a/web/apps/photos/src/components/MachineLearning/ObjectList.tsx
+++ b/web/apps/photos/src/components/MachineLearning/ObjectList.tsx
@ -1,51 +0,0 @@
-import Box from "@mui/material/Box";
-import { Chip } from "components/Chip";
-import { Legend } from "components/PhotoViewer/styledComponents/Legend";
-import { t } from "i18next";
-import { useEffect, useState } from "react";
-import { EnteFile } from "types/file";
-import mlIDbStorage from "utils/storage/mlIDbStorage";
-
-export function ObjectLabelList(props: {
-    file: EnteFile;
-    updateMLDataIndex: number;
-}) {
-    const [objects, setObjects] = useState<Array<string>>([]);
-    useEffect(() => {
-        let didCancel = false;
-        const main = async () => {
-            const objects = await mlIDbStorage.getAllObjectsMap();
-            const uniqueObjectNames = [
-                ...new Set(
-                    (objects.get(props.file.id) ?? []).map(
-                        (object) => object.detection.class,
-                    ),
-                ),
-            ];
-            !didCancel && setObjects(uniqueObjectNames);
-        };
-        main();
-        return () => {
-            didCancel = true;
-        };
-    }, [props.file, props.updateMLDataIndex]);
-
-    if (objects.length === 0) return <></>;
-
-    return (
-        <div>
-            <Legend sx={{ pb: 1, display: "block" }}>{t("OBJECTS")}</Legend>
-            <Box
-                display={"flex"}
-                gap={1}
-                flexWrap="wrap"
-                justifyContent={"flex-start"}
-                alignItems={"flex-start"}
-            >
-                {objects.map((object) => (
-                    <Chip key={object}>{object}</Chip>
-                ))}
-            </Box>
-        </div>
-    );
-}
--- a/web/apps/photos/src/components/MachineLearning/TFJSImage.tsx
+++ b/web/apps/photos/src/components/MachineLearning/TFJSImage.tsx
@ -1,39 +0,0 @@
-import * as tf from "@tensorflow/tfjs-core";
-import { useEffect, useRef } from "react";
-import { FaceImage } from "types/machineLearning";
-
-interface FaceImageProps {
-    faceImage: FaceImage;
-    width?: number;
-    height?: number;
-}
-
-export default function TFJSImage(props: FaceImageProps) {
-    const canvasRef = useRef(null);
-
-    useEffect(() => {
-        if (!props || !props.faceImage) {
-            return;
-        }
-        const canvas = canvasRef.current;
-        const faceTensor = tf.tensor3d(props.faceImage);
-        const resized =
-            props.width && props.height
-                ? tf.image.resizeBilinear(faceTensor, [
-                      props.width,
-                      props.height,
-                  ])
-                : faceTensor;
-        const normFaceImage = tf.div(tf.add(resized, 1.0), 2);
-        tf.browser.toPixels(normFaceImage as tf.Tensor3D, canvas);
-    }, [props]);
-
-    return (
-        <canvas
-            ref={canvasRef}
-            width={112}
-            height={112}
-            style={{ display: "inline" }}
-        />
-    );
-}
--- a/web/apps/photos/src/components/PhotoViewer/FileInfo/index.tsx
+++ b/web/apps/photos/src/components/PhotoViewer/FileInfo/index.tsx
@ -10,7 +10,6 @@ import TextSnippetOutlined from "@mui/icons-material/TextSnippetOutlined";
 import { Box, DialogProps, Link, Stack, styled } from "@mui/material";
 import { Chip } from "components/Chip";
 import { EnteDrawer } from "components/EnteDrawer";
-import { ObjectLabelList } from "components/MachineLearning/ObjectList";
 import {
    PhotoPeopleList,
    UnidentifiedFaces,
@ -344,10 +343,6 @@ export function FileInfo({
                            file={file}
                            updateMLDataIndex={updateMLDataIndex}
                        />
-                        <ObjectLabelList
-                            file={file}
-                            updateMLDataIndex={updateMLDataIndex}
-                        />
                    </>
                )}
            </Stack>
--- a/web/apps/photos/src/components/Search/SearchBar/searchInput/index.tsx
+++ b/web/apps/photos/src/components/Search/SearchBar/searchInput/index.tsx
@ -17,7 +17,7 @@ import {
 import { Collection } from "types/collection";
 import { LocationTagData } from "types/entity";
 import { EnteFile } from "types/file";
-import { Person, Thing, WordGroup } from "types/machineLearning";
+import { Person } from "types/machineLearning";
 import {
    ClipSearchScores,
    DateValue,
@ -146,12 +146,6 @@ export default function SearchInput(props: Iprops) {
            case SuggestionType.PERSON:
                search = { person: selectedOption.value as Person };
                break;
-            case SuggestionType.THING:
-                search = { thing: selectedOption.value as Thing };
-                break;
-            case SuggestionType.TEXT:
-                search = { text: selectedOption.value as WordGroup };
-                break;
            case SuggestionType.FILE_TYPE:
                search = { fileType: selectedOption.value as FILE_TYPE };
                break;
--- a/web/apps/photos/src/constants/mlConfig.ts
+++ b/web/apps/photos/src/constants/mlConfig.ts
@ -46,22 +46,6 @@ export const DEFAULT_ML_SYNC_CONFIG: MLSyncConfig = {
        // maxDistanceInsideCluster: 0.4,
        generateDebugInfo: true,
    },
-    objectDetection: {
-        method: "SSDMobileNetV2",
-        maxNumBoxes: 20,
-        minScore: 0.2,
-    },
-    sceneDetection: {
-        method: "ImageScene",
-        minScore: 0.1,
-    },
-    // tsne: {
-    //     samples: 200,
-    //     dim: 2,
-    //     perplexity: 10.0,
-    //     learningRate: 10.0,
-    //     metric: 'euclidean',
-    // },
    mlVersion: 3,
 };

@ -69,29 +53,4 @@ export const DEFAULT_ML_SEARCH_CONFIG: MLSearchConfig = {
    enabled: false,
 };

-export const ML_SYNC_DOWNLOAD_TIMEOUT_MS = 300000;
-
-export const MAX_FACE_DISTANCE_PERCENT = Math.sqrt(2) / 100;
-
 export const MAX_ML_SYNC_ERROR_COUNT = 1;
-
-export const TEXT_DETECTION_TIMEOUT_MS = [10000, 30000, 60000, 120000, 240000];
-
-export const BLAZEFACE_MAX_FACES = 50;
-export const BLAZEFACE_INPUT_SIZE = 256;
-export const BLAZEFACE_IOU_THRESHOLD = 0.3;
-export const BLAZEFACE_SCORE_THRESHOLD = 0.75;
-export const BLAZEFACE_PASS1_SCORE_THRESHOLD = 0.4;
-export const BLAZEFACE_FACE_SIZE = 112;
-export const MOBILEFACENET_FACE_SIZE = 112;
-export const MOBILEFACENET_EMBEDDING_SIZE = 192;
-
-// scene detection model takes fixed-shaped (224x224) inputs
-// https://tfhub.dev/sayannath/lite-model/image-scene/1
-export const SCENE_DETECTION_IMAGE_SIZE = 224;
-
-// SSD with Mobilenet v2 initialized from Imagenet classification checkpoint. Trained on COCO 2017 dataset (images scaled to 320x320 resolution).
-// https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2
-export const OBJECT_DETECTION_IMAGE_SIZE = 320;
-
-export const BATCHES_BEFORE_SYNCING_INDEX = 5;
--- a/web/apps/photos/src/services/embeddingService.ts
+++ b/web/apps/photos/src/services/embeddingService.ts
@ -1,4 +1,6 @@
+import { inWorker } from "@/next/env";
 import log from "@/next/log";
+import { workerBridge } from "@/next/worker/worker-bridge";
 import ComlinkCryptoWorker from "@ente/shared/crypto";
 import { CustomError } from "@ente/shared/error";
 import HTTPService from "@ente/shared/network/HTTPService";
@ -262,7 +264,9 @@ export const putEmbedding = async (
    putEmbeddingReq: PutEmbeddingRequest,
 ): Promise<EncryptedEmbedding> => {
    try {
-        const token = getToken();
+        const token = inWorker()
+            ? await workerBridge.getAuthToken()
+            : getToken();
        if (!token) {
            log.info("putEmbedding failed: token not found");
            throw Error(CustomError.TOKEN_MISSING);
--- a/web/apps/photos/src/services/machineLearning/blazeFaceDetectionService.ts
+++ b/web/apps/photos/src/services/machineLearning/blazeFaceDetectionService.ts
@ -1,257 +0,0 @@
-import log from "@/next/log";
-import { GraphModel } from "@tensorflow/tfjs-converter";
-import * as tf from "@tensorflow/tfjs-core";
-import {
-    load as blazeFaceLoad,
-    BlazeFaceModel,
-    NormalizedFace,
-} from "blazeface-back";
-import {
-    BLAZEFACE_FACE_SIZE,
-    BLAZEFACE_INPUT_SIZE,
-    BLAZEFACE_IOU_THRESHOLD,
-    BLAZEFACE_MAX_FACES,
-    BLAZEFACE_PASS1_SCORE_THRESHOLD,
-    BLAZEFACE_SCORE_THRESHOLD,
-    MAX_FACE_DISTANCE_PERCENT,
-} from "constants/mlConfig";
-import {
-    FaceDetection,
-    FaceDetectionMethod,
-    FaceDetectionService,
-    Versioned,
-} from "types/machineLearning";
-import { addPadding, crop, resizeToSquare } from "utils/image";
-import { enlargeBox, newBox, normFaceBox } from "utils/machineLearning";
-import {
-    getNearestDetection,
-    removeDuplicateDetections,
-    transformPaddedToImage,
-} from "utils/machineLearning/faceDetection";
-import {
-    computeTransformToBox,
-    transformBox,
-    transformPoints,
-} from "utils/machineLearning/transform";
-import { Box, Point } from "../../../thirdparty/face-api/classes";
-
-class BlazeFaceDetectionService implements FaceDetectionService {
-    private blazeFaceModel: Promise<BlazeFaceModel>;
-    private blazeFaceBackModel: GraphModel;
-    public method: Versioned<FaceDetectionMethod>;
-
-    private desiredLeftEye = [0.36, 0.45];
-    private desiredFaceSize;
-
-    public constructor(desiredFaceSize: number = BLAZEFACE_FACE_SIZE) {
-        this.method = {
-            value: "BlazeFace",
-            version: 1,
-        };
-        this.desiredFaceSize = desiredFaceSize;
-    }
-
-    public getRelativeDetection(): FaceDetection {
-        // TODO(MR): onnx-yolo
-        throw new Error();
-    }
-
-    private async init() {
-        this.blazeFaceModel = blazeFaceLoad({
-            maxFaces: BLAZEFACE_MAX_FACES,
-            scoreThreshold: BLAZEFACE_PASS1_SCORE_THRESHOLD,
-            iouThreshold: BLAZEFACE_IOU_THRESHOLD,
-            modelUrl: "/models/blazeface/back/model.json",
-            inputHeight: BLAZEFACE_INPUT_SIZE,
-            inputWidth: BLAZEFACE_INPUT_SIZE,
-        });
-        log.info(
-            "loaded blazeFaceModel: ",
-            // await this.blazeFaceModel,
-            // eslint-disable-next-line @typescript-eslint/await-thenable
-            await tf.getBackend(),
-        );
-    }
-
-    private getDlibAlignedFace(normFace: NormalizedFace): Box {
-        const relX = 0.5;
-        const relY = 0.43;
-        const relScale = 0.45;
-
-        const leftEyeCenter = normFace.landmarks[0];
-        const rightEyeCenter = normFace.landmarks[1];
-        const mountCenter = normFace.landmarks[3];
-
-        const distToMouth = (pt) => {
-            const dy = mountCenter[1] - pt[1];
-            const dx = mountCenter[0] - pt[0];
-            return Math.sqrt(dx * dx + dy * dy);
-        };
-        const eyeToMouthDist =
-            (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
-
-        const size = Math.floor(eyeToMouthDist / relScale);
-
-        const center = [
-            (leftEyeCenter[0] + rightEyeCenter[0] + mountCenter[0]) / 3,
-            (leftEyeCenter[1] + rightEyeCenter[1] + mountCenter[1]) / 3,
-        ];
-
-        const left = center[0] - relX * size;
-        const top = center[1] - relY * size;
-        const right = center[0] + relX * size;
-        const bottom = center[1] + relY * size;
-
-        return new Box({
-            left: left,
-            top: top,
-            right: right,
-            bottom: bottom,
-        });
-    }
-
-    private getAlignedFace(normFace: NormalizedFace): Box {
-        const leftEye = normFace.landmarks[0];
-        const rightEye = normFace.landmarks[1];
-        // const noseTip = normFace.landmarks[2];
-
-        const dy = rightEye[1] - leftEye[1];
-        const dx = rightEye[0] - leftEye[0];
-
-        const desiredRightEyeX = 1.0 - this.desiredLeftEye[0];
-
-        // const eyesCenterX = (leftEye[0] + rightEye[0]) / 2;
-        // const yaw = Math.abs(noseTip[0] - eyesCenterX)
-        const dist = Math.sqrt(dx * dx + dy * dy);
-        let desiredDist = desiredRightEyeX - this.desiredLeftEye[0];
-        desiredDist *= this.desiredFaceSize;
-        const scale = desiredDist / dist;
-        // log.info("scale: ", scale);
-
-        const eyesCenter = [];
-        eyesCenter[0] = Math.floor((leftEye[0] + rightEye[0]) / 2);
-        eyesCenter[1] = Math.floor((leftEye[1] + rightEye[1]) / 2);
-        // log.info("eyesCenter: ", eyesCenter);
-
-        const faceWidth = this.desiredFaceSize / scale;
-        const faceHeight = this.desiredFaceSize / scale;
-        // log.info("faceWidth: ", faceWidth, "faceHeight: ", faceHeight)
-
-        const tx = eyesCenter[0] - faceWidth * 0.5;
-        const ty = eyesCenter[1] - faceHeight * this.desiredLeftEye[1];
-        // log.info("tx: ", tx, "ty: ", ty);
-
-        return new Box({
-            left: tx,
-            top: ty,
-            right: tx + faceWidth,
-            bottom: ty + faceHeight,
-        });
-    }
-
-    public async detectFacesUsingModel(image: tf.Tensor3D) {
-        const resizedImage = tf.image.resizeBilinear(image, [256, 256]);
-        const reshapedImage = tf.reshape(resizedImage, [
-            1,
-            resizedImage.shape[0],
-            resizedImage.shape[1],
-            3,
-        ]);
-        const normalizedImage = tf.sub(tf.div(reshapedImage, 127.5), 1.0);
-        // eslint-disable-next-line @typescript-eslint/await-thenable
-        const results = await this.blazeFaceBackModel.predict(normalizedImage);
-        // log.info('onFacesDetected: ', results);
-        return results;
-    }
-
-    private async getBlazefaceModel() {
-        if (!this.blazeFaceModel) {
-            await this.init();
-        }
-
-        return this.blazeFaceModel;
-    }
-
-    private async estimateFaces(
-        imageBitmap: ImageBitmap,
-    ): Promise<Array<FaceDetection>> {
-        const resized = resizeToSquare(imageBitmap, BLAZEFACE_INPUT_SIZE);
-        const tfImage = tf.browser.fromPixels(resized.image);
-        const blazeFaceModel = await this.getBlazefaceModel();
-        // TODO: check if this works concurrently, else use serialqueue
-        const faces = await blazeFaceModel.estimateFaces(tfImage);
-        tf.dispose(tfImage);
-
-        const inBox = newBox(0, 0, resized.width, resized.height);
-        const toBox = newBox(0, 0, imageBitmap.width, imageBitmap.height);
-        const transform = computeTransformToBox(inBox, toBox);
-        // log.info("1st pass: ", { transform });
-
-        const faceDetections: Array<FaceDetection> = faces?.map((f) => {
-            const box = transformBox(normFaceBox(f), transform);
-            const normLandmarks = (f.landmarks as number[][])?.map(
-                (l) => new Point(l[0], l[1]),
-            );
-            const landmarks = transformPoints(normLandmarks, transform);
-            return {
-                box,
-                landmarks,
-                probability: f.probability as number,
-                // detectionMethod: this.method,
-            } as FaceDetection;
-        });
-
-        return faceDetections;
-    }
-
-    public async detectFaces(
-        imageBitmap: ImageBitmap,
-    ): Promise<Array<FaceDetection>> {
-        const maxFaceDistance = imageBitmap.width * MAX_FACE_DISTANCE_PERCENT;
-        const pass1Detections = await this.estimateFaces(imageBitmap);
-
-        // run 2nd pass for accuracy
-        const detections: Array<FaceDetection> = [];
-        for (const pass1Detection of pass1Detections) {
-            const imageBox = enlargeBox(pass1Detection.box, 2);
-            const faceImage = crop(
-                imageBitmap,
-                imageBox,
-                BLAZEFACE_INPUT_SIZE / 2,
-            );
-            const paddedImage = addPadding(faceImage, 0.5);
-            const paddedBox = enlargeBox(imageBox, 2);
-            const pass2Detections = await this.estimateFaces(paddedImage);
-
-            pass2Detections?.forEach((d) =>
-                transformPaddedToImage(d, faceImage, imageBox, paddedBox),
-            );
-            let selected = pass2Detections?.[0];
-            if (pass2Detections?.length > 1) {
-                // log.info('2nd pass >1 face', pass2Detections.length);
-                selected = getNearestDetection(
-                    pass1Detection,
-                    pass2Detections,
-                    // maxFaceDistance
-                );
-            }
-
-            // we might miss 1st pass face actually having score within threshold
-            // it is ok as results will be consistent with 2nd pass only detections
-            if (selected && selected.probability >= BLAZEFACE_SCORE_THRESHOLD) {
-                // log.info("pass2: ", { imageBox, paddedBox, transform, selected });
-                detections.push(selected);
-            }
-        }
-
-        return removeDuplicateDetections(detections, maxFaceDistance);
-    }
-
-    public async dispose() {
-        const blazeFaceModel = await this.getBlazefaceModel();
-        blazeFaceModel?.dispose();
-        this.blazeFaceModel = undefined;
-    }
-}
-
-export default new BlazeFaceDetectionService();
--- a/web/apps/photos/src/services/machineLearning/faceService.ts
+++ b/web/apps/photos/src/services/machineLearning/faceService.ts
@ -55,7 +55,7 @@ class FaceService {
            await syncContext.faceDetectionService.detectFaces(imageBitmap);
        console.timeEnd(timerId);
        console.log("faceDetections: ", faceDetections?.length);
-        // log.info('3 TF Memory stats: ',JSON.stringify(tf.memory()));
+
        // TODO: reenable faces filtering based on width
        const detectedFaces = faceDetections?.map((detection) => {
            return {
@ -150,7 +150,7 @@ class FaceService {

        imageBitmap.close();
        log.info("[MLService] alignedFaces: ", newMlFile.faces?.length);
-        // log.info('4 TF Memory stats: ',JSON.stringify(tf.memory()));
+
        return faceImages;
    }

@ -187,7 +187,6 @@ class FaceService {
        newMlFile.faces.forEach((f, i) => (f.embedding = embeddings[i]));

        log.info("[MLService] facesWithEmbeddings: ", newMlFile.faces.length);
-        // log.info('5 TF Memory stats: ',JSON.stringify(tf.memory()));
    }

    async syncFileFaceMakeRelativeDetections(
@ -226,11 +225,21 @@ class FaceService {
            face.detection,
            syncContext.config.faceCrop,
        );
-        face.crop = await storeFaceCrop(
-            face.id,
-            faceCrop,
-            syncContext.config.faceCrop.blobOptions,
-        );
+        try {
+            face.crop = await storeFaceCrop(
+                face.id,
+                faceCrop,
+                syncContext.config.faceCrop.blobOptions,
+            );
+        } catch (e) {
+            // TODO(MR): Temporarily ignoring errors about failing cache puts
+            // when using a custom scheme in Electron. Needs an alternative
+            // approach, perhaps OPFS.
+            console.error(
+                "Ignoring error when caching face crop, the face crop will not be available",
+                e,
+            );
+        }
        const blob = await imageBitmapToBlob(faceCrop.image);
        faceCrop.image.close();
        return blob;
--- a/web/apps/photos/src/services/machineLearning/imageSceneService.ts
+++ b/web/apps/photos/src/services/machineLearning/imageSceneService.ts
@ -1,108 +0,0 @@
-import log from "@/next/log";
-import * as tfjsConverter from "@tensorflow/tfjs-converter";
-import * as tf from "@tensorflow/tfjs-core";
-import { SCENE_DETECTION_IMAGE_SIZE } from "constants/mlConfig";
-import {
-    ObjectDetection,
-    SceneDetectionMethod,
-    SceneDetectionService,
-    Versioned,
-} from "types/machineLearning";
-import { resizeToSquare } from "utils/image";
-
-class ImageScene implements SceneDetectionService {
-    method: Versioned<SceneDetectionMethod>;
-    private model: tfjsConverter.GraphModel;
-    private sceneMap: { [key: string]: string };
-    private ready: Promise<void>;
-    private workerID: number;
-
-    public constructor() {
-        this.method = {
-            value: "ImageScene",
-            version: 1,
-        };
-        this.workerID = Math.round(Math.random() * 1000);
-    }
-
-    private async init() {
-        log.info(`[${this.workerID}]`, "ImageScene init called");
-        if (this.model) {
-            return;
-        }
-
-        this.sceneMap = await (
-            await fetch("/models/imagescene/sceneMap.json")
-        ).json();
-
-        this.model = await tfjsConverter.loadGraphModel(
-            "/models/imagescene/model.json",
-        );
-        log.info(
-            `[${this.workerID}]`,
-            "loaded ImageScene model",
-            tf.getBackend(),
-        );
-
-        tf.tidy(() => {
-            const zeroTensor = tf.zeros([1, 224, 224, 3]);
-            // warmup the model
-            this.model.predict(zeroTensor) as tf.Tensor;
-        });
-    }
-
-    private async getImageSceneModel() {
-        log.info(`[${this.workerID}]`, "ImageScene getImageSceneModel called");
-        if (!this.ready) {
-            this.ready = this.init();
-        }
-        await this.ready;
-        return this.model;
-    }
-
-    async detectScenes(image: ImageBitmap, minScore: number) {
-        const resized = resizeToSquare(image, SCENE_DETECTION_IMAGE_SIZE);
-
-        const model = await this.getImageSceneModel();
-
-        const output = tf.tidy(() => {
-            const tfImage = tf.browser.fromPixels(resized.image);
-            const input = tf.expandDims(tf.cast(tfImage, "float32"));
-            const output = model.predict(input) as tf.Tensor;
-            return output;
-        });
-
-        const data = (await output.data()) as Float32Array;
-        output.dispose();
-
-        const scenes = this.parseSceneDetectionResult(
-            data,
-            minScore,
-            image.width,
-            image.height,
-        );
-
-        return scenes;
-    }
-
-    private parseSceneDetectionResult(
-        outputData: Float32Array,
-        minScore: number,
-        width: number,
-        height: number,
-    ): ObjectDetection[] {
-        const scenes = [];
-        for (let i = 0; i < outputData.length; i++) {
-            if (outputData[i] >= minScore) {
-                scenes.push({
-                    class: this.sceneMap[i.toString()],
-                    score: outputData[i],
-                    bbox: [0, 0, width, height],
-                });
-            }
-        }
-        return scenes;
-    }
-}
-
-export default new ImageScene();
--- a/web/apps/photos/src/services/machineLearning/laplacianBlurDetectionService.ts
+++ b/web/apps/photos/src/services/machineLearning/laplacianBlurDetectionService.ts
@ -1,10 +1,10 @@
-import { MOBILEFACENET_FACE_SIZE } from "constants/mlConfig";
 import {
    BlurDetectionMethod,
    BlurDetectionService,
    Versioned,
 } from "types/machineLearning";
 import { createGrayscaleIntMatrixFromNormalized2List } from "utils/image";
+import { mobileFaceNetFaceSize } from "./mobileFaceNetEmbeddingService";

 class LaplacianBlurDetectionService implements BlurDetectionService {
    public method: Versioned<BlurDetectionMethod>;
@ -19,7 +19,7 @@ class LaplacianBlurDetectionService implements BlurDetectionService {
    public detectBlur(alignedFaces: Float32Array): number[] {
        const numFaces = Math.round(
            alignedFaces.length /
-                (MOBILEFACENET_FACE_SIZE * MOBILEFACENET_FACE_SIZE * 3),
+                (mobileFaceNetFaceSize * mobileFaceNetFaceSize * 3),
        );
        const blurValues: number[] = [];
        for (let i = 0; i < numFaces; i++) {
--- a/web/apps/photos/src/services/machineLearning/machineLearningFactory.ts
+++ b/web/apps/photos/src/services/machineLearning/machineLearningFactory.ts
@ -22,20 +22,14 @@ import {
    MLLibraryData,
    MLSyncConfig,
    MLSyncContext,
-    ObjectDetectionMethod,
-    ObjectDetectionService,
-    SceneDetectionMethod,
-    SceneDetectionService,
 } from "types/machineLearning";
 import { logQueueStats } from "utils/machineLearning";
 import arcfaceAlignmentService from "./arcfaceAlignmentService";
 import arcfaceCropService from "./arcfaceCropService";
 import dbscanClusteringService from "./dbscanClusteringService";
 import hdbscanClusteringService from "./hdbscanClusteringService";
-import imageSceneService from "./imageSceneService";
 import laplacianBlurDetectionService from "./laplacianBlurDetectionService";
 import mobileFaceNetEmbeddingService from "./mobileFaceNetEmbeddingService";
-import ssdMobileNetV2Service from "./ssdMobileNetV2Service";
 import yoloFaceDetectionService from "./yoloFaceDetectionService";

 export class MLFactory {
@ -49,26 +43,6 @@ export class MLFactory {
        throw Error("Unknon face detection method: " + method);
    }

-    public static getObjectDetectionService(
-        method: ObjectDetectionMethod,
-    ): ObjectDetectionService {
-        if (method === "SSDMobileNetV2") {
-            return ssdMobileNetV2Service;
-        }
-
-        throw Error("Unknown object detection method: " + method);
-    }
-
-    public static getSceneDetectionService(
-        method: SceneDetectionMethod,
-    ): SceneDetectionService {
-        if (method === "ImageScene") {
-            return imageSceneService;
-        }
-
-        throw Error("Unknown scene detection method: " + method);
-    }
-
    public static getFaceCropService(method: FaceCropMethod) {
        if (method === "ArcFace") {
            return arcfaceCropService;
@ -147,15 +121,12 @@ export class LocalMLSyncContext implements MLSyncContext {
    public blurDetectionService: BlurDetectionService;
    public faceEmbeddingService: FaceEmbeddingService;
    public faceClusteringService: ClusteringService;
-    public objectDetectionService: ObjectDetectionService;
-    public sceneDetectionService: SceneDetectionService;

    public localFilesMap: Map<number, EnteFile>;
    public outOfSyncFiles: EnteFile[];
    public nSyncedFiles: number;
    public nSyncedFaces: number;
    public allSyncedFacesMap?: Map<number, Array<Face>>;
-    public tsne?: any;

    public error?: Error;

@ -202,13 +173,6 @@ export class LocalMLSyncContext implements MLSyncContext {
            this.config.faceClustering.method,
        );

-        this.objectDetectionService = MLFactory.getObjectDetectionService(
-            this.config.objectDetection.method,
-        );
-        this.sceneDetectionService = MLFactory.getSceneDetectionService(
-            this.config.sceneDetection.method,
-        );
-
        this.outOfSyncFiles = [];
        this.nSyncedFiles = 0;
        this.nSyncedFaces = 0;
@ -239,9 +203,6 @@ export class LocalMLSyncContext implements MLSyncContext {
    }

    public async dispose() {
-        // await this.faceDetectionService.dispose();
-        // await this.faceEmbeddingService.dispose();
-
        this.localFilesMap = undefined;
        await this.syncQueue.onIdle();
        this.syncQueue.removeAllListeners();
--- a/web/apps/photos/src/services/machineLearning/machineLearningService.ts
+++ b/web/apps/photos/src/services/machineLearning/machineLearningService.ts
@ -2,9 +2,6 @@ import log from "@/next/log";
 import { APPS } from "@ente/shared/apps/constants";
 import ComlinkCryptoWorker from "@ente/shared/crypto";
 import { CustomError, parseUploadErrorCodes } from "@ente/shared/error";
-import "@tensorflow/tfjs-backend-cpu";
-import "@tensorflow/tfjs-backend-webgl";
-import * as tf from "@tensorflow/tfjs-core";
 import { MAX_ML_SYNC_ERROR_COUNT } from "constants/mlConfig";
 import downloadManager from "services/download";
 import { putEmbedding } from "services/embeddingService";
@ -21,13 +18,10 @@ import { LocalFileMlDataToServerFileMl } from "utils/machineLearning/mldataMappe
 import mlIDbStorage from "utils/storage/mlIDbStorage";
 import FaceService from "./faceService";
 import { MLFactory } from "./machineLearningFactory";
-import ObjectService from "./objectService";
 import PeopleService from "./peopleService";
 import ReaderService from "./readerService";

 class MachineLearningService {
-    private initialized = false;
-
    private localSyncContext: Promise<MLSyncContext>;
    private syncContext: Promise<MLSyncContext>;

@ -58,12 +52,6 @@ class MachineLearningService {
            await this.syncIndex(syncContext);
        }

-        // tf.engine().endScope();
-
-        // if (syncContext.config.tsne) {
-        //     await this.runTSNE(syncContext);
-        // }
-
        const mlSyncResult: MLSyncResult = {
            nOutOfSyncFiles: syncContext.outOfSyncFiles.length,
            nSyncedFiles: syncContext.nSyncedFiles,
@ -73,14 +61,10 @@ class MachineLearningService {
                    .length,
            nFaceNoise:
                syncContext.mlLibraryData?.faceClusteringResults?.noise.length,
-            tsne: syncContext.tsne,
            error: syncContext.error,
        };
        // log.info('[MLService] sync results: ', mlSyncResult);

-        // await syncContext.dispose();
-        log.info("Final TF Memory stats: ", JSON.stringify(tf.memory()));
-
        return mlSyncResult;
    }

@ -183,50 +167,6 @@ class MachineLearningService {
        log.info("getOutOfSyncFiles", Date.now() - startTime, "ms");
    }

-    // TODO: optimize, use indexdb indexes, move facecrops to cache to reduce io
-    // remove, already done
-    private async getUniqueOutOfSyncFilesNoIdx(
-        syncContext: MLSyncContext,
-        files: EnteFile[],
-    ) {
-        const limit = syncContext.config.batchSize;
-        const mlVersion = syncContext.config.mlVersion;
-        const uniqueFiles: Map<number, EnteFile> = new Map<number, EnteFile>();
-        for (let i = 0; uniqueFiles.size < limit && i < files.length; i++) {
-            const mlFileData = await this.getMLFileData(files[i].id);
-            const mlFileVersion = mlFileData?.mlVersion || 0;
-            if (
-                !uniqueFiles.has(files[i].id) &&
-                (!mlFileData?.errorCount || mlFileData.errorCount < 2) &&
-                (mlFileVersion < mlVersion ||
-                    syncContext.config.imageSource !== mlFileData.imageSource)
-            ) {
-                uniqueFiles.set(files[i].id, files[i]);
-            }
-        }
-
-        return [...uniqueFiles.values()];
-    }
-
-    private async getOutOfSyncFilesNoIdx(syncContext: MLSyncContext) {
-        const existingFilesMap = await this.getLocalFilesMap(syncContext);
-        // existingFiles.sort(
-        //     (a, b) => b.metadata.creationTime - a.metadata.creationTime
-        // );
-        console.time("getUniqueOutOfSyncFiles");
-        syncContext.outOfSyncFiles = await this.getUniqueOutOfSyncFilesNoIdx(
-            syncContext,
-            [...existingFilesMap.values()],
-        );
-        log.info("getUniqueOutOfSyncFiles");
-        log.info(
-            "Got unique outOfSyncFiles: ",
-            syncContext.outOfSyncFiles.length,
-            "for batchSize: ",
-            syncContext.config.batchSize,
-        );
-    }
-
    private async syncFiles(syncContext: MLSyncContext) {
        try {
            const functions = syncContext.outOfSyncFiles.map(
@ -295,7 +235,6 @@ class MachineLearningService {
        userID: number,
        enteFile: EnteFile,
        localFile?: globalThis.File,
-        textDetectionTimeoutIndex?: number,
    ): Promise<MlFileData | Error> {
        const syncContext = await this.getLocalSyncContext(token, userID);

@ -304,7 +243,6 @@ class MachineLearningService {
                syncContext,
                enteFile,
                localFile,
-                textDetectionTimeoutIndex,
            );

            if (syncContext.nSyncedFiles >= syncContext.config.batchSize) {
@ -322,19 +260,15 @@ class MachineLearningService {
        syncContext: MLSyncContext,
        enteFile: EnteFile,
        localFile?: globalThis.File,
-        textDetectionTimeoutIndex?: number,
    ): Promise<MlFileData> {
        try {
            console.log(
-                "Start index for ",
-                enteFile.title ?? "no title",
-                enteFile.id,
+                `Indexing ${enteFile.title ?? "<untitled>"} ${enteFile.id}`,
            );
            const mlFileData = await this.syncFile(
                syncContext,
                enteFile,
                localFile,
-                textDetectionTimeoutIndex,
            );
            syncContext.nSyncedFaces += mlFileData.faces?.length || 0;
            syncContext.nSyncedFiles += 1;
@ -363,16 +297,8 @@ class MachineLearningService {
                    throw error;
            }

-            await this.persistMLFileSyncError(syncContext, enteFile, error);
+            await this.persistMLFileSyncError(enteFile, error);
            syncContext.nSyncedFiles += 1;
-        } finally {
-            console.log(
-                "done index for ",
-                enteFile.title ?? "no title",
-                enteFile.id,
-            );
-            // addLogLine('TF Memory stats: ', JSON.stringify(tf.memory()));
-            log.info("TF Memory stats: ", JSON.stringify(tf.memory()));
        }
    }

@ -380,8 +306,6 @@ class MachineLearningService {
        syncContext: MLSyncContext,
        enteFile: EnteFile,
        localFile?: globalThis.File,
-        // eslint-disable-next-line @typescript-eslint/no-unused-vars
-        textDetectionTimeoutIndex?: number,
    ) {
        console.log("Syncing for file" + enteFile.title);
        const fileContext: MLSyncFileContext = { enteFile, localFile };
@ -406,34 +330,17 @@ class MachineLearningService {
            await ReaderService.getImageBitmap(syncContext, fileContext);
            await Promise.all([
                this.syncFileAnalyzeFaces(syncContext, fileContext),
-                // ObjectService.syncFileObjectDetections(
-                //     syncContext,
-                //     fileContext
-                // ),
-                // TextService.syncFileTextDetections(
-                //     syncContext,
-                //     fileContext,
-                //     textDetectionTimeoutIndex
-                // ),
            ]);
            newMlFile.errorCount = 0;
            newMlFile.lastErrorMessage = undefined;
            await this.persistOnServer(newMlFile, enteFile);
-            await this.persistMLFileData(syncContext, newMlFile);
+            await mlIDbStorage.putFile(newMlFile);
        } catch (e) {
            log.error("ml detection failed", e);
            newMlFile.mlVersion = oldMlFile.mlVersion;
            throw e;
        } finally {
-            fileContext.tfImage && fileContext.tfImage.dispose();
            fileContext.imageBitmap && fileContext.imageBitmap.close();
-            // log.info('8 TF Memory stats: ',JSON.stringify(tf.memory()));
-
-            // TODO: enable once faceId changes go in
-            // await removeOldFaceCrops(
-            //     fileContext.oldMlFile,
-            //     fileContext.newMlFile
-            // );
        }

        return newMlFile;
@ -458,38 +365,11 @@ class MachineLearningService {
        log.info("putEmbedding response: ", res);
    }

-    public async init() {
-        if (this.initialized) {
-            return;
-        }
-
-        await tf.ready();
-
-        log.info("01 TF Memory stats: ", JSON.stringify(tf.memory()));
-
-        this.initialized = true;
-    }
-
-    public async dispose() {
-        this.initialized = false;
-    }
-
    private async getMLFileData(fileId: number) {
        return mlIDbStorage.getFile(fileId);
    }

-    private async persistMLFileData(
-        syncContext: MLSyncContext,
-        mlFileData: MlFileData,
-    ) {
-        mlIDbStorage.putFile(mlFileData);
-    }
-
-    private async persistMLFileSyncError(
-        syncContext: MLSyncContext,
-        enteFile: EnteFile,
-        e: Error,
-    ) {
+    private async persistMLFileSyncError(enteFile: EnteFile, e: Error) {
        try {
            await mlIDbStorage.upsertFileInTx(enteFile.id, (mlFileData) => {
                if (!mlFileData) {
@ -522,8 +402,6 @@ class MachineLearningService {

        await PeopleService.syncPeopleIndex(syncContext);

-        await ObjectService.syncThingsIndex(syncContext);
-
        await this.persistMLLibraryData(syncContext);
    }

--- a/web/apps/photos/src/services/machineLearning/mlSyncJob.ts
+++ b/web/apps/photos/src/services/machineLearning/mlSyncJob.ts
@ -1,9 +0,0 @@
-import { JobResult } from "types/common/job";
-import { MLSyncResult } from "types/machineLearning";
-import { SimpleJob } from "utils/common/job";
-
-export interface MLSyncJobResult extends JobResult {
-    mlSyncResult: MLSyncResult;
-}
-
-export class MLSyncJob extends SimpleJob<MLSyncJobResult> {}
--- a/web/apps/photos/src/services/machineLearning/mlWorkManager.ts
+++ b/web/apps/photos/src/services/machineLearning/mlWorkManager.ts
@ -5,18 +5,26 @@ import { getToken, getUserID } from "@ente/shared/storage/localStorage/helpers";
 import { FILE_TYPE } from "constants/file";
 import debounce from "debounce";
 import PQueue from "p-queue";
+import { JobResult } from "types/common/job";
 import { EnteFile } from "types/file";
+import { MLSyncResult } from "types/machineLearning";
 import { getDedicatedMLWorker } from "utils/comlink/ComlinkMLWorker";
+import { SimpleJob } from "utils/common/job";
 import { logQueueStats } from "utils/machineLearning";
 import { getMLSyncJobConfig } from "utils/machineLearning/config";
 import mlIDbStorage from "utils/storage/mlIDbStorage";
 import { DedicatedMLWorker } from "worker/ml.worker";
-import { MLSyncJob, MLSyncJobResult } from "./mlSyncJob";

 const LIVE_SYNC_IDLE_DEBOUNCE_SEC = 30;
 const LIVE_SYNC_QUEUE_TIMEOUT_SEC = 300;
 const LOCAL_FILES_UPDATED_DEBOUNCE_SEC = 30;

+export interface MLSyncJobResult extends JobResult {
+    mlSyncResult: MLSyncResult;
+}
+
+export class MLSyncJob extends SimpleJob<MLSyncJobResult> {}
+
 class MLWorkManager {
    private mlSyncJob: MLSyncJob;
    private syncJobWorker: ComlinkWorker<typeof DedicatedMLWorker>;
@ -178,8 +186,7 @@ class MLWorkManager {
            return mlWorker.syncLocalFile(token, userID, enteFile, localFile);
        });

-        // @ts-expect-error "TODO: Fix ML related type errors"
-        if ("message" in result) {
+        if (result instanceof Error) {
            // TODO: redirect/refresh to gallery in case of session_expired
            // may not be required as uploader should anyways take care of this
            console.error("Error while syncing local file: ", result);
--- a/web/apps/photos/src/services/machineLearning/mobileFaceNetEmbeddingService.ts
+++ b/web/apps/photos/src/services/machineLearning/mobileFaceNetEmbeddingService.ts
@ -1,11 +1,4 @@
-import log from "@/next/log";
-import * as tf from "@tensorflow/tfjs-core";
-import {
-    MOBILEFACENET_EMBEDDING_SIZE,
-    MOBILEFACENET_FACE_SIZE,
-} from "constants/mlConfig";
-// import { TFLiteModel } from "@tensorflow/tfjs-tflite";
-// import PQueue from "p-queue";
+import { workerBridge } from "@/next/worker/worker-bridge";
 import {
    FaceEmbedding,
    FaceEmbeddingMethod,
@ -13,184 +6,36 @@ import {
    Versioned,
 } from "types/machineLearning";

-// TODO(MR): onnx-yolo
-// import * as ort from "onnxruntime-web";
-// import { env } from "onnxruntime-web";
-const ort: any = {};
+export const mobileFaceNetFaceSize = 112;

-import {
-    clamp,
-    getPixelBilinear,
-    normalizePixelBetweenMinus1And1,
-} from "utils/image";
-
-// TODO(MR): onnx-yolo
-// env.wasm.wasmPaths = "/js/onnx/";
 class MobileFaceNetEmbeddingService implements FaceEmbeddingService {
-    // TODO(MR): onnx-yolo
-    // private onnxInferenceSession?: ort.InferenceSession;
-    private onnxInferenceSession?: any;
    public method: Versioned<FaceEmbeddingMethod>;
    public faceSize: number;

-    public constructor(faceSize: number = MOBILEFACENET_FACE_SIZE) {
+    public constructor() {
        this.method = {
            value: "MobileFaceNet",
            version: 2,
        };
-        this.faceSize = faceSize;
-        // TODO: set timeout
-    }
-
-    private async initOnnx() {
-        console.log("start ort mobilefacenet");
-        this.onnxInferenceSession = await ort.InferenceSession.create(
-            "/models/mobilefacenet/mobilefacenet_opset15.onnx",
-        );
-        const faceBatchSize = 1;
-        const data = new Float32Array(
-            faceBatchSize * 3 * this.faceSize * this.faceSize,
-        );
-        const inputTensor = new ort.Tensor("float32", data, [
-            faceBatchSize,
-            this.faceSize,
-            this.faceSize,
-            3,
-        ]);
-        // TODO(MR): onnx-yolo
-        // const feeds: Record<string, ort.Tensor> = {};
-        const feeds: Record<string, any> = {};
-        const name = this.onnxInferenceSession.inputNames[0];
-        feeds[name] = inputTensor;
-        await this.onnxInferenceSession.run(feeds);
-        console.log("start end mobilefacenet");
-    }
-
-    private async getOnnxInferenceSession() {
-        if (!this.onnxInferenceSession) {
-            await this.initOnnx();
-        }
-        return this.onnxInferenceSession;
-    }
-
-    private preprocessImageBitmapToFloat32(
-        imageBitmap: ImageBitmap,
-        requiredWidth: number = this.faceSize,
-        requiredHeight: number = this.faceSize,
-        maintainAspectRatio: boolean = true,
-        normFunction: (
-            pixelValue: number,
-        ) => number = normalizePixelBetweenMinus1And1,
-    ) {
-        // Create an OffscreenCanvas and set its size
-        const offscreenCanvas = new OffscreenCanvas(
-            imageBitmap.width,
-            imageBitmap.height,
-        );
-        const ctx = offscreenCanvas.getContext("2d");
-        ctx.drawImage(imageBitmap, 0, 0, imageBitmap.width, imageBitmap.height);
-        const imageData = ctx.getImageData(
-            0,
-            0,
-            imageBitmap.width,
-            imageBitmap.height,
-        );
-        const pixelData = imageData.data;
-
-        let scaleW = requiredWidth / imageBitmap.width;
-        let scaleH = requiredHeight / imageBitmap.height;
-        if (maintainAspectRatio) {
-            const scale = Math.min(
-                requiredWidth / imageBitmap.width,
-                requiredHeight / imageBitmap.height,
-            );
-            scaleW = scale;
-            scaleH = scale;
-        }
-        const scaledWidth = clamp(
-            Math.round(imageBitmap.width * scaleW),
-            0,
-            requiredWidth,
-        );
-        const scaledHeight = clamp(
-            Math.round(imageBitmap.height * scaleH),
-            0,
-            requiredHeight,
-        );
-
-        const processedImage = new Float32Array(
-            1 * requiredWidth * requiredHeight * 3,
-        );
-        log.info("loaded mobileFaceNetModel: ", tf.getBackend());
-
-        // Populate the Float32Array with normalized pixel values
-        for (let h = 0; h < requiredHeight; h++) {
-            for (let w = 0; w < requiredWidth; w++) {
-                let pixel: {
-                    r: number;
-                    g: number;
-                    b: number;
-                };
-                if (w >= scaledWidth || h >= scaledHeight) {
-                    pixel = { r: 114, g: 114, b: 114 };
-                } else {
-                    pixel = getPixelBilinear(
-                        w / scaleW,
-                        h / scaleH,
-                        pixelData,
-                        imageBitmap.width,
-                        imageBitmap.height,
-                    );
-                }
-                const pixelIndex = 3 * (h * requiredWidth + w);
-                processedImage[pixelIndex] = normFunction(pixel.r);
-                processedImage[pixelIndex + 1] = normFunction(pixel.g);
-                processedImage[pixelIndex + 2] = normFunction(pixel.b);
-            }
-        }
-
-        return processedImage;
+        this.faceSize = mobileFaceNetFaceSize;
    }

    public async getFaceEmbeddings(
        faceData: Float32Array,
    ): Promise<Array<FaceEmbedding>> {
-        const inputTensor = new ort.Tensor("float32", faceData, [
-            Math.round(faceData.length / (this.faceSize * this.faceSize * 3)),
-            this.faceSize,
-            this.faceSize,
-            3,
-        ]);
-        // TODO(MR): onnx-yolo
-        // const feeds: Record<string, ort.Tensor> = {};
-        const feeds: Record<string, any> = {};
-        feeds["img_inputs"] = inputTensor;
-        const inferenceSession = await this.getOnnxInferenceSession();
-        // TODO(MR): onnx-yolo
-        // const runout: ort.InferenceSession.OnnxValueMapType =
-        const runout: any = await inferenceSession.run(feeds);
-        // const test = runout.embeddings;
-        // const test2 = test.cpuData;
-        const outputData = runout.embeddings["cpuData"] as Float32Array;
+        const outputData = await workerBridge.faceEmbedding(faceData);
+
+        const embeddingSize = 192;
        const embeddings = new Array<FaceEmbedding>(
-            outputData.length / MOBILEFACENET_EMBEDDING_SIZE,
+            outputData.length / embeddingSize,
        );
        for (let i = 0; i < embeddings.length; i++) {
            embeddings[i] = new Float32Array(
-                outputData.slice(
-                    i * MOBILEFACENET_EMBEDDING_SIZE,
-                    (i + 1) * MOBILEFACENET_EMBEDDING_SIZE,
-                ),
+                outputData.slice(i * embeddingSize, (i + 1) * embeddingSize),
            );
        }
        return embeddings;
    }
-
-    public async dispose() {
-        const inferenceSession = await this.getOnnxInferenceSession();
-        inferenceSession?.release();
-        this.onnxInferenceSession = undefined;
-    }
 }

 export default new MobileFaceNetEmbeddingService();
--- a/web/apps/photos/src/services/machineLearning/objectService.ts
+++ b/web/apps/photos/src/services/machineLearning/objectService.ts
@ -1,146 +0,0 @@
-import log from "@/next/log";
-import {
-    DetectedObject,
-    MLSyncContext,
-    MLSyncFileContext,
-    Thing,
-} from "types/machineLearning";
-import {
-    getAllObjectsFromMap,
-    getObjectId,
-    isDifferentOrOld,
-} from "utils/machineLearning";
-import mlIDbStorage from "utils/storage/mlIDbStorage";
-import ReaderService from "./readerService";
-
-class ObjectService {
-    async syncFileObjectDetections(
-        syncContext: MLSyncContext,
-        fileContext: MLSyncFileContext,
-    ) {
-        const startTime = Date.now();
-        const { oldMlFile, newMlFile } = fileContext;
-        if (
-            !isDifferentOrOld(
-                oldMlFile?.objectDetectionMethod,
-                syncContext.objectDetectionService.method,
-            ) &&
-            !isDifferentOrOld(
-                oldMlFile?.sceneDetectionMethod,
-                syncContext.sceneDetectionService.method,
-            ) &&
-            oldMlFile?.imageSource === syncContext.config.imageSource
-        ) {
-            newMlFile.objects = oldMlFile?.objects;
-            newMlFile.imageSource = oldMlFile.imageSource;
-            newMlFile.imageDimensions = oldMlFile.imageDimensions;
-            newMlFile.objectDetectionMethod = oldMlFile.objectDetectionMethod;
-            newMlFile.sceneDetectionMethod = oldMlFile.sceneDetectionMethod;
-            return;
-        }
-
-        newMlFile.objectDetectionMethod =
-            syncContext.objectDetectionService.method;
-        newMlFile.sceneDetectionMethod =
-            syncContext.sceneDetectionService.method;
-
-        fileContext.newDetection = true;
-        const imageBitmap = await ReaderService.getImageBitmap(
-            syncContext,
-            fileContext,
-        );
-        const objectDetections =
-            await syncContext.objectDetectionService.detectObjects(
-                imageBitmap,
-                syncContext.config.objectDetection.maxNumBoxes,
-                syncContext.config.objectDetection.minScore,
-            );
-        objectDetections.push(
-            ...(await syncContext.sceneDetectionService.detectScenes(
-                imageBitmap,
-                syncContext.config.sceneDetection.minScore,
-            )),
-        );
-        // log.info('3 TF Memory stats: ',JSON.stringify(tf.memory()));
-        // TODO: reenable faces filtering based on width
-        const detectedObjects = objectDetections?.map((detection) => {
-            return {
-                fileID: fileContext.enteFile.id,
-                detection,
-            } as DetectedObject;
-        });
-        newMlFile.objects = detectedObjects?.map((detectedObject) => ({
-            ...detectedObject,
-            id: getObjectId(detectedObject, newMlFile.imageDimensions),
-            className: detectedObject.detection.class,
-        }));
-        // ?.filter((f) =>
-        //     f.box.width > syncContext.config.faceDetection.minFaceSize
-        // );
-        log.info(
-            `object detection time taken ${fileContext.enteFile.id}`,
-            Date.now() - startTime,
-            "ms",
-        );
-
-        log.info("[MLService] Detected Objects: ", newMlFile.objects?.length);
-    }
-
-    async getAllSyncedObjectsMap(syncContext: MLSyncContext) {
-        if (syncContext.allSyncedObjectsMap) {
-            return syncContext.allSyncedObjectsMap;
-        }
-
-        syncContext.allSyncedObjectsMap = await mlIDbStorage.getAllObjectsMap();
-        return syncContext.allSyncedObjectsMap;
-    }
-
-    public async clusterThings(syncContext: MLSyncContext): Promise<Thing[]> {
-        const allObjectsMap = await this.getAllSyncedObjectsMap(syncContext);
-        const allObjects = getAllObjectsFromMap(allObjectsMap);
-        const objectClusters = new Map<string, number[]>();
-        allObjects.map((object) => {
-            if (!objectClusters.has(object.detection.class)) {
-                objectClusters.set(object.detection.class, []);
-            }
-            const objectsInCluster = objectClusters.get(object.detection.class);
-            objectsInCluster.push(object.fileID);
-        });
-        return [...objectClusters.entries()].map(([className, files], id) => ({
-            id,
-            name: className,
-            files,
-        }));
-    }
-
-    async syncThingsIndex(syncContext: MLSyncContext) {
-        const filesVersion = await mlIDbStorage.getIndexVersion("files");
-        log.info("things", await mlIDbStorage.getIndexVersion("things"));
-        if (filesVersion <= (await mlIDbStorage.getIndexVersion("things"))) {
-            log.info(
-                "[MLService] Skipping people index as already synced to latest version",
-            );
-            return;
-        }
-
-        const things = await this.clusterThings(syncContext);
-
-        if (!things || things.length < 1) {
-            return;
-        }
-
-        await mlIDbStorage.clearAllThings();
-
-        for (const thing of things) {
-            await mlIDbStorage.putThing(thing);
-        }
-
-        await mlIDbStorage.setIndexVersion("things", filesVersion);
-    }
-
-    async getAllThings() {
-        return await mlIDbStorage.getAllThings();
-    }
-}
-
-export default new ObjectService();
--- a/web/apps/photos/src/services/machineLearning/readerService.ts
+++ b/web/apps/photos/src/services/machineLearning/readerService.ts
@ -16,7 +16,6 @@ class ReaderService {
            if (fileContext.imageBitmap) {
                return fileContext.imageBitmap;
            }
-            // log.info('1 TF Memory stats: ',JSON.stringify(tf.memory()));
            if (fileContext.localFile) {
                if (
                    fileContext.enteFile.metadata.fileType !== FILE_TYPE.IMAGE
@ -47,7 +46,6 @@ class ReaderService {
            fileContext.newMlFile.imageSource = syncContext.config.imageSource;
            const { width, height } = fileContext.imageBitmap;
            fileContext.newMlFile.imageDimensions = { width, height };
-            // log.info('2 TF Memory stats: ',JSON.stringify(tf.memory()));

            return fileContext.imageBitmap;
        } catch (e) {
--- a/web/apps/photos/src/services/machineLearning/ssdMobileNetV2Service.ts
+++ b/web/apps/photos/src/services/machineLearning/ssdMobileNetV2Service.ts
@ -1,66 +0,0 @@
-import log from "@/next/log";
-import * as tf from "@tensorflow/tfjs-core";
-import {
-    ObjectDetection,
-    ObjectDetectionMethod,
-    ObjectDetectionService,
-    Versioned,
-} from "types/machineLearning";
-
-import * as SSDMobileNet from "@tensorflow-models/coco-ssd";
-import { OBJECT_DETECTION_IMAGE_SIZE } from "constants/mlConfig";
-import { resizeToSquare } from "utils/image";
-
-class SSDMobileNetV2 implements ObjectDetectionService {
-    private ssdMobileNetV2Model: SSDMobileNet.ObjectDetection;
-    public method: Versioned<ObjectDetectionMethod>;
-    private ready: Promise<void>;
-
-    public constructor() {
-        this.method = {
-            value: "SSDMobileNetV2",
-            version: 1,
-        };
-    }
-
-    private async init() {
-        this.ssdMobileNetV2Model = await SSDMobileNet.load({
-            base: "mobilenet_v2",
-            modelUrl: "/models/ssdmobilenet/model.json",
-        });
-        log.info("loaded ssdMobileNetV2Model", tf.getBackend());
-    }
-
-    private async getSSDMobileNetV2Model() {
-        if (!this.ready) {
-            this.ready = this.init();
-        }
-        await this.ready;
-        return this.ssdMobileNetV2Model;
-    }
-
-    public async detectObjects(
-        image: ImageBitmap,
-        maxNumberBoxes: number,
-        minScore: number,
-    ): Promise<ObjectDetection[]> {
-        const ssdMobileNetV2Model = await this.getSSDMobileNetV2Model();
-        const resized = resizeToSquare(image, OBJECT_DETECTION_IMAGE_SIZE);
-        const tfImage = tf.browser.fromPixels(resized.image);
-        const detections = await ssdMobileNetV2Model.detect(
-            tfImage,
-            maxNumberBoxes,
-            minScore,
-        );
-        tfImage.dispose();
-        return detections;
-    }
-
-    public async dispose() {
-        const ssdMobileNetV2Model = await this.getSSDMobileNetV2Model();
-        ssdMobileNetV2Model?.dispose();
-        this.ssdMobileNetV2Model = null;
-    }
-}
-
-export default new SSDMobileNetV2();
--- a/web/apps/photos/src/services/machineLearning/yoloFaceDetectionService.ts
+++ b/web/apps/photos/src/services/machineLearning/yoloFaceDetectionService.ts
@ -1,7 +1,12 @@
+import { workerBridge } from "@/next/worker/worker-bridge";
+import { euclidean } from "hdbscan";
 import {
-    BLAZEFACE_FACE_SIZE,
-    MAX_FACE_DISTANCE_PERCENT,
-} from "constants/mlConfig";
+    Matrix,
+    applyToPoint,
+    compose,
+    scale,
+    translate,
+} from "transformation-matrix";
 import { Dimensions } from "types/image";
 import {
    FaceDetection,
@ -15,57 +20,50 @@ import {
    normalizePixelBetween0And1,
 } from "utils/image";
 import { newBox } from "utils/machineLearning";
-import { removeDuplicateDetections } from "utils/machineLearning/faceDetection";
-import {
-    computeTransformToBox,
-    transformBox,
-    transformPoints,
-} from "utils/machineLearning/transform";
 import { Box, Point } from "../../../thirdparty/face-api/classes";

-// TODO(MR): onnx-yolo
-// import * as ort from "onnxruntime-web";
-// import { env } from "onnxruntime-web";
-const ort: any = {};
-
-// TODO(MR): onnx-yolo
-// env.wasm.wasmPaths = "/js/onnx/";
 class YoloFaceDetectionService implements FaceDetectionService {
-    // TODO(MR): onnx-yolo
-    // private onnxInferenceSession?: ort.InferenceSession;
-    private onnxInferenceSession?: any;
    public method: Versioned<FaceDetectionMethod>;
-    private desiredFaceSize;

-    public constructor(desiredFaceSize: number = BLAZEFACE_FACE_SIZE) {
+    public constructor() {
        this.method = {
            value: "YoloFace",
            version: 1,
        };
-        this.desiredFaceSize = desiredFaceSize;
    }

-    private async initOnnx() {
-        console.log("start ort");
-        this.onnxInferenceSession = await ort.InferenceSession.create(
-            "/models/yoloface/yolov5s_face_640_640_dynamic.onnx",
+    public async detectFaces(
+        imageBitmap: ImageBitmap,
+    ): Promise<Array<FaceDetection>> {
+        const maxFaceDistancePercent = Math.sqrt(2) / 100;
+        const maxFaceDistance = imageBitmap.width * maxFaceDistancePercent;
+        const preprocessResult =
+            this.preprocessImageBitmapToFloat32ChannelsFirst(
+                imageBitmap,
+                640,
+                640,
+            );
+        const data = preprocessResult.data;
+        const resized = preprocessResult.newSize;
+        const outputData = await workerBridge.detectFaces(data);
+        const faces = this.getFacesFromYoloOutput(
+            outputData as Float32Array,
+            0.7,
        );
-        const data = new Float32Array(1 * 3 * 640 * 640);
-        const inputTensor = new ort.Tensor("float32", data, [1, 3, 640, 640]);
-        // TODO(MR): onnx-yolo
-        // const feeds: Record<string, ort.Tensor> = {};
-        const feeds: Record<string, any> = {};
-        const name = this.onnxInferenceSession.inputNames[0];
-        feeds[name] = inputTensor;
-        await this.onnxInferenceSession.run(feeds);
-        console.log("start end");
-    }
-
-    private async getOnnxInferenceSession() {
-        if (!this.onnxInferenceSession) {
-            await this.initOnnx();
-        }
-        return this.onnxInferenceSession;
+        const inBox = newBox(0, 0, resized.width, resized.height);
+        const toBox = newBox(0, 0, imageBitmap.width, imageBitmap.height);
+        const transform = computeTransformToBox(inBox, toBox);
+        const faceDetections: Array<FaceDetection> = faces?.map((f) => {
+            const box = transformBox(f.box, transform);
+            const normLandmarks = f.landmarks;
+            const landmarks = transformPoints(normLandmarks, transform);
+            return {
+                box,
+                landmarks,
+                probability: f.probability as number,
+            } as FaceDetection;
+        });
+        return removeDuplicateDetections(faceDetections, maxFaceDistance);
    }

    private preprocessImageBitmapToFloat32ChannelsFirst(
@ -160,43 +158,6 @@ class YoloFaceDetectionService implements FaceDetectionService {
        };
    }

-    /**
-     * @deprecated The method should not be used
-     */
-    private imageBitmapToTensorData(imageBitmap) {
-        // Create an OffscreenCanvas and set its size
-        const offscreenCanvas = new OffscreenCanvas(
-            imageBitmap.width,
-            imageBitmap.height,
-        );
-        const ctx = offscreenCanvas.getContext("2d");
-        ctx.drawImage(imageBitmap, 0, 0, imageBitmap.width, imageBitmap.height);
-        const imageData = ctx.getImageData(
-            0,
-            0,
-            imageBitmap.width,
-            imageBitmap.height,
-        );
-        const pixelData = imageData.data;
-        const data = new Float32Array(
-            1 * 3 * imageBitmap.width * imageBitmap.height,
-        );
-        // Populate the Float32Array with normalized pixel values
-        for (let i = 0; i < pixelData.length; i += 4) {
-            // Normalize pixel values to the range [0, 1]
-            data[i / 4] = pixelData[i] / 255.0; // Red channel
-            data[i / 4 + imageBitmap.width * imageBitmap.height] =
-                pixelData[i + 1] / 255.0; // Green channel
-            data[i / 4 + 2 * imageBitmap.width * imageBitmap.height] =
-                pixelData[i + 2] / 255.0; // Blue channel
-        }
-
-        return {
-            data: data,
-            shape: [1, 3, imageBitmap.width, imageBitmap.height],
-        };
-    }
-
    // The rowOutput is a Float32Array of shape [25200, 16], where each row represents a bounding box.
    private getFacesFromYoloOutput(
        rowOutput: Float32Array,
@ -274,58 +235,98 @@ class YoloFaceDetectionService implements FaceDetectionService {
            probability: faceDetection.probability,
        };
    }
-
-    private async estimateOnnx(imageBitmap: ImageBitmap) {
-        const maxFaceDistance = imageBitmap.width * MAX_FACE_DISTANCE_PERCENT;
-        const preprocessResult =
-            this.preprocessImageBitmapToFloat32ChannelsFirst(
-                imageBitmap,
-                640,
-                640,
-            );
-        const data = preprocessResult.data;
-        const resized = preprocessResult.newSize;
-        const inputTensor = new ort.Tensor("float32", data, [1, 3, 640, 640]);
-        // TODO(MR): onnx-yolo
-        // const feeds: Record<string, ort.Tensor> = {};
-        const feeds: Record<string, any> = {};
-        feeds["input"] = inputTensor;
-        const inferenceSession = await this.getOnnxInferenceSession();
-        const runout = await inferenceSession.run(feeds);
-        const outputData = runout.output.data;
-        const faces = this.getFacesFromYoloOutput(
-            outputData as Float32Array,
-            0.7,
-        );
-        const inBox = newBox(0, 0, resized.width, resized.height);
-        const toBox = newBox(0, 0, imageBitmap.width, imageBitmap.height);
-        const transform = computeTransformToBox(inBox, toBox);
-        const faceDetections: Array<FaceDetection> = faces?.map((f) => {
-            const box = transformBox(f.box, transform);
-            const normLandmarks = f.landmarks;
-            const landmarks = transformPoints(normLandmarks, transform);
-            return {
-                box,
-                landmarks,
-                probability: f.probability as number,
-            } as FaceDetection;
-        });
-        return removeDuplicateDetections(faceDetections, maxFaceDistance);
-    }
-
-    public async detectFaces(
-        imageBitmap: ImageBitmap,
-    ): Promise<Array<FaceDetection>> {
-        // measure time taken
-        const facesFromOnnx = await this.estimateOnnx(imageBitmap);
-        return facesFromOnnx;
-    }
-
-    public async dispose() {
-        const inferenceSession = await this.getOnnxInferenceSession();
-        inferenceSession?.release();
-        this.onnxInferenceSession = undefined;
-    }
 }

 export default new YoloFaceDetectionService();
+
+/**
+ * Removes duplicate face detections from an array of detections.
+ *
+ * This function sorts the detections by their probability in descending order, then iterates over them.
+ * For each detection, it calculates the Euclidean distance to all other detections.
+ * If the distance is less than or equal to the specified threshold (`withinDistance`), the other detection is considered a duplicate and is removed.
+ *
+ * @param detections - An array of face detections to remove duplicates from.
+ * @param withinDistance - The maximum Euclidean distance between two detections for them to be considered duplicates.
+ *
+ * @returns An array of face detections with duplicates removed.
+ */
+function removeDuplicateDetections(
+    detections: Array<FaceDetection>,
+    withinDistance: number,
+) {
+    // console.time('removeDuplicates');
+    detections.sort((a, b) => b.probability - a.probability);
+    const isSelected = new Map<number, boolean>();
+    for (let i = 0; i < detections.length; i++) {
+        if (isSelected.get(i) === false) {
+            continue;
+        }
+        isSelected.set(i, true);
+        for (let j = i + 1; j < detections.length; j++) {
+            if (isSelected.get(j) === false) {
+                continue;
+            }
+            const centeri = getDetectionCenter(detections[i]);
+            const centerj = getDetectionCenter(detections[j]);
+            const dist = euclidean(
+                [centeri.x, centeri.y],
+                [centerj.x, centerj.y],
+            );
+            if (dist <= withinDistance) {
+                isSelected.set(j, false);
+            }
+        }
+    }
+
+    const uniques: Array<FaceDetection> = [];
+    for (let i = 0; i < detections.length; i++) {
+        isSelected.get(i) && uniques.push(detections[i]);
+    }
+    // console.timeEnd('removeDuplicates');
+    return uniques;
+}
+
+function getDetectionCenter(detection: FaceDetection) {
+    const center = new Point(0, 0);
+    // TODO: first 4 landmarks is applicable to blazeface only
+    // this needs to consider eyes, nose and mouth landmarks to take center
+    detection.landmarks?.slice(0, 4).forEach((p) => {
+        center.x += p.x;
+        center.y += p.y;
+    });
+
+    return center.div({ x: 4, y: 4 });
+}
+
+function computeTransformToBox(inBox: Box, toBox: Box): Matrix {
+    return compose(
+        translate(toBox.x, toBox.y),
+        scale(toBox.width / inBox.width, toBox.height / inBox.height),
+    );
+}
+
+function transformPoint(point: Point, transform: Matrix) {
+    const txdPoint = applyToPoint(transform, point);
+    return new Point(txdPoint.x, txdPoint.y);
+}
+
+function transformPoints(points: Point[], transform: Matrix) {
+    return points?.map((p) => transformPoint(p, transform));
+}
+
+function transformBox(box: Box, transform: Matrix) {
+    const topLeft = transformPoint(box.topLeft, transform);
+    const bottomRight = transformPoint(box.bottomRight, transform);
+
+    return newBoxFromPoints(topLeft.x, topLeft.y, bottomRight.x, bottomRight.y);
+}
+
+function newBoxFromPoints(
+    left: number,
+    top: number,
+    right: number,
+    bottom: number,
+) {
+    return new Box({ left, top, right, bottom });
+}
--- a/web/apps/photos/src/services/searchService.ts
+++ b/web/apps/photos/src/services/searchService.ts
@ -6,7 +6,7 @@ import { t } from "i18next";
 import { Collection } from "types/collection";
 import { EntityType, LocationTag, LocationTagData } from "types/entity";
 import { EnteFile } from "types/file";
-import { Person, Thing } from "types/machineLearning";
+import { Person } from "types/machineLearning";
 import {
    ClipSearchScores,
    DateValue,
@ -25,7 +25,6 @@ import { clipService, computeClipMatchScore } from "./clip-service";
 import { getLocalEmbeddings } from "./embeddingService";
 import { getLatestEntities } from "./entityService";
 import locationSearchService, { City } from "./locationSearchService";
-import ObjectService from "./machineLearning/objectService";

 const DIGITS = new Set(["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]);

@ -56,7 +55,6 @@ export const getAutoCompleteSuggestions =
                getFileNameSuggestion(searchPhrase, files),
                getFileCaptionSuggestion(searchPhrase, files),
                ...(await getLocationSuggestions(searchPhrase)),
-                ...(await getThingSuggestion(searchPhrase)),
            ].filter((suggestion) => !!suggestion);

            return convertSuggestionsToOptions(suggestions);
@ -289,19 +287,6 @@ async function getLocationSuggestions(searchPhrase: string) {
    return [...locationTagSuggestions, ...citySearchSuggestions];
 }

-async function getThingSuggestion(searchPhrase: string): Promise<Suggestion[]> {
-    const thingResults = await searchThing(searchPhrase);
-
-    return thingResults.map(
-        (searchResult) =>
-            ({
-                type: SuggestionType.THING,
-                value: searchResult,
-                label: searchResult.name,
-            }) as Suggestion,
-    );
-}
-
 async function getClipSuggestion(searchPhrase: string): Promise<Suggestion> {
    try {
        if (!clipService.isPlatformSupported()) {
@ -389,13 +374,6 @@ async function searchLocationTag(searchPhrase: string): Promise<LocationTag[]> {
    return matchedLocationTags;
 }

-async function searchThing(searchPhrase: string) {
-    const things = await ObjectService.getAllThings();
-    return things.filter((thing) =>
-        thing.name.toLocaleLowerCase().includes(searchPhrase),
-    );
-}
-
 async function searchClip(searchPhrase: string): Promise<ClipSearchScores> {
    const imageEmbeddings = await getLocalEmbeddings();
    const textEmbedding = await clipService.getTextEmbedding(searchPhrase);
@ -445,10 +423,9 @@ function convertSuggestionToSearchQuery(option: Suggestion): Search {
        case SuggestionType.PERSON:
            return { person: option.value as Person };

-        case SuggestionType.THING:
-            return { thing: option.value as Thing };
        case SuggestionType.FILE_TYPE:
            return { fileType: option.value as FILE_TYPE };
+
        case SuggestionType.CLIP:
            return { clip: option.value as ClipSearchScores };
    }
--- a/web/apps/photos/src/types/machineLearning/archface.ts
+++ b/web/apps/photos/src/types/machineLearning/archface.ts
@ -1,16 +0,0 @@
-export const ARCFACE_LANDMARKS = [
-    [38.2946, 51.6963],
-    [73.5318, 51.5014],
-    [56.0252, 71.7366],
-    [56.1396, 92.2848],
-] as Array<[number, number]>;
-
-export const ARCFACE_LANDMARKS_FACE_SIZE = 112;
-
-export const ARC_FACE_5_LANDMARKS = [
-    [38.2946, 51.6963],
-    [73.5318, 51.5014],
-    [56.0252, 71.7366],
-    [41.5493, 92.3655],
-    [70.7299, 92.2041],
-] as Array<[number, number]>;
--- a/web/apps/photos/src/types/machineLearning/index.ts
+++ b/web/apps/photos/src/types/machineLearning/index.ts
@ -1,5 +1,3 @@
-import * as tf from "@tensorflow/tfjs-core";
-
 import { DebugInfo } from "hdbscan";
 import PQueue from "p-queue";
 import { EnteFile } from "types/file";
@ -12,21 +10,9 @@ export interface MLSyncResult {
    nSyncedFaces: number;
    nFaceClusters: number;
    nFaceNoise: number;
-    tsne?: any;
    error?: Error;
 }

-export interface DebugFace {
-    fileId: string;
-    // face: FaceApiResult;
-    face: AlignedFace;
-    embedding: FaceEmbedding;
-    faceImage: FaceImage;
-}
-
-export declare type FaceImage = Array<Array<Array<number>>>;
-export declare type FaceImageBlob = Blob;
-
 export declare type FaceDescriptor = Float32Array;

 export declare type Cluster = Array<number>;
@ -59,23 +45,13 @@ export declare type Landmark = Point;

 export declare type ImageType = "Original" | "Preview";

-export declare type FaceDetectionMethod =
-    | "BlazeFace"
-    | "FaceApiSSD"
-    | "YoloFace";
-
-export declare type ObjectDetectionMethod = "SSDMobileNetV2";
-
-export declare type SceneDetectionMethod = "ImageScene";
+export declare type FaceDetectionMethod = "YoloFace";

 export declare type FaceCropMethod = "ArcFace";

-export declare type FaceAlignmentMethod =
-    | "ArcFace"
-    | "FaceApiDlib"
-    | "RotatedFaceApiDlib";
+export declare type FaceAlignmentMethod = "ArcFace";

-export declare type FaceEmbeddingMethod = "MobileFaceNet" | "FaceApiDlib";
+export declare type FaceEmbeddingMethod = "MobileFaceNet";

 export declare type BlurDetectionMethod = "Laplacian";

@ -155,45 +131,15 @@ export interface Person {
    displayImageUrl?: string;
 }

-export interface ObjectDetection {
-    bbox: [number, number, number, number];
-    class: string;
-    score: number;
-}
-
-export interface DetectedObject {
-    fileID: number;
-    detection: ObjectDetection;
-}
-
-export interface RealWorldObject extends DetectedObject {
-    id: string;
-    className: string;
-}
-
-export interface Thing {
-    id: number;
-    name: string;
-    files: Array<number>;
-}
-
-export interface WordGroup {
-    word: string;
-    files: Array<number>;
-}
-
 export interface MlFileData {
    fileId: number;
    faces?: Face[];
-    objects?: RealWorldObject[];
    imageSource?: ImageType;
    imageDimensions?: Dimensions;
    faceDetectionMethod?: Versioned<FaceDetectionMethod>;
    faceCropMethod?: Versioned<FaceCropMethod>;
    faceAlignmentMethod?: Versioned<FaceAlignmentMethod>;
    faceEmbeddingMethod?: Versioned<FaceEmbeddingMethod>;
-    objectDetectionMethod?: Versioned<ObjectDetectionMethod>;
-    sceneDetectionMethod?: Versioned<SceneDetectionMethod>;
    mlVersion: number;
    errorCount: number;
    lastErrorMessage?: string;
@ -203,17 +149,6 @@ export interface FaceDetectionConfig {
    method: FaceDetectionMethod;
 }

-export interface ObjectDetectionConfig {
-    method: ObjectDetectionMethod;
-    maxNumBoxes: number;
-    minScore: number;
-}
-
-export interface SceneDetectionConfig {
-    method: SceneDetectionMethod;
-    minScore: number;
-}
-
 export interface FaceCropConfig {
    enabled: boolean;
    method: FaceCropMethod;
@ -263,9 +198,6 @@ export interface MLSyncConfig {
    blurDetection: BlurDetectionConfig;
    faceEmbedding: FaceEmbeddingConfig;
    faceClustering: FaceClusteringConfig;
-    objectDetection: ObjectDetectionConfig;
-    sceneDetection: SceneDetectionConfig;
-    tsne?: TSNEConfig;
    mlVersion: number;
 }

@ -285,16 +217,12 @@ export interface MLSyncContext {
    faceEmbeddingService: FaceEmbeddingService;
    blurDetectionService: BlurDetectionService;
    faceClusteringService: ClusteringService;
-    objectDetectionService: ObjectDetectionService;
-    sceneDetectionService: SceneDetectionService;

    localFilesMap: Map<number, EnteFile>;
    outOfSyncFiles: EnteFile[];
    nSyncedFiles: number;
    nSyncedFaces: number;
    allSyncedFacesMap?: Map<number, Array<Face>>;
-    allSyncedObjectsMap?: Map<number, Array<RealWorldObject>>;
-    tsne?: any;

    error?: Error;

@ -314,7 +242,6 @@ export interface MLSyncFileContext {
    oldMlFile?: MlFileData;
    newMlFile?: MlFileData;

-    tfImage?: tf.Tensor3D;
    imageBitmap?: ImageBitmap;

    newDetection?: boolean;
@ -331,33 +258,12 @@ export declare type MLIndex = "files" | "people";

 export interface FaceDetectionService {
    method: Versioned<FaceDetectionMethod>;
-    // init(): Promise<void>;
+
    detectFaces(image: ImageBitmap): Promise<Array<FaceDetection>>;
    getRelativeDetection(
        faceDetection: FaceDetection,
        imageDimensions: Dimensions,
    ): FaceDetection;
-    dispose(): Promise<void>;
-}
-
-export interface ObjectDetectionService {
-    method: Versioned<ObjectDetectionMethod>;
-    // init(): Promise<void>;
-    detectObjects(
-        image: ImageBitmap,
-        maxNumBoxes: number,
-        minScore: number,
-    ): Promise<ObjectDetection[]>;
-    dispose(): Promise<void>;
-}
-
-export interface SceneDetectionService {
-    method: Versioned<SceneDetectionMethod>;
-    // init(): Promise<void>;
-    detectScenes(
-        image: ImageBitmap,
-        minScore: number,
-    ): Promise<ObjectDetection[]>;
 }

 export interface FaceCropService {
@ -378,9 +284,8 @@ export interface FaceAlignmentService {
 export interface FaceEmbeddingService {
    method: Versioned<FaceEmbeddingMethod>;
    faceSize: number;
-    // init(): Promise<void>;
+
    getFaceEmbeddings(faceImages: Float32Array): Promise<Array<FaceEmbedding>>;
-    dispose(): Promise<void>;
 }

 export interface BlurDetectionService {
--- a/web/apps/photos/src/types/search/index.ts
+++ b/web/apps/photos/src/types/search/index.ts
@ -2,7 +2,7 @@ import { FILE_TYPE } from "constants/file";
 import { City } from "services/locationSearchService";
 import { LocationTagData } from "types/entity";
 import { EnteFile } from "types/file";
-import { Person, Thing, WordGroup } from "types/machineLearning";
+import { Person } from "types/machineLearning";
 import { IndexStatus } from "types/machineLearning/ui";

 export enum SuggestionType {
@ -12,8 +12,6 @@ export enum SuggestionType {
    FILE_NAME = "FILE_NAME",
    PERSON = "PERSON",
    INDEX_STATUS = "INDEX_STATUS",
-    THING = "THING",
-    TEXT = "TEXT",
    FILE_CAPTION = "FILE_CAPTION",
    FILE_TYPE = "FILE_TYPE",
    CLIP = "CLIP",
@ -34,8 +32,6 @@ export interface Suggestion {
        | number[]
        | Person
        | IndexStatus
-        | Thing
-        | WordGroup
        | LocationTagData
        | City
        | FILE_TYPE
@ -50,8 +46,6 @@ export type Search = {
    collection?: number;
    files?: number[];
    person?: Person;
-    thing?: Thing;
-    text?: WordGroup;
    fileType?: FILE_TYPE;
    clip?: ClipSearchScores;
 };
--- a/web/apps/photos/src/utils/common/job.ts
+++ b/web/apps/photos/src/utils/common/job.ts
@ -50,7 +50,7 @@ export class SimpleJob<R extends JobResult> {

        try {
            const jobResult = await this.runCallback();
-            if (jobResult.shouldBackoff) {
+            if (jobResult && jobResult.shouldBackoff) {
                this.intervalSec = Math.min(
                    this.config.maxItervalSec,
                    this.intervalSec * this.config.backoffMultiplier,
--- a/web/apps/photos/src/utils/machineLearning/faceAlign.ts
+++ b/web/apps/photos/src/utils/machineLearning/faceAlign.ts
@ -1,34 +1,39 @@
-import * as tf from "@tensorflow/tfjs-core";
-import { Matrix, inverse } from "ml-matrix";
+import { Matrix } from "ml-matrix";
 import { getSimilarityTransformation } from "similarity-transformation";
-import { Dimensions } from "types/image";
 import { FaceAlignment, FaceDetection } from "types/machineLearning";
-import {
-    ARCFACE_LANDMARKS,
-    ARCFACE_LANDMARKS_FACE_SIZE,
-    ARC_FACE_5_LANDMARKS,
-} from "types/machineLearning/archface";
-import { cropWithRotation, transform } from "utils/image";
-import {
-    computeRotation,
-    enlargeBox,
-    extractFaces,
-    getBoxCenter,
-    getBoxCenterPt,
-    toTensor4D,
-} from ".";
-import { Box, Point } from "../../../thirdparty/face-api/classes";
+import { Point } from "../../../thirdparty/face-api/classes";

-export function normalizeLandmarks(
-    landmarks: Array<[number, number]>,
-    faceSize: number,
-): Array<[number, number]> {
-    return landmarks.map((landmark) =>
-        landmark.map((p) => p / faceSize),
-    ) as Array<[number, number]>;
+const ARCFACE_LANDMARKS = [
+    [38.2946, 51.6963],
+    [73.5318, 51.5014],
+    [56.0252, 71.7366],
+    [56.1396, 92.2848],
+] as Array<[number, number]>;
+
+const ARCFACE_LANDMARKS_FACE_SIZE = 112;
+
+const ARC_FACE_5_LANDMARKS = [
+    [38.2946, 51.6963],
+    [73.5318, 51.5014],
+    [56.0252, 71.7366],
+    [41.5493, 92.3655],
+    [70.7299, 92.2041],
+] as Array<[number, number]>;
+
+export function getArcfaceAlignment(
+    faceDetection: FaceDetection,
+): FaceAlignment {
+    const landmarkCount = faceDetection.landmarks.length;
+    return getFaceAlignmentUsingSimilarityTransform(
+        faceDetection,
+        normalizeLandmarks(
+            landmarkCount === 5 ? ARC_FACE_5_LANDMARKS : ARCFACE_LANDMARKS,
+            ARCFACE_LANDMARKS_FACE_SIZE,
+        ),
+    );
 }

-export function getFaceAlignmentUsingSimilarityTransform(
+function getFaceAlignmentUsingSimilarityTransform(
    faceDetection: FaceDetection,
    alignedLandmarks: Array<[number, number]>,
    // alignmentMethod: Versioned<FaceAlignmentMethod>
@ -72,175 +77,11 @@ export function getFaceAlignmentUsingSimilarityTransform(
    };
 }

-export function getArcfaceAlignment(
-    faceDetection: FaceDetection,
-): FaceAlignment {
-    const landmarkCount = faceDetection.landmarks.length;
-    return getFaceAlignmentUsingSimilarityTransform(
-        faceDetection,
-        normalizeLandmarks(
-            landmarkCount === 5 ? ARC_FACE_5_LANDMARKS : ARCFACE_LANDMARKS,
-            ARCFACE_LANDMARKS_FACE_SIZE,
-        ),
-    );
-}
-
-export function extractFaceImage(
-    image: tf.Tensor4D,
-    alignment: FaceAlignment,
+function normalizeLandmarks(
+    landmarks: Array<[number, number]>,
    faceSize: number,
-) {
-    const affineMat = new Matrix(alignment.affineMatrix);
-
-    const I = inverse(affineMat);
-
-    return tf.tidy(() => {
-        const projection = tf.tensor2d([
-            [
-                I.get(0, 0),
-                I.get(0, 1),
-                I.get(0, 2),
-                I.get(1, 0),
-                I.get(1, 1),
-                I.get(1, 2),
-                0,
-                0,
-            ],
-        ]);
-        const faceImage = tf.image.transform(
-            image,
-            projection,
-            "bilinear",
-            "constant",
-            0,
-            [faceSize, faceSize],
-        );
-        return faceImage;
-    });
-}
-
-export function tfExtractFaceImages(
-    image: tf.Tensor3D | tf.Tensor4D,
-    alignments: Array<FaceAlignment>,
-    faceSize: number,
-): tf.Tensor4D {
-    return tf.tidy(() => {
-        const tf4dFloat32Image = toTensor4D(image, "float32");
-        const faceImages = new Array<tf.Tensor3D>(alignments.length);
-        for (let i = 0; i < alignments.length; i++) {
-            faceImages[i] = tf.squeeze(
-                extractFaceImage(tf4dFloat32Image, alignments[i], faceSize),
-                [0],
-            );
-        }
-
-        return tf.stack(faceImages) as tf.Tensor4D;
-    });
-}
-
-export function getAlignedFaceBox(alignment: FaceAlignment) {
-    return new Box({
-        x: alignment.center.x - alignment.size / 2,
-        y: alignment.center.y - alignment.size / 2,
-        width: alignment.size,
-        height: alignment.size,
-    }).round();
-}
-
-export function ibExtractFaceImage(
-    image: ImageBitmap,
-    alignment: FaceAlignment,
-    faceSize: number,
-): ImageBitmap {
-    const box = getAlignedFaceBox(alignment);
-    const faceSizeDimentions: Dimensions = {
-        width: faceSize,
-        height: faceSize,
-    };
-    return cropWithRotation(
-        image,
-        box,
-        alignment.rotation,
-        faceSizeDimentions,
-        faceSizeDimentions,
-    );
-}
-
-// Used in MLDebugViewOnly
-export function ibExtractFaceImageUsingTransform(
-    image: ImageBitmap,
-    alignment: FaceAlignment,
-    faceSize: number,
-): ImageBitmap {
-    const scaledMatrix = new Matrix(alignment.affineMatrix)
-        .mul(faceSize)
-        .to2DArray();
-    // log.info("scaledMatrix: ", scaledMatrix);
-    return transform(image, scaledMatrix, faceSize, faceSize);
-}
-
-export function ibExtractFaceImages(
-    image: ImageBitmap,
-    alignments: Array<FaceAlignment>,
-    faceSize: number,
-): Array<ImageBitmap> {
-    return alignments.map((alignment) =>
-        ibExtractFaceImage(image, alignment, faceSize),
-    );
-}
-
-const BLAZEFACE_LEFT_EYE_INDEX = 0;
-const BLAZEFACE_RIGHT_EYE_INDEX = 1;
-// const BLAZEFACE_NOSE_INDEX = 2;
-const BLAZEFACE_MOUTH_INDEX = 3;
-
-export function getRotatedFaceImage(
-    image: tf.Tensor3D | tf.Tensor4D,
-    faceDetection: FaceDetection,
-    padding: number = 1.5,
-): tf.Tensor4D {
-    const paddedBox = enlargeBox(faceDetection.box, padding);
-    // log.info("paddedBox", paddedBox);
-    const landmarkPoints = faceDetection.landmarks;
-
-    return tf.tidy(() => {
-        const tf4dFloat32Image = toTensor4D(image, "float32");
-        let angle = 0;
-        const leftEye = landmarkPoints[BLAZEFACE_LEFT_EYE_INDEX];
-        const rightEye = landmarkPoints[BLAZEFACE_RIGHT_EYE_INDEX];
-        const foreheadCenter = getBoxCenterPt(leftEye, rightEye);
-
-        angle = computeRotation(
-            landmarkPoints[BLAZEFACE_MOUTH_INDEX],
-            foreheadCenter,
-        ); // landmarkPoints[BLAZEFACE_NOSE_INDEX]
-        // angle = computeRotation(leftEye, rightEye);
-        // log.info('angle: ', angle);
-
-        const faceCenter = getBoxCenter(faceDetection.box);
-        // log.info('faceCenter: ', faceCenter);
-        const faceCenterNormalized: [number, number] = [
-            faceCenter.x / tf4dFloat32Image.shape[2],
-            faceCenter.y / tf4dFloat32Image.shape[1],
-        ];
-        // log.info('faceCenterNormalized: ', faceCenterNormalized);
-
-        let rotatedImage = tf4dFloat32Image;
-        if (angle !== 0) {
-            rotatedImage = tf.image.rotateWithOffset(
-                tf4dFloat32Image,
-                angle,
-                0,
-                faceCenterNormalized,
-            );
-        }
-
-        const faceImageTensor = extractFaces(
-            rotatedImage,
-            [paddedBox],
-            paddedBox.width > 224 ? 448 : 224,
-        );
-        return faceImageTensor;
-        // return tf.gather(faceImageTensor, 0);
-    });
+): Array<[number, number]> {
+    return landmarks.map((landmark) =>
+        landmark.map((p) => p / faceSize),
+    ) as Array<[number, number]>;
 }
--- a/web/apps/photos/src/utils/machineLearning/faceCrop.ts
+++ b/web/apps/photos/src/utils/machineLearning/faceCrop.ts
@ -1,23 +1,15 @@
-import log from "@/next/log";
 import { CacheStorageService } from "@ente/shared/storage/cacheStorage";
 import { CACHES } from "@ente/shared/storage/cacheStorage/constants";
-import { getBlobFromCache } from "@ente/shared/storage/cacheStorage/helpers";
-import { compose, Matrix, scale, translate } from "transformation-matrix";
-import { BlobOptions, Dimensions } from "types/image";
+import { BlobOptions } from "types/image";
 import {
-    AlignedFace,
    FaceAlignment,
    FaceCrop,
    FaceCropConfig,
-    FaceDetection,
-    MlFileData,
    StoredFaceCrop,
 } from "types/machineLearning";
 import { cropWithRotation, imageBitmapToBlob } from "utils/image";
 import { enlargeBox } from ".";
 import { Box } from "../../../thirdparty/face-api/classes";
-import { getAlignedFaceBox } from "./faceAlign";
-import { transformBox, transformPoints } from "./transform";

 export function getFaceCrop(
    imageBitmap: ImageBitmap,
@ -38,7 +30,25 @@ export function getFaceCrop(
    };
 }

-export async function storeFaceCropForBlob(
+function getAlignedFaceBox(alignment: FaceAlignment) {
+    return new Box({
+        x: alignment.center.x - alignment.size / 2,
+        y: alignment.center.y - alignment.size / 2,
+        width: alignment.size,
+        height: alignment.size,
+    }).round();
+}
+
+export async function storeFaceCrop(
+    faceId: string,
+    faceCrop: FaceCrop,
+    blobOptions: BlobOptions,
+): Promise<StoredFaceCrop> {
+    const faceCropBlob = await imageBitmapToBlob(faceCrop.image, blobOptions);
+    return storeFaceCropForBlob(faceId, faceCrop.imageBox, faceCropBlob);
+}
+
+async function storeFaceCropForBlob(
    faceId: string,
    imageBox: Box,
    faceCropBlob: Blob,
@ -52,166 +62,3 @@ export async function storeFaceCropForBlob(
        imageBox: imageBox,
    };
 }
-
-export async function storeFaceCrop(
-    faceId: string,
-    faceCrop: FaceCrop,
-    blobOptions: BlobOptions,
-): Promise<StoredFaceCrop> {
-    const faceCropBlob = await imageBitmapToBlob(faceCrop.image, blobOptions);
-    return storeFaceCropForBlob(faceId, faceCrop.imageBox, faceCropBlob);
-}
-
-export async function getFaceCropBlobFromStorage(
-    storedFaceCrop: StoredFaceCrop,
-): Promise<Blob> {
-    return getBlobFromCache(CACHES.FACE_CROPS, storedFaceCrop.imageUrl);
-}
-
-export async function getFaceCropFromStorage(
-    storedFaceCrop: StoredFaceCrop,
-): Promise<FaceCrop> {
-    const faceCropBlob = await getFaceCropBlobFromStorage(storedFaceCrop);
-    const faceCropImage = await createImageBitmap(faceCropBlob);
-
-    return {
-        image: faceCropImage,
-        imageBox: storedFaceCrop.imageBox,
-    };
-}
-
-export async function removeOldFaceCrops(
-    oldMLFileData: MlFileData,
-    newMLFileData: MlFileData,
-) {
-    const newFaceCropUrls =
-        newMLFileData?.faces
-            ?.map((f) => f.crop?.imageUrl)
-            ?.filter((fc) => fc !== null && fc !== undefined) || [];
-
-    const oldFaceCropUrls =
-        oldMLFileData?.faces
-            ?.map((f) => f.crop?.imageUrl)
-            ?.filter((fc) => fc !== null && fc !== undefined) || [];
-
-    const unusedFaceCropUrls = oldFaceCropUrls.filter(
-        (oldUrl) => !newFaceCropUrls.includes(oldUrl),
-    );
-    if (!unusedFaceCropUrls || unusedFaceCropUrls.length < 1) {
-        return;
-    }
-
-    return removeFaceCropUrls(unusedFaceCropUrls);
-}
-
-export async function removeFaceCropUrls(faceCropUrls: Array<string>) {
-    log.info("Removing face crop urls: ", JSON.stringify(faceCropUrls));
-    const faceCropCache = await CacheStorageService.open(CACHES.FACE_CROPS);
-    const urlRemovalPromises = faceCropUrls?.map((url) =>
-        faceCropCache.delete(url),
-    );
-    return urlRemovalPromises && Promise.all(urlRemovalPromises);
-}
-
-export function extractFaceImageFromCrop(
-    faceCrop: FaceCrop,
-    box: Box,
-    rotation: number,
-    faceSize: number,
-): ImageBitmap {
-    const faceCropImage = faceCrop?.image;
-    let imageBox = faceCrop?.imageBox;
-    if (!faceCropImage || !imageBox) {
-        throw Error("Face crop not present");
-    }
-
-    // TODO: Have better serialization to avoid creating new object manually when calling class methods
-    imageBox = new Box(imageBox);
-    const scale = faceCropImage.width / imageBox.width;
-    const transformedBox = box
-        .shift(-imageBox.x, -imageBox.y)
-        .rescale(scale)
-        .round();
-    // log.info({ box, imageBox, faceCropImage, scale, scaledBox, scaledImageBox, shiftedBox });
-
-    const faceSizeDimentions: Dimensions = {
-        width: faceSize,
-        height: faceSize,
-    };
-    const faceImage = cropWithRotation(
-        faceCropImage,
-        transformedBox,
-        rotation,
-        faceSizeDimentions,
-        faceSizeDimentions,
-    );
-
-    return faceImage;
-}
-
-export async function ibExtractFaceImageFromCrop(
-    faceCrop: FaceCrop,
-    alignment: FaceAlignment,
-    faceSize: number,
-): Promise<ImageBitmap> {
-    const box = getAlignedFaceBox(alignment);
-
-    return extractFaceImageFromCrop(
-        faceCrop,
-        box,
-        alignment.rotation,
-        faceSize,
-    );
-}
-
-export async function ibExtractFaceImagesFromCrops(
-    faces: Array<AlignedFace>,
-    faceSize: number,
-): Promise<Array<ImageBitmap>> {
-    const faceImagePromises = faces.map(async (alignedFace) => {
-        const faceCrop = await getFaceCropFromStorage(alignedFace.crop);
-        return ibExtractFaceImageFromCrop(
-            faceCrop,
-            alignedFace.alignment,
-            faceSize,
-        );
-    });
-    return Promise.all(faceImagePromises);
-}
-
-export function transformFace(faceDetection: FaceDetection, transform: Matrix) {
-    return {
-        ...faceDetection,
-
-        box: transformBox(faceDetection.box, transform),
-        landmarks: transformPoints(faceDetection.landmarks, transform),
-    };
-}
-
-export function transformToFaceCropDims(
-    faceCrop: FaceCrop,
-    faceDetection: FaceDetection,
-) {
-    const imageBox = new Box(faceCrop.imageBox);
-
-    const transform = compose(
-        scale(faceCrop.image.width / imageBox.width),
-        translate(-imageBox.x, -imageBox.y),
-    );
-
-    return transformFace(faceDetection, transform);
-}
-
-export function transformToImageDims(
-    faceCrop: FaceCrop,
-    faceDetection: FaceDetection,
-) {
-    const imageBox = new Box(faceCrop.imageBox);
-
-    const transform = compose(
-        translate(imageBox.x, imageBox.y),
-        scale(imageBox.width / faceCrop.image.width),
-    );
-
-    return transformFace(faceDetection, transform);
-}
--- a/web/apps/photos/src/utils/machineLearning/faceDetection.ts
+++ b/web/apps/photos/src/utils/machineLearning/faceDetection.ts
@ -1,108 +0,0 @@
-import { euclidean } from "hdbscan";
-import { FaceDetection } from "types/machineLearning";
-import { getNearestPointIndex, newBox } from ".";
-import { Box, Point } from "../../../thirdparty/face-api/classes";
-import {
-    computeTransformToBox,
-    transformBox,
-    transformPoints,
-} from "./transform";
-
-export function transformPaddedToImage(
-    detection: FaceDetection,
-    faceImage: ImageBitmap,
-    imageBox: Box,
-    paddedBox: Box,
-) {
-    const inBox = newBox(0, 0, faceImage.width, faceImage.height);
-    imageBox.x = paddedBox.x;
-    imageBox.y = paddedBox.y;
-    const transform = computeTransformToBox(inBox, imageBox);
-
-    detection.box = transformBox(detection.box, transform);
-    detection.landmarks = transformPoints(detection.landmarks, transform);
-}
-
-export function getDetectionCenter(detection: FaceDetection) {
-    const center = new Point(0, 0);
-    // TODO: first 4 landmarks is applicable to blazeface only
-    // this needs to consider eyes, nose and mouth landmarks to take center
-    detection.landmarks?.slice(0, 4).forEach((p) => {
-        center.x += p.x;
-        center.y += p.y;
-    });
-
-    return center.div({ x: 4, y: 4 });
-}
-
-/**
- * Finds the nearest face detection from a list of detections to a specified detection.
- *
- * This function calculates the center of each detection and then finds the detection whose center is nearest to the center of the specified detection.
- * If a maximum distance is specified, only detections within that distance are considered.
- *
- * @param toDetection - The face detection to find the nearest detection to.
- * @param fromDetections - An array of face detections to search in.
- * @param maxDistance - The maximum distance between the centers of the two detections for a detection to be considered. If not specified, all detections are considered.
- *
- * @returns The nearest face detection from the list, or `undefined` if no detection is within the maximum distance.
- */
-export function getNearestDetection(
-    toDetection: FaceDetection,
-    fromDetections: Array<FaceDetection>,
-    maxDistance?: number,
-) {
-    const toCenter = getDetectionCenter(toDetection);
-    const centers = fromDetections.map((d) => getDetectionCenter(d));
-    const nearestIndex = getNearestPointIndex(toCenter, centers, maxDistance);
-
-    return nearestIndex >= 0 && fromDetections[nearestIndex];
-}
-
-/**
- * Removes duplicate face detections from an array of detections.
- *
- * This function sorts the detections by their probability in descending order, then iterates over them.
- * For each detection, it calculates the Euclidean distance to all other detections.
- * If the distance is less than or equal to the specified threshold (`withinDistance`), the other detection is considered a duplicate and is removed.
- *
- * @param detections - An array of face detections to remove duplicates from.
- * @param withinDistance - The maximum Euclidean distance between two detections for them to be considered duplicates.
- *
- * @returns An array of face detections with duplicates removed.
- */
-export function removeDuplicateDetections(
-    detections: Array<FaceDetection>,
-    withinDistance: number,
-) {
-    // console.time('removeDuplicates');
-    detections.sort((a, b) => b.probability - a.probability);
-    const isSelected = new Map<number, boolean>();
-    for (let i = 0; i < detections.length; i++) {
-        if (isSelected.get(i) === false) {
-            continue;
-        }
-        isSelected.set(i, true);
-        for (let j = i + 1; j < detections.length; j++) {
-            if (isSelected.get(j) === false) {
-                continue;
-            }
-            const centeri = getDetectionCenter(detections[i]);
-            const centerj = getDetectionCenter(detections[j]);
-            const dist = euclidean(
-                [centeri.x, centeri.y],
-                [centerj.x, centerj.y],
-            );
-            if (dist <= withinDistance) {
-                isSelected.set(j, false);
-            }
-        }
-    }
-
-    const uniques: Array<FaceDetection> = [];
-    for (let i = 0; i < detections.length; i++) {
-        isSelected.get(i) && uniques.push(detections[i]);
-    }
-    // console.timeEnd('removeDuplicates');
-    return uniques;
-}
--- a/web/apps/photos/src/utils/machineLearning/index.ts
+++ b/web/apps/photos/src/utils/machineLearning/index.ts
@ -1,11 +1,7 @@
 import log from "@/next/log";
 import { CACHES } from "@ente/shared/storage/cacheStorage/constants";
 import { cached } from "@ente/shared/storage/cacheStorage/helpers";
-import * as tf from "@tensorflow/tfjs-core";
-import { NormalizedFace } from "blazeface-back";
 import { FILE_TYPE } from "constants/file";
-import { BLAZEFACE_FACE_SIZE } from "constants/mlConfig";
-import { euclidean } from "hdbscan";
 import PQueue from "p-queue";
 import DownloadManager from "services/download";
 import { getLocalFiles } from "services/fileService";
@ -13,152 +9,22 @@ import { decodeLivePhoto } from "services/livePhotoService";
 import { EnteFile } from "types/file";
 import { Dimensions } from "types/image";
 import {
-    AlignedFace,
    DetectedFace,
-    DetectedObject,
    Face,
    FaceAlignment,
-    FaceImageBlob,
    MlFileData,
    Person,
-    RealWorldObject,
    Versioned,
 } from "types/machineLearning";
 import { getRenderableImage } from "utils/file";
-import { clamp, imageBitmapToBlob, warpAffineFloat32List } from "utils/image";
+import { clamp, warpAffineFloat32List } from "utils/image";
 import mlIDbStorage from "utils/storage/mlIDbStorage";
 import { Box, Point } from "../../../thirdparty/face-api/classes";
-import { ibExtractFaceImage, ibExtractFaceImages } from "./faceAlign";
-import { getFaceCropBlobFromStorage } from "./faceCrop";
-
-export function f32Average(descriptors: Float32Array[]) {
-    if (descriptors.length < 1) {
-        throw Error("f32Average: input size 0");
-    }
-
-    if (descriptors.length === 1) {
-        return descriptors[0];
-    }
-
-    const f32Size = descriptors[0].length;
-    const avg = new Float32Array(f32Size);
-
-    for (let index = 0; index < f32Size; index++) {
-        avg[index] = descriptors[0][index];
-        for (let desc = 1; desc < descriptors.length; desc++) {
-            avg[index] = avg[index] + descriptors[desc][index];
-        }
-        avg[index] = avg[index] / descriptors.length;
-    }
-
-    return avg;
-}
-
-export function isTensor(tensor: any, dim: number) {
-    return tensor instanceof tf.Tensor && tensor.shape.length === dim;
-}
-
-export function isTensor1D(tensor: any): tensor is tf.Tensor1D {
-    return isTensor(tensor, 1);
-}
-
-export function isTensor2D(tensor: any): tensor is tf.Tensor2D {
-    return isTensor(tensor, 2);
-}
-
-export function isTensor3D(tensor: any): tensor is tf.Tensor3D {
-    return isTensor(tensor, 3);
-}
-
-export function isTensor4D(tensor: any): tensor is tf.Tensor4D {
-    return isTensor(tensor, 4);
-}
-
-export function toTensor4D(
-    image: tf.Tensor3D | tf.Tensor4D,
-    dtype?: tf.DataType,
-) {
-    return tf.tidy(() => {
-        let reshapedImage: tf.Tensor4D;
-        if (isTensor3D(image)) {
-            reshapedImage = tf.expandDims(image, 0);
-        } else if (isTensor4D(image)) {
-            reshapedImage = image;
-        } else {
-            throw Error("toTensor4D only supports Tensor3D and Tensor4D input");
-        }
-        if (dtype) {
-            reshapedImage = tf.cast(reshapedImage, dtype);
-        }
-
-        return reshapedImage;
-    });
-}
-
-export function imageBitmapsToTensor4D(imageBitmaps: Array<ImageBitmap>) {
-    return tf.tidy(() => {
-        const tfImages = imageBitmaps.map((ib) => tf.browser.fromPixels(ib));
-        return tf.stack(tfImages) as tf.Tensor4D;
-    });
-}
-
-export function extractFaces(
-    image: tf.Tensor3D | tf.Tensor4D,
-    facebBoxes: Array<Box>,
-    faceSize: number,
-) {
-    return tf.tidy(() => {
-        const reshapedImage = toTensor4D(image, "float32");
-
-        const boxes = facebBoxes.map((box) => {
-            const normalized = box.rescale({
-                width: 1 / reshapedImage.shape[2],
-                height: 1 / reshapedImage.shape[1],
-            });
-
-            return [
-                normalized.top,
-                normalized.left,
-                normalized.bottom,
-                normalized.right,
-            ];
-        });
-
-        // log.info('boxes: ', boxes[0]);
-
-        const faceImagesTensor = tf.image.cropAndResize(
-            reshapedImage,
-            boxes,
-            tf.fill([boxes.length], 0, "int32"),
-            [faceSize, faceSize],
-        );
-
-        return faceImagesTensor;
-    });
-}

 export function newBox(x: number, y: number, width: number, height: number) {
    return new Box({ x, y, width, height });
 }

-export function newBoxFromPoints(
-    left: number,
-    top: number,
-    right: number,
-    bottom: number,
-) {
-    return new Box({ left, top, right, bottom });
-}
-
-export function normFaceBox(face: NormalizedFace) {
-    return newBoxFromPoints(
-        face.topLeft[0],
-        face.topLeft[1],
-        face.bottomRight[0],
-        face.bottomRight[1],
-    );
-}
-
 export function getBoxCenterPt(topLeft: Point, bottomRight: Point): Point {
    return topLeft.add(bottomRight.sub(topLeft).div(new Point(2, 2)));
 }
@ -180,74 +46,17 @@ export function enlargeBox(box: Box, factor: number = 1.5) {
    });
 }

-export function normalizeRadians(angle: number) {
-    return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
-}
-
-export function computeRotation(point1: Point, point2: Point) {
-    const radians =
-        Math.PI / 2 - Math.atan2(-(point2.y - point1.y), point2.x - point1.x);
-    return normalizeRadians(radians);
-}
-
 export function getAllFacesFromMap(allFacesMap: Map<number, Array<Face>>) {
    const allFaces = [...allFacesMap.values()].flat();

    return allFaces;
 }

-export function getAllObjectsFromMap(
-    allObjectsMap: Map<number, Array<RealWorldObject>>,
-) {
-    return [...allObjectsMap.values()].flat();
-}
-
 export async function getLocalFile(fileId: number) {
    const localFiles = await getLocalFiles();
    return localFiles.find((f) => f.id === fileId);
 }

-export async function getFaceImage(
-    face: AlignedFace,
-    token: string,
-    faceSize: number = BLAZEFACE_FACE_SIZE,
-    file?: EnteFile,
-): Promise<FaceImageBlob> {
-    if (!file) {
-        file = await getLocalFile(face.fileId);
-    }
-
-    const imageBitmap = await getOriginalImageBitmap(file);
-    const faceImageBitmap = ibExtractFaceImage(
-        imageBitmap,
-        face.alignment,
-        faceSize,
-    );
-    const faceImage = imageBitmapToBlob(faceImageBitmap);
-    faceImageBitmap.close();
-    imageBitmap.close();
-
-    return faceImage;
-}
-
-export async function extractFaceImages(
-    faces: Array<AlignedFace>,
-    faceSize: number,
-    image?: ImageBitmap,
-) {
-    // if (faces.length === faces.filter((f) => f.crop).length) {
-    // return ibExtractFaceImagesFromCrops(faces, faceSize);
-    // } else
-    if (image) {
-        const faceAlignments = faces.map((f) => f.alignment);
-        return ibExtractFaceImages(image, faceAlignments, faceSize);
-    } else {
-        throw Error(
-            "Either face crops or image is required to extract face images",
-        );
-    }
-}
-
 export async function extractFaceImagesToFloat32(
    faceAlignments: Array<FaceAlignment>,
    faceSize: number,
@ -270,10 +79,6 @@ export async function extractFaceImagesToFloat32(
    return faceData;
 }

-export function leftFillNum(num: number, length: number, padding: number) {
-    return num.toString().padStart(length, padding.toString());
-}
-
 export function getFaceId(detectedFace: DetectedFace, imageDims: Dimensions) {
    const xMin = clamp(
        detectedFace.detection.box.x / imageDims.width,
@ -312,45 +117,10 @@ export function getFaceId(detectedFace: DetectedFace, imageDims: Dimensions) {
    return faceID;
 }

-export function getObjectId(
-    detectedObject: DetectedObject,
-    imageDims: Dimensions,
-) {
-    const imgDimPoint = new Point(imageDims.width, imageDims.height);
-    const objectCenterPoint = new Point(
-        detectedObject.detection.bbox[2] / 2,
-        detectedObject.detection.bbox[3] / 2,
-    );
-    const gridPt = objectCenterPoint
-        .mul(new Point(100, 100))
-        .div(imgDimPoint)
-        .floor()
-        .bound(0, 99);
-    const gridPaddedX = leftFillNum(gridPt.x, 2, 0);
-    const gridPaddedY = leftFillNum(gridPt.y, 2, 0);
-
-    return `${detectedObject.fileID}-${gridPaddedX}-${gridPaddedY}`;
-}
-
-export async function getTFImage(blob): Promise<tf.Tensor3D> {
-    const imageBitmap = await createImageBitmap(blob);
-    const tfImage = tf.browser.fromPixels(imageBitmap);
-    imageBitmap.close();
-
-    return tfImage;
-}
-
 export async function getImageBlobBitmap(blob: Blob): Promise<ImageBitmap> {
    return await createImageBitmap(blob);
 }

-// export async function getTFImageUsingJpegJS(blob: Blob): Promise<TFImageBitmap> {
-//     const imageData = jpegjs.decode(await blob.arrayBuffer());
-//     const tfImage = tf.browser.fromPixels(imageData);
-
-//     return new TFImageBitmap(undefined, tfImage);
-// }
-
 async function getOriginalFile(file: EnteFile, queue?: PQueue) {
    let fileStream;
    if (queue) {
@ -453,21 +223,6 @@ export async function getUnidentifiedFaces(
    );
 }

-export async function getFaceCropBlobs(
-    faces: Array<Face>,
-): Promise<Array<FaceImageBlob>> {
-    const faceCrops = faces
-        .map((f) => f.crop)
-        .filter((faceCrop) => faceCrop !== null && faceCrop !== undefined);
-
-    return (
-        faceCrops &&
-        Promise.all(
-            faceCrops.map((faceCrop) => getFaceCropBlobFromStorage(faceCrop)),
-        )
-    );
-}
-
 export async function getAllPeople(limit: number = undefined) {
    let people: Array<Person> = await mlIDbStorage.getAllPeople();
    // await mlPeopleStore.iterate<Person, void>((person) => {
@ -531,27 +286,6 @@ export function areFaceIdsSame(ofFaces: Array<Face>, toFaces: Array<Face>) {
    );
 }

-export function getNearestPointIndex(
-    toPoint: Point,
-    fromPoints: Array<Point>,
-    maxDistance?: number,
-) {
-    const dists = fromPoints.map((point, i) => ({
-        index: i,
-        point: point,
-        distance: euclidean([point.x, point.y], [toPoint.x, toPoint.y]),
-    }));
-    const nearest = findFirstIfSorted(
-        dists,
-        (a, b) => Math.abs(a.distance) - Math.abs(b.distance),
-    );
-
-    // log.info('Nearest dist: ', nearest.distance, maxDistance);
-    if (!maxDistance || nearest.distance <= maxDistance) {
-        return nearest.index;
-    }
-}
-
 export function logQueueStats(queue: PQueue, name: string) {
    queue.on("active", () =>
        log.info(
--- a/web/apps/photos/src/utils/machineLearning/transform.ts
+++ b/web/apps/photos/src/utils/machineLearning/transform.ts
@ -1,33 +0,0 @@
-import { newBoxFromPoints } from ".";
-import { Box, Point } from "../../../thirdparty/face-api/classes";
-
-import {
-    Matrix,
-    applyToPoint,
-    compose,
-    scale,
-    translate,
-} from "transformation-matrix";
-
-export function computeTransformToBox(inBox: Box, toBox: Box): Matrix {
-    return compose(
-        translate(toBox.x, toBox.y),
-        scale(toBox.width / inBox.width, toBox.height / inBox.height),
-    );
-}
-
-export function transformPoint(point: Point, transform: Matrix) {
-    const txdPoint = applyToPoint(transform, point);
-    return new Point(txdPoint.x, txdPoint.y);
-}
-
-export function transformPoints(points: Point[], transform: Matrix) {
-    return points?.map((p) => transformPoint(p, transform));
-}
-
-export function transformBox(box: Box, transform: Matrix) {
-    const topLeft = transformPoint(box.topLeft, transform);
-    const bottomRight = transformPoint(box.bottomRight, transform);
-
-    return newBoxFromPoints(topLeft.x, topLeft.y, bottomRight.x, bottomRight.y);
-}
--- a/web/apps/photos/src/utils/storage/mlIDbStorage.ts
+++ b/web/apps/photos/src/utils/storage/mlIDbStorage.ts
@ -15,14 +15,7 @@ import {
    openDB,
 } from "idb";
 import isElectron from "is-electron";
-import {
-    Face,
-    MLLibraryData,
-    MlFileData,
-    Person,
-    RealWorldObject,
-    Thing,
-} from "types/machineLearning";
+import { Face, MLLibraryData, MlFileData, Person } from "types/machineLearning";
 import { IndexStatus } from "types/machineLearning/ui";

 interface Config {}
@ -42,9 +35,11 @@ interface MLDb extends DBSchema {
        key: number;
        value: Person;
    };
+    // Unused, we only retain this is the schema so that we can delete it during
+    // migration.
    things: {
        key: number;
-        value: Thing;
+        value: unknown;
    };
    versions: {
        key: string;
@ -72,7 +67,7 @@ class MLIDbStorage {
    }

    private openDB(): Promise<IDBPDatabase<MLDb>> {
-        return openDB<MLDb>(MLDATA_DB_NAME, 3, {
+        return openDB<MLDb>(MLDATA_DB_NAME, 4, {
            terminated: async () => {
                log.error("ML Indexed DB terminated");
                this._db = undefined;
@ -128,6 +123,14 @@ class MLIDbStorage {
                        .objectStore("configs")
                        .add(DEFAULT_ML_SEARCH_CONFIG, ML_SEARCH_CONFIG_NAME);
                }
+                if (oldVersion < 4) {
+                    // TODO(MR): This loses the user's settings.
+                    db.deleteObjectStore("configs");
+                    db.createObjectStore("configs");
+
+                    db.deleteObjectStore("things");
+                }
+
                log.info(
                    `Ml DB upgraded to version: ${newVersion} from version: ${oldVersion}`,
                );
@ -299,21 +302,6 @@ class MLIDbStorage {
        log.info("updateFaces", Date.now() - startTime, "ms");
    }

-    public async getAllObjectsMap() {
-        const startTime = Date.now();
-        const db = await this.db;
-        const allFiles = await db.getAll("files");
-        const allObjectsMap = new Map<number, Array<RealWorldObject>>();
-        allFiles.forEach(
-            (mlFileData) =>
-                mlFileData.objects &&
-                allObjectsMap.set(mlFileData.fileId, mlFileData.objects),
-        );
-        log.info("allObjectsMap", Date.now() - startTime, "ms");
-
-        return allObjectsMap;
-    }
-
    public async getPerson(id: number) {
        const db = await this.db;
        return db.get("people", id);
@ -334,20 +322,6 @@ class MLIDbStorage {
        return db.clear("people");
    }

-    public async getAllThings() {
-        const db = await this.db;
-        return db.getAll("things");
-    }
-    public async putThing(thing: Thing) {
-        const db = await this.db;
-        return db.put("things", thing);
-    }
-
-    public async clearAllThings() {
-        const db = await this.db;
-        return db.clear("things");
-    }
-
    public async getIndexVersion(index: string) {
        const db = await this.db;
        return db.get("versions", index);
--- a/web/apps/photos/src/worker/search.worker.ts
+++ b/web/apps/photos/src/worker/search.worker.ts
@ -57,14 +57,6 @@ function isSearchedFile(file: EnteFile, search: Search) {
    if (search?.person) {
        return search.person.files.indexOf(file.id) !== -1;
    }
-
-    if (search?.thing) {
-        return search.thing.files.indexOf(file.id) !== -1;
-    }
-
-    if (search?.text) {
-        return search.text.files.indexOf(file.id) !== -1;
-    }
    if (typeof search?.fileType !== "undefined") {
        return search.fileType === file.metadata.fileType;
    }
--- a/web/apps/photos/thirdparty/face-api/classes/Box.ts
+++ b/web/apps/photos/thirdparty/face-api/classes/Box.ts
@ -1,4 +1,3 @@
-import { isDimensions, isValidNumber } from '../utils';
 import { IBoundingBox } from './BoundingBox';
 import { IDimensions } from './Dimensions';
 import { Point } from './Point';
@ -172,4 +171,12 @@ export class Box<BoxType = any> implements IBoundingBox, IRect {
      bottom: this.bottom + (region.bottom * this.height)
    }).toSquare().round()
  }
-}
+}
+
+export function isValidNumber(num: any) {
+  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0
+}
+
+export function isDimensions(obj: any): boolean {
+    return obj && obj.width && obj.height
+}
--- a/web/apps/photos/thirdparty/face-api/classes/Dimensions.ts
+++ b/web/apps/photos/thirdparty/face-api/classes/Dimensions.ts
@ -1,4 +1,4 @@
-import { isValidNumber } from '../utils';
+import { isValidNumber } from './Box';

 export interface IDimensions {
  width: number
@ -25,4 +25,4 @@ export class Dimensions implements IDimensions {
  public reverse(): Dimensions {
    return new Dimensions(1 / this.width, 1 / this.height)
  }
-}
+}
--- a/web/apps/photos/thirdparty/face-api/utils/index.ts
+++ b/web/apps/photos/thirdparty/face-api/utils/index.ts
@ -1,63 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-
-import { Point } from '../classes';
-import { Dimensions, IDimensions } from '../classes/Dimensions';
-
-export function isTensor(tensor: any, dim: number) {
-  return tensor instanceof tf.Tensor && tensor.shape.length === dim
-}
-
-export function isTensor1D(tensor: any): tensor is tf.Tensor1D {
-  return isTensor(tensor, 1)
-}
-
-export function isTensor2D(tensor: any): tensor is tf.Tensor2D {
-  return isTensor(tensor, 2)
-}
-
-export function isTensor3D(tensor: any): tensor is tf.Tensor3D {
-  return isTensor(tensor, 3)
-}
-
-export function isTensor4D(tensor: any): tensor is tf.Tensor4D {
-  return isTensor(tensor, 4)
-}
-
-export function isFloat(num: number) {
-  return num % 1 !== 0
-}
-
-export function isEven(num: number) {
-  return num % 2 === 0
-}
-
-export function round(num: number, prec: number = 2) {
-  const f = Math.pow(10, prec)
-  return Math.floor(num * f) / f
-}
-
-export function isDimensions(obj: any): boolean {
-  return obj && obj.width && obj.height
-}
-
-export function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {
-  const scale = inputSize / Math.max(height, width)
-  return new Dimensions(Math.round(width * scale), Math.round(height * scale))
-}
-
-export function getCenterPoint(pts: Point[]): Point {
-  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))
-    .div(new Point(pts.length, pts.length))
-}
-
-export function range(num: number, start: number, step: number): number[] {
-  return Array(num).fill(0).map((_, i) => start + (i * step))
-}
-
-export function isValidNumber(num: any) {
-  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0
-}
-
-export function isValidProbablitiy(num: any) {
-  return isValidNumber(num) && 0 <= num && num <= 1.0
-}
--- a/web/packages/next/types/ipc.ts
+++ b/web/packages/next/types/ipc.ts
@ -196,7 +196,7 @@ export interface Electron {
    // - ML

    /**
-     * Compute and return a CLIP embedding of the given image.
+     * Return a CLIP embedding of the given image.
     *
     * See: [Note: CLIP based magic search]
     *
@ -207,7 +207,7 @@ export interface Electron {
    clipImageEmbedding: (jpegImageData: Uint8Array) => Promise<Float32Array>;

    /**
-     * Compute and return a CLIP embedding of the given image.
+     * Return a CLIP embedding of the given image.
     *
     * See: [Note: CLIP based magic search]
     *
@ -217,6 +217,22 @@ export interface Electron {
     */
    clipTextEmbedding: (text: string) => Promise<Float32Array>;

+    /**
+     * Detect faces in the given image using YOLO.
+     *
+     * Both the input and output are opaque binary data whose internal structure
+     * is specific to our implementation and the model (YOLO) we use.
+     */
+    detectFaces: (input: Float32Array) => Promise<Float32Array>;
+
+    /**
+     * Return a MobileFaceNet embedding for the given face data.
+     *
+     * Both the input and output are opaque binary data whose internal structure
+     * is specific to our implementation and the model (MobileFaceNet) we use.
+     */
+    faceEmbedding: (input: Float32Array) => Promise<Float32Array>;
+
    // - File selection
    // TODO: Deprecated - use dialogs on the renderer process itself

--- a/web/packages/next/worker/comlink-worker.ts
+++ b/web/packages/next/worker/comlink-worker.ts
@ -35,6 +35,19 @@ export class ComlinkWorker<T extends new () => InstanceType<T>> {
    }
 }

+// TODO(MR): Temporary method to forward auth tokens to workers
+const getAuthToken = () => {
+    // LS_KEYS.USER
+    const userJSONString = localStorage.getItem("user");
+    if (!userJSONString) return undefined;
+    const json: unknown = JSON.parse(userJSONString);
+    if (!json || typeof json != "object" || !("token" in json))
+        return undefined;
+    const token = json.token;
+    if (typeof token != "string") return undefined;
+    return token;
+};
+
 /**
 * A minimal set of utility functions that we expose to all workers that we
 * create.
@ -44,8 +57,12 @@ export class ComlinkWorker<T extends new () => InstanceType<T>> {
 */
 const workerBridge = {
    logToDisk,
+    getAuthToken,
    convertToJPEG: (inputFileData: Uint8Array, filename: string) =>
        ensureElectron().convertToJPEG(inputFileData, filename),
+    detectFaces: (input: Float32Array) => ensureElectron().detectFaces(input),
+    faceEmbedding: (input: Float32Array) =>
+        ensureElectron().faceEmbedding(input),
 };

 export type WorkerBridge = typeof workerBridge;
--- a/web/yarn.lock
+++ b/web/yarn.lock
@ -954,52 +954,6 @@
  dependencies:
    tslib "^2.4.0"

-"@tensorflow-models/coco-ssd@^2.2.2":
-  version "2.2.3"
-  resolved "https://registry.yarnpkg.com/@tensorflow-models/coco-ssd/-/coco-ssd-2.2.3.tgz#3825286569076d6788199c9cb89fb2fa31f7d2f2"
-  integrity sha512-iCLGktG/XhHbP6h2FWxqCKMp/Px0lCp6MZU1fjNhjDHeaWEC9G7S7cZrnPXsfH+NewCM53YShlrHnknxU3SQig==
-
-"@tensorflow/tfjs-backend-cpu@4.17.0", "@tensorflow/tfjs-backend-cpu@^4.10.0":
-  version "4.17.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-4.17.0.tgz#b0c495de686cf700f2ae1f6d8bc2eb6f1964d250"
-  integrity sha512-2VSCHnX9qhYTjw9HiVwTBSnRVlntKXeBlK7aSVsmZfHGwWE2faErTtO7bWmqNqw0U7gyznJbVAjlow/p+0RNGw==
-  dependencies:
-    "@types/seedrandom" "^2.4.28"
-    seedrandom "^3.0.5"
-
-"@tensorflow/tfjs-backend-webgl@^4.9.0":
-  version "4.17.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-4.17.0.tgz#7d540a92343582d37d2cdf9509060598a19cd17a"
-  integrity sha512-CC5GsGECCd7eYAUaKq0XJ48FjEZdgXZWPxgUYx4djvfUx5fQPp35hCSP9w/k463jllBMbjl2tKRg8u7Ia/LYzg==
-  dependencies:
-    "@tensorflow/tfjs-backend-cpu" "4.17.0"
-    "@types/offscreencanvas" "~2019.3.0"
-    "@types/seedrandom" "^2.4.28"
-    seedrandom "^3.0.5"
-
-"@tensorflow/tfjs-converter@^4.10.0":
-  version "4.17.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-converter/-/tfjs-converter-4.17.0.tgz#f4407bd53d5e300b05ed0b0f068506bc50c956b0"
-  integrity sha512-qFxIjPfomCuTrYxsFjtKbi3QfdmTTCWo+RvqD64oCMS0sjp7sUDNhJyKDoLx6LZhXlwXpHIVDJctLMRMwet0Zw==
-
-"@tensorflow/tfjs-core@^4.10.0":
-  version "4.17.0"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-4.17.0.tgz#1ea128555a4d197aed417d70461fcbc7eaec635f"
-  integrity sha512-v9Q5430EnRpyhWNd9LVgXadciKvxLiq+sTrLKRowh26BHyAsams4tZIgX3lFKjB7b90p+FYifVMcqLTTHgjGpQ==
-  dependencies:
-    "@types/long" "^4.0.1"
-    "@types/offscreencanvas" "~2019.7.0"
-    "@types/seedrandom" "^2.4.28"
-    "@webgpu/types" "0.1.38"
-    long "4.0.0"
-    node-fetch "~2.6.1"
-    seedrandom "^3.0.5"
-
-"@tensorflow/tfjs-tflite@0.0.1-alpha.7":
-  version "0.0.1-alpha.7"
-  resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-tflite/-/tfjs-tflite-0.0.1-alpha.7.tgz#647c088689131fee424b7ae0bb9b7fdc74a61475"
-  integrity sha512-aOmmEC/AHzfc/u1Q6ccY6Kr7CfNwjonqyTGVU1OqlQGDrH2IopcCjNSZdatJIB6J2RxlBs979JilCOUpK1LXng==
-
 "@tokenizer/token@^0.3.0":
  version "0.3.0"
  resolved "https://registry.yarnpkg.com/@tokenizer/token/-/token-0.3.0.tgz#fe98a93fe789247e998c75e74e9c7c63217aa276"
@ -1098,11 +1052,6 @@
  resolved "https://registry.yarnpkg.com/@types/lodash/-/lodash-4.14.202.tgz#f09dbd2fb082d507178b2f2a5c7e74bd72ff98f8"
  integrity sha512-OvlIYQK9tNneDlS0VN54LLd5uiPCBOp7gS5Z0f1mjoJYBrtStzgmJBxONW3U6OZqdtNzZPmn9BS/7WI7BFFcFQ==

-"@types/long@^4.0.1":
-  version "4.0.2"
-  resolved "https://registry.yarnpkg.com/@types/long/-/long-4.0.2.tgz#b74129719fc8d11c01868010082d483b7545591a"
-  integrity sha512-MqTGEo5bj5t157U6fA/BiDynNkn0YknVdh48CMPkTSpFTVmvao5UQmm7uEF6xBEo7qIMAlY/JSleYaE6VOdpaA==
-
 "@types/node@*":
  version "20.11.20"
  resolved "https://registry.yarnpkg.com/@types/node/-/node-20.11.20.tgz#f0a2aee575215149a62784210ad88b3a34843659"
@ -1110,16 +1059,6 @@
  dependencies:
    undici-types "~5.26.4"

-"@types/offscreencanvas@~2019.3.0":
-  version "2019.3.0"
-  resolved "https://registry.yarnpkg.com/@types/offscreencanvas/-/offscreencanvas-2019.3.0.tgz#3336428ec7e9180cf4566dfea5da04eb586a6553"
-  integrity sha512-esIJx9bQg+QYF0ra8GnvfianIY8qWB0GBx54PK5Eps6m+xTj86KLavHv6qDhzKcu5UUOgNfJ2pWaIIV7TRUd9Q==
-
-"@types/offscreencanvas@~2019.7.0":
-  version "2019.7.3"
-  resolved "https://registry.yarnpkg.com/@types/offscreencanvas/-/offscreencanvas-2019.7.3.tgz#90267db13f64d6e9ccb5ae3eac92786a7c77a516"
-  integrity sha512-ieXiYmgSRXUDeOntE1InxjWyvEelZGP63M+cGuquuRLuIKKT1osnkXjxev9B7d1nXSug5vpunx+gNlbVxMlC9A==
-
 "@types/parse-json@^4.0.0":
  version "4.0.2"
  resolved "https://registry.yarnpkg.com/@types/parse-json/-/parse-json-4.0.2.tgz#5950e50960793055845e956c427fc2b0d70c5239"
@ -1195,11 +1134,6 @@
  resolved "https://registry.yarnpkg.com/@types/scheduler/-/scheduler-0.16.8.tgz#ce5ace04cfeabe7ef87c0091e50752e36707deff"
  integrity sha512-WZLiwShhwLRmeV6zH+GkbOFT6Z6VklCItrDioxUnv+u4Ll+8vKeFySoFyK/0ctcRpOmwAicELfmys1sDc/Rw+A==

-"@types/seedrandom@^2.4.28":
-  version "2.4.34"
-  resolved "https://registry.yarnpkg.com/@types/seedrandom/-/seedrandom-2.4.34.tgz#c725cd0fc0442e2d3d0e5913af005686ffb7eb99"
-  integrity sha512-ytDiArvrn/3Xk6/vtylys5tlY6eo7Ane0hvcx++TKo6RxQXuVfW0AF/oeWqAj9dN29SyhtawuXstgmPlwNcv/A==
-
 "@types/semver@^7.5.0":
  version "7.5.7"
  resolved "https://registry.yarnpkg.com/@types/semver/-/semver-7.5.7.tgz#326f5fdda70d13580777bcaa1bc6fa772a5aef0e"
@ -1363,11 +1297,6 @@
    "@types/babel__core" "^7.20.5"
    react-refresh "^0.14.0"

-"@webgpu/types@0.1.38":
-  version "0.1.38"
-  resolved "https://registry.yarnpkg.com/@webgpu/types/-/types-0.1.38.tgz#6fda4b410edc753d3213c648320ebcf319669020"
-  integrity sha512-7LrhVKz2PRh+DD7+S+PVaFd5HxaWQvoMqBbsV9fNJO1pjUs1P8bM2vQVNfk+3URTqbuTI7gkXi0rfsN0IadoBA==
-
 acorn-jsx@^5.3.2:
  version "5.3.2"
  resolved "https://registry.yarnpkg.com/acorn-jsx/-/acorn-jsx-5.3.2.tgz#7ed5bb55908b3b2f1bc55c6af1653bada7f07937"
@ -1630,11 +1559,6 @@ bip39@^3.0.4:
  dependencies:
    "@noble/hashes" "^1.2.0"

-blazeface-back@^0.0.9:
-  version "0.0.9"
-  resolved "https://registry.yarnpkg.com/blazeface-back/-/blazeface-back-0.0.9.tgz#a8a26a0022950eb21136693f2fca3c52315ad2a4"
-  integrity sha512-t0i5V117j074d7d7mlLaRq9n/bYchXcSEgpWVbGGloV68A6Jn22t4SNoEC3t+MOsU8H+eXoDv2/6+JsqActM1g==
-
 brace-expansion@^1.1.7:
  version "1.1.11"
  resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-1.1.11.tgz#3c7fcbf529d87226f3d2f52b966ff5271eb441dd"
@ -3445,11 +3369,6 @@ lodash@^4.17.21:
  resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.21.tgz#679591c564c3bffaae8454cf0b3df370c3d6911c"
  integrity sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==

-long@4.0.0:
-  version "4.0.0"
-  resolved "https://registry.yarnpkg.com/long/-/long-4.0.0.tgz#9a7b71cfb7d361a194ea555241c92f7468d5bf28"
-  integrity sha512-XsP+KhQif4bjX1kbuSiySJFNAehNxgLb6hPRGJ9QsUr8ajHkuXGdrHmFUTUUXhDwVX2R5bY4JNZEwbUiMhV+MA==
-
 loose-envify@^1.1.0, loose-envify@^1.4.0:
  version "1.4.0"
  resolved "https://registry.yarnpkg.com/loose-envify/-/loose-envify-1.4.0.tgz#71ee51fa7be4caec1a63839f7e682d8132d30caf"
@ -3621,13 +3540,6 @@ node-fetch@^2.6.1:
  dependencies:
    whatwg-url "^5.0.0"

-node-fetch@~2.6.1:
-  version "2.6.13"
-  resolved "https://registry.yarnpkg.com/node-fetch/-/node-fetch-2.6.13.tgz#a20acbbec73c2e09f9007de5cda17104122e0010"
-  integrity sha512-StxNAxh15zr77QvvkmveSQ8uCQ4+v5FkvNTj0OESmiHu+VRi/gXArXtkWMElOsOUNLtUEvI4yS+rdtOHZTwlQA==
-  dependencies:
-    whatwg-url "^5.0.0"
-
 node-releases@^2.0.14:
  version "2.0.14"
  resolved "https://registry.yarnpkg.com/node-releases/-/node-releases-2.0.14.tgz#2ffb053bceb8b2be8495ece1ab6ce600c4461b0b"
@ -4251,11 +4163,6 @@ scheduler@^0.23.0:
  dependencies:
    loose-envify "^1.1.0"

-seedrandom@^3.0.5:
-  version "3.0.5"
-  resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-3.0.5.tgz#54edc85c95222525b0c7a6f6b3543d8e0b3aa0a7"
-  integrity sha512-8OwmbklUNzwezjGInmZ+2clQmExQPvomqjL7LFqOYqtmuxRgQYqOD3mHaU+MvZn5FLUeVxVfQjwLZW/n/JFuqg==
-
 semver@^6.3.1:
  version "6.3.1"
  resolved "https://registry.yarnpkg.com/semver/-/semver-6.3.1.tgz#556d2ef8689146e46dcea4bfdd095f3434dffcb4"
				`@ -1 +0,0 @@`
				"use strict";var Module={};var initializedJS=false;function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};function moduleLoaded(){}self.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}tflite_web_api_ModuleFactory(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();if(!initializedJS){Module["___embind_register_native_and_builtin_types"]();initializedJS=true}try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["PThread"].threadExit(result)}}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};