explain early stopping

cleanlab · Jan 22, 2024 · 9f12174 · 9f12174
1 parent 36e2c7b
commit 9f12174
Showing 1 changed file with 30 additions and 28 deletions.
diff --git a/object_detection/detectron2_training-kfold.ipynb b/object_detection/detectron2_training-kfold.ipynb
@@ -56,7 +56,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "!wget -nc \"http://images.cocodataset.org/annotations/annotations_trainval2017.zip\"  && unzip -q -o annotations_trainval2017.zip\n",
@@ -96,7 +98,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import json\n",
@@ -150,8 +154,12 @@
     "pairs = []\n",
     "for fold, (train_indices, test_indices) in enumerate(kf.split(image_ids)):\n",
     "    train_data, test_data = split_data(train_indices, test_indices)\n",
+    "    # Register COCO instances for training and validation. \n",
+    "    # Note: The 'train2017' folder is retained as the base path for images.\n",
     "    train_file = f\"train_coco_{fold}_fold.json\"\n",
     "    test_file = f\"test_coco_{fold}_fold.json\"\n",
+    "    register_coco_instances(train_file, {}, train_file, \"train2017\")\n",
+    "    register_coco_instances(test_file, {}, test_file, \"train2017\")\n",
     "    pairs.append([train_file,test_file])\n",
     "    with open(train_file, 'w') as train_file:\n",
     "        json.dump(train_data, train_file)\n",
@@ -161,20 +169,7 @@
     "    print_data_info(train_data, fold)\n",
     "    print(f\"Data info for test data fold {fold}:\")\n",
     "    print_data_info(test_data, fold)\n",
-    "    # Register COCO instances for training and validation. \n",
-    "    # Note: The 'train2017' folder is retained as the base path for images.\n",
-    "    register_coco_instances(train_file, {}, train_file, \"train2017\")\n",
-    "    register_coco_instances(test_file, {}, test_file, \"train2017\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
+    "    \n",
     "TRAIN_PATH = os.path.join(os.getcwd(),\"train2017\")"
    ]
   },
@@ -194,14 +189,15 @@
     "The number of worker threads is set to 2 and the batch size is set to 2.\n",
     "The learning rate and maximum number of iterations are also specified. The model is initialized from the COCO-Detection model zoo and the output directory for the trained model is created. Finally, the configuration is passed to the DefaultTrainer class for training the object detection model.\n",
     "\n",
-    "<strong>Note:</strong> he choice of the number of iterations is informed by the incorporation of [early stopping.](https://en.wikipedia.org/wiki/Early_stopping#:~:text=In%20machine%20learning%2C%20early%20stopping,training%20data%20with%20each%20iteration.) This technique monitors the validation loss throughout training, saving the model upon improvement and halting training if no progress is observed within a defined patience period. Early stopping aims to identify an optimal model iteration, mitigating the risk of overfitting."
+    "<strong>Note:</strong> The choice of the number of iterations is informed by the incorporation of [early stopping.](https://en.wikipedia.org/wiki/Early_stopping#:~:text=In%20machine%20learning%2C%20early%20stopping,training%20data%20with%20each%20iteration.) This technique monitors the validation loss throughout training, saving the model upon improvement and halting training if no progress is observed within a defined patience period. Early stopping aims to identify an optimal model iteration, mitigating the risk of overfitting."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -232,7 +228,6 @@
     "                self._trainer.has_finished = True\n",
     "\n",
     "    def validation(self):\n",
-    "        # val_loader = self.build_test_loader(cfg=self.cfg, dataset_name=self.cfg.DATASETS.TEST[0])\n",
     "        val_loader = build_detection_test_loader(self.cfg, self.cfg.DATASETS.TEST[0], evaluators=[evaluator])\n",
     "        evaluator = COCOEvaluator(self.cfg.DATASETS.TEST[0], self.cfg, True, output_dir=\"./output/\")\n",
     "        val_results = self._trainer.test(self.cfg, self.model, evaluators=[evaluator])[0]\n",
@@ -256,7 +251,7 @@
     "    \n",
     "    \n",
     "    cfg.SOLVER.IMS_PER_BATCH = 2  # This is the real \"batch size\" commonly known to deep learning people\n",
-    "    cfg.SOLVER.BASE_LR = 0.0004  # pick a good LR\n",
+    "    cfg.SOLVER.BASE_LR = 0.004  # pick a good LR\n",
     "    cfg.SOLVER.STEPS = []        # milestones where LR is reduced, in this case there's no decay\n",
     "    cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128   # The \"RoIHead batch size\". \n",
     "    cfg.MODEL.ROI_HEADS.NUM_CLASSES = 80  \n",
@@ -285,7 +280,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def format_detectron2_predictions(instances, num_classes):\n",
@@ -315,7 +312,7 @@
     "    formatted_results = []\n",
     "    for i in results:\n",
     "        if len(i) == 0:\n",
-    "            formatted_array = np.array(i, dtype=np.float32).reshape((0, num_classes))\n",
+    "            formatted_array = np.array(i, dtype=np.float32).reshape((0, 5))\n",
     "        else:\n",
     "            formatted_array = np.array(i, dtype=np.float32)\n",
     "        formatted_results.append(formatted_array)\n",
@@ -327,7 +324,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -353,7 +351,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "result_dict = {}\n",
@@ -366,7 +366,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "dataset = pickle.load(open(\"TRAIN_COCO_ALL_labels.pkl\",'rb'))\n",
@@ -380,9 +382,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "mmdet3d",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "mmdet3d"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -394,7 +396,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.17"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,