{ "cells": [ { "cell_type": "markdown", "id": "f326c1b6", "metadata": { "editable": true, "id": "QYuALZOG-AMq", "papermill": { "duration": 0.00657, "end_time": "2026-03-07T03:09:48.150016", "exception": false, "start_time": "2026-03-07T03:09:48.143446", "status": "completed" }, "tags": [] }, "source": [ "## Assignment: Image recognition\n", "- Alumno 1: LAMY Léo\n", "- Alumno 2: LACLAIS Melen\n", "- Alumno 3: Adrián García-Pozuelo Fornieles\n", "\n", "The goals of the assignment are:\n", "* Develop proficiency in using Tensorflow/Keras for training Neural Nets (NNs).\n", "* Put into practice the acquired knowledge to optimize the parameters and architecture of a feedforward Neural Net (ffNN), in the context of an image recognition problem.\n", "* Put into practice NNs specially conceived for analysing images. Design and optimize the parameters of a Convolutional Neural Net (CNN) to deal with previous task.\n", "* Train popular architectures from scratch (e.g., GoogLeNet, VGG, ResNet, ...), and compare the results with the ones provided by their pre-trained versions using transfer learning.\n", "\n", "Follow the link below to download the classification data set “xview_recognition”: [https://drive.upm.es/s/2DDPE2zHw5dbM3G](https://drive.upm.es/s/2DDPE2zHw5dbM3G)" ] }, { "cell_type": "code", "execution_count": 1, "id": "c4a5b003", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T03:09:48.159805Z", "iopub.status.busy": "2026-03-07T03:09:48.159527Z", "iopub.status.idle": "2026-03-07T03:11:11.287829Z", "shell.execute_reply": "2026-03-07T03:11:11.286850Z" }, "id": "6U41pnVrpwbd", "outputId": "74d8e77e-04bb-4b69-f6a8-1fb509306bcf", "papermill": { "duration": 83.138704, "end_time": "2026-03-07T03:11:11.293094", "exception": false, "start_time": "2026-03-07T03:09:48.154390", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SUCCÈS : Fichier trouvé à : ./xview_recognition/xview_ann_train.json\n", "Base de données chargée avec succès !\n" ] } ], "source": [ "import os\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", "import requests\n", "import zipfile\n", "\n", "url = 'https://drive.upm.es/s/2DDPE2zHw5dbM3G/download'\n", "zip_name = 'dataset.zip'\n", "\n", "r = requests.get(url, stream=True)\n", "with open(zip_name, 'wb') as f:\n", " for chunk in r.iter_content(chunk_size=1024):\n", " f.write(chunk)\n", "\n", "if os.path.getsize(zip_name) < 10000:\n", " print(f\"ERREUR : Le fichier {zip_name} est trop petit. Le lien est invalide ou nécessite une connexion.\")\n", "else:\n", " with zipfile.ZipFile(zip_name, 'r') as z:\n", " z.extractall(\".\")\n", "\n", " target_file = 'xview_ann_train.json'\n", " found_path = None\n", "\n", " for root, dirs, files in os.walk(\".\"):\n", " if target_file in files:\n", " found_path = os.path.join(root, target_file)\n", " break\n", "\n", " if found_path:\n", " print(f\"SUCCÈS : Fichier trouvé à : {found_path}\")\n", "\n", " import json\n", " json_file = found_path\n", "\n", " with open(json_file) as ifs:\n", " json_data = json.load(ifs)\n", " print(\"Base de données chargée avec succès !\")\n", "\n", " else:\n", " print(f\"ERREUR : {target_file} reste introuvable après extraction.\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "979761d0", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:21.031186Z", "start_time": "2024-10-26T00:00:17.131476Z" }, "editable": true, "execution": { "iopub.execute_input": "2026-03-07T03:11:11.302744Z", "iopub.status.busy": "2026-03-07T03:11:11.302467Z", "iopub.status.idle": "2026-03-07T03:11:39.207959Z", "shell.execute_reply": "2026-03-07T03:11:39.207171Z" }, "id": "kIvtyEfkpwbf", "outputId": "5e3b9375-72a4-4d32-a29a-5c330cd0a40f", "papermill": { "duration": 27.912202, "end_time": "2026-03-07T03:11:39.209590", "exception": false, "start_time": "2026-03-07T03:11:11.297388", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-03-07 03:11:12.946730: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1772853073.138019 24 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1772853073.195927 24 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "W0000 00:00:1772853073.664521 24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1772853073.664567 24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1772853073.664570 24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1772853073.664572 24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "GPU activé : 1 processeur(s) détecté(s)\n" ] } ], "source": [ "import tensorflow as tf\n", "\n", "# Vérifie la présence de processeurs graphiques\n", "gpus = tf.config.list_physical_devices('GPU')\n", "\n", "if gpus:\n", " try:\n", " # Configuration pour ne pas allouer toute la mémoire d'un coup\n", " for gpu in gpus:\n", " tf.config.experimental.set_memory_growth(gpu, True)\n", " print(f\"GPU activé : {len(gpus)} processeur(s) détecté(s)\")\n", " except RuntimeError as e:\n", " print(e)\n", "else:\n", " print(\"GPU non détecté. Activez l'accélérateur dans les réglages du notebook.\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "fb05dbba", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:21.066937Z", "start_time": "2024-10-26T00:00:21.059126Z" }, "editable": true, "execution": { "iopub.execute_input": "2026-03-07T03:11:39.223026Z", "iopub.status.busy": "2026-03-07T03:11:39.222098Z", "iopub.status.idle": "2026-03-07T03:11:39.228515Z", "shell.execute_reply": "2026-03-07T03:11:39.227791Z" }, "id": "OYtqD3Oh-AMw", "papermill": { "duration": 0.015044, "end_time": "2026-03-07T03:11:39.230111", "exception": false, "start_time": "2026-03-07T03:11:39.215067", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import uuid\n", "import numpy as np\n", "\n", "class GenericObject:\n", " \"\"\"\n", " Generic object data.\n", " \"\"\"\n", " def __init__(self):\n", " self.id = uuid.uuid4()\n", " self.bb = (-1, -1, -1, -1)\n", " self.category= -1\n", " self.score = -1\n", "\n", "class GenericImage:\n", " \"\"\"\n", " Generic image data.\n", " \"\"\"\n", " def __init__(self, filename):\n", " self.filename = filename\n", " self.tile = np.array([-1, -1, -1, -1]) # (pt_x, pt_y, pt_x+width, pt_y+height)\n", " self.objects = list([])\n", "\n", " def add_object(self, obj: GenericObject):\n", " self.objects.append(obj)" ] }, { "cell_type": "code", "execution_count": 4, "id": "dbdd8d56", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:21.153693Z", "start_time": "2024-10-26T00:00:21.149079Z" }, "execution": { "iopub.execute_input": "2026-03-07T03:11:39.240531Z", "iopub.status.busy": "2026-03-07T03:11:39.239935Z", "iopub.status.idle": "2026-03-07T03:11:39.243998Z", "shell.execute_reply": "2026-03-07T03:11:39.243339Z" }, "id": "I_GygShu-AMz", "papermill": { "duration": 0.010864, "end_time": "2026-03-07T03:11:39.245463", "exception": false, "start_time": "2026-03-07T03:11:39.234599", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "categories = {0: 'Cargo plane', 1: 'Small car', 2: 'Bus', 3: 'Truck', 4: 'Motorboat', 5: 'Fishing vessel', 6: 'Dump truck', 7: 'Excavator', 8: 'Building', 9: 'Helipad', 10: 'Storage tank', 11: 'Shipping container', 12: 'Pylon'}" ] }, { "cell_type": "code", "execution_count": 5, "id": "6ab473c7", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T03:11:39.255122Z", "iopub.status.busy": "2026-03-07T03:11:39.254792Z", "iopub.status.idle": "2026-03-07T03:11:42.763289Z", "shell.execute_reply": "2026-03-07T03:11:42.762276Z" }, "id": "LGYyCbE9pwbg", "outputId": "a8b4a6a2-4400-4f82-ceee-4569b28bf84e", "papermill": { "duration": 3.51539, "end_time": "2026-03-07T03:11:42.765245", "exception": false, "start_time": "2026-03-07T03:11:39.249855", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: rasterio in /usr/local/lib/python3.12/dist-packages (1.5.0)\r\n", "Requirement already satisfied: affine in /usr/local/lib/python3.12/dist-packages (from rasterio) (2.4.0)\r\n", "Requirement already satisfied: attrs in /usr/local/lib/python3.12/dist-packages (from rasterio) (25.4.0)\r\n", "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from rasterio) (2026.1.4)\r\n", "Requirement already satisfied: click!=8.2.*,>=4.0 in /usr/local/lib/python3.12/dist-packages (from rasterio) (8.3.1)\r\n", "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.12/dist-packages (from rasterio) (0.7.2)\r\n", "Requirement already satisfied: numpy>=2 in /usr/local/lib/python3.12/dist-packages (from rasterio) (2.0.2)\r\n", "Requirement already satisfied: pyparsing in /usr/local/lib/python3.12/dist-packages (from rasterio) (3.3.1)\r\n" ] } ], "source": [ "!pip install rasterio" ] }, { "cell_type": "code", "execution_count": 6, "id": "b08b9d9d", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:21.292654Z", "start_time": "2024-10-26T00:00:21.205321Z" }, "editable": true, "execution": { "iopub.execute_input": "2026-03-07T03:11:42.775562Z", "iopub.status.busy": "2026-03-07T03:11:42.775267Z", "iopub.status.idle": "2026-03-07T03:11:43.128609Z", "shell.execute_reply": "2026-03-07T03:11:43.128007Z" }, "id": "fRBA7ReQ-AM0", "papermill": { "duration": 0.360476, "end_time": "2026-03-07T03:11:43.130246", "exception": false, "start_time": "2026-03-07T03:11:42.769770", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import warnings\n", "import rasterio\n", "import numpy as np\n", "\n", "def load_geoimage(filename):\n", " warnings.filterwarnings('ignore', category=rasterio.errors.NotGeoreferencedWarning)\n", " src_raster = rasterio.open('./xview_recognition/'+filename, 'r')\n", " # RasterIO to OpenCV (see inconsistencies between libjpeg and libjpeg-turbo)\n", " input_type = src_raster.profile['dtype']\n", " input_channels = src_raster.count\n", " img = np.zeros((src_raster.height, src_raster.width, src_raster.count), dtype=input_type)\n", " for band in range(input_channels):\n", " img[:, :, band] = src_raster.read(band+1)\n", " return img" ] }, { "cell_type": "markdown", "id": "c8fff25d", "metadata": { "id": "diNBB3qy-AM2", "papermill": { "duration": 0.004293, "end_time": "2026-03-07T03:11:43.139070", "exception": false, "start_time": "2026-03-07T03:11:43.134777", "status": "completed" }, "tags": [] }, "source": [ "#### Training\n", "Design and train a ffNN to deal with the “xview_recognition” classification task." ] }, { "cell_type": "code", "execution_count": 7, "id": "3807f893", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:21.416449Z", "start_time": "2024-10-26T00:00:21.311510Z" }, "editable": true, "execution": { "iopub.execute_input": "2026-03-07T03:11:43.150215Z", "iopub.status.busy": "2026-03-07T03:11:43.148931Z", "iopub.status.idle": "2026-03-07T03:11:43.226835Z", "shell.execute_reply": "2026-03-07T03:11:43.225860Z" }, "id": "Orto292C-AM3", "papermill": { "duration": 0.08575, "end_time": "2026-03-07T03:11:43.229038", "exception": false, "start_time": "2026-03-07T03:11:43.143288", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import json\n", "\n", "# Load database\n", "json_file = './xview_recognition/xview_ann_train.json'\n", "with open(json_file) as ifs:\n", " json_data = json.load(ifs)\n", "ifs.close()" ] }, { "cell_type": "code", "execution_count": 8, "id": "9d210f21", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:22.874518Z", "start_time": "2024-10-26T00:00:22.204948Z" }, "execution": { "iopub.execute_input": "2026-03-07T03:11:43.239181Z", "iopub.status.busy": "2026-03-07T03:11:43.238881Z", "iopub.status.idle": "2026-03-07T03:11:43.648237Z", "shell.execute_reply": "2026-03-07T03:11:43.647456Z" }, "id": "4GjFLHs4-AM4", "outputId": "bbeecd9d-1e7a-4b87-ebe8-c088cab37408", "papermill": { "duration": 0.416249, "end_time": "2026-03-07T03:11:43.649792", "exception": false, "start_time": "2026-03-07T03:11:43.233543", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'Cargo plane': 635, 'Small car': 3324, 'Bus': 1768, 'Truck': 2210, 'Motorboat': 1069, 'Fishing vessel': 706, 'Dump truck': 1236, 'Excavator': 789, 'Building': 3594, 'Helipad': 111, 'Storage tank': 1469, 'Shipping container': 1523, 'Pylon': 312}\n" ] } ], "source": [ "import numpy as np\n", "\n", "counts = dict.fromkeys(categories.values(), 0)\n", "anns = []\n", "for json_img, json_ann in zip(json_data['images'].values(), json_data['annotations'].values()):\n", " image = GenericImage(json_img['filename'])\n", " image.tile = np.array([0, 0, json_img['width'], json_img['height']])\n", " obj = GenericObject()\n", " obj.bb = (int(json_ann['bbox'][0]), int(json_ann['bbox'][1]), int(json_ann['bbox'][2]), int(json_ann['bbox'][3]))\n", " obj.category = json_ann['category_id']\n", " # Resampling strategy to reduce training time\n", " counts[obj.category] += 1\n", " image.add_object(obj)\n", " anns.append(image)\n", "print(counts)\n", "labels = [img.objects[0].category for img in anns]" ] }, { "cell_type": "code", "execution_count": null, "id": "545e0184", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T03:11:43.659726Z", "iopub.status.busy": "2026-03-07T03:11:43.659464Z", "iopub.status.idle": "2026-03-07T03:11:43.662992Z", "shell.execute_reply": "2026-03-07T03:11:43.662303Z" }, "papermill": { "duration": 0.010139, "end_time": "2026-03-07T03:11:43.664428", "exception": false, "start_time": "2026-03-07T03:11:43.654289", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "IMG_SIZE = 224 \n", "n_models = 3" ] }, { "cell_type": "code", "execution_count": 10, "id": "e45bc612", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:23.656800Z", "start_time": "2024-10-26T00:00:23.123245Z" }, "execution": { "iopub.execute_input": "2026-03-07T03:11:43.674535Z", "iopub.status.busy": "2026-03-07T03:11:43.673902Z", "iopub.status.idle": "2026-03-07T03:11:43.804666Z", "shell.execute_reply": "2026-03-07T03:11:43.803888Z" }, "id": "NriAECvS-AM6", "outputId": "09d29d13-d37a-4d6c-f907-6b9d992ef2c1", "papermill": { "duration": 0.137227, "end_time": "2026-03-07T03:11:43.806035", "exception": false, "start_time": "2026-03-07T03:11:43.668808", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of training images: 15934\n", "Number of validation images: 2812\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "anns_train, anns_valid = train_test_split(anns, test_size=0.15, random_state=42, shuffle=True, stratify=labels)\n", "print('Number of training images: ' + str(len(anns_train)))\n", "print('Number of validation images: ' + str(len(anns_valid)))" ] }, { "cell_type": "code", "execution_count": null, "id": "df30c03b", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:25.056806Z", "start_time": "2024-10-26T00:00:24.261581Z" }, "execution": { "iopub.execute_input": "2026-03-07T03:11:43.816679Z", "iopub.status.busy": "2026-03-07T03:11:43.815826Z", "iopub.status.idle": "2026-03-07T03:11:43.829515Z", "shell.execute_reply": "2026-03-07T03:11:43.828972Z" }, "id": "BNkjbY2e-AM7", "outputId": "0e267b07-0457-4158-b89a-ebb6d741f5c8", "papermill": { "duration": 0.020256, "end_time": "2026-03-07T03:11:43.830811", "exception": false, "start_time": "2026-03-07T03:11:43.810555", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.layers import (\n", " Input, Dense, Dropout, Rescaling,\n", " GlobalAveragePooling2D, SpatialDropout2D\n", ")\n", "from tensorflow.keras.models import Model\n", "from tensorflow.keras.optimizers import Nadam\n", "from tensorflow.keras.losses import CategoricalCrossentropy\n", "\n", "BACKBONES = {\n", " 'EfficientNetB0': tf.keras.applications.EfficientNetB0,\n", " 'MobileNetV2': tf.keras.applications.MobileNetV2,\n", " 'ResNet50V2': tf.keras.applications.ResNet50V2,\n", "}\n", "\n", "def create_transfer_model(backbone_name='EfficientNetB0',\n", " input_shape=(IMG_SIZE, IMG_SIZE, 3),\n", " num_classes=13,\n", " freeze_last_only=True,\n", " label_smoothing=0.1,\n", " lr=1e-3):\n", "\n", " BackboneClass = BACKBONES[backbone_name]\n", " img_input = Input(shape=input_shape, name='img_input')\n", "\n", " x = Rescaling(255.0)(img_input)\n", "\n", " backbone = BackboneClass(\n", " include_top=False, weights='imagenet',\n", " input_shape=input_shape, pooling=None\n", " )\n", "\n", " if freeze_last_only:\n", " backbone.trainable = False\n", " backbone.layers[-1].trainable = True \n", " else:\n", " backbone.trainable = False \n", " x = backbone(x, training=False)\n", " x = SpatialDropout2D(0.3)(x)\n", " x = GlobalAveragePooling2D()(x)\n", " x = Dense(1024, activation='relu')(x)\n", " x = Dropout(0.4)(x)\n", " outputs = Dense(num_classes, activation='softmax')(x)\n", "\n", " model = Model(inputs=img_input, outputs=outputs)\n", " loss = CategoricalCrossentropy(label_smoothing=label_smoothing)\n", " model.compile(optimizer=Nadam(lr), loss=loss, metrics=['accuracy'])\n", " return model" ] }, { "cell_type": "code", "execution_count": 12, "id": "df5beba8", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:25.467525Z", "start_time": "2024-10-26T00:00:25.434068Z" }, "execution": { "iopub.execute_input": "2026-03-07T03:11:43.840481Z", "iopub.status.busy": "2026-03-07T03:11:43.840256Z", "iopub.status.idle": "2026-03-07T03:11:43.843584Z", "shell.execute_reply": "2026-03-07T03:11:43.842883Z" }, "id": "-aSlKtG6-AM7", "papermill": { "duration": 0.009809, "end_time": "2026-03-07T03:11:43.844930", "exception": false, "start_time": "2026-03-07T03:11:43.835121", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#from tensorflow.keras.optimizers import Adam\n", "#\n", "## Learning rate is changed to 0.001\n", "#opt = Adam(learning_rate=lr_schedule, beta_1=0.9, beta_2=0.999, epsilon=1e-8, amsgrad=True, clipnorm=1.0)\n", "#model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 13, "id": "4594dda1", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:26.254555Z", "start_time": "2024-10-26T00:00:26.243908Z" }, "execution": { "iopub.execute_input": "2026-03-07T03:11:43.855087Z", "iopub.status.busy": "2026-03-07T03:11:43.854469Z", "iopub.status.idle": "2026-03-07T03:11:43.860262Z", "shell.execute_reply": "2026-03-07T03:11:43.859797Z" }, "id": "GGAJEfpB-AM8", "papermill": { "duration": 0.012158, "end_time": "2026-03-07T03:11:43.861542", "exception": false, "start_time": "2026-03-07T03:11:43.849384", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from tensorflow.keras.callbacks import TerminateOnNaN, EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n", "\n", "# Callbacks\n", "model_checkpoint = ModelCheckpoint('model.keras', monitor='val_accuracy', verbose=1, save_best_only=True)\n", "#reduce_lr = ReduceLROnPlateau('val_accuracy', factor=0.1, patience=10, verbose=1)\n", "early_stop = EarlyStopping('val_accuracy', patience=10, verbose=1)\n", "terminate = TerminateOnNaN()\n", "callbacks = [model_checkpoint, early_stop, terminate]" ] }, { "cell_type": "code", "execution_count": 14, "id": "54d86715", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T03:11:43.871406Z", "iopub.status.busy": "2026-03-07T03:11:43.871167Z", "iopub.status.idle": "2026-03-07T03:11:43.879866Z", "shell.execute_reply": "2026-03-07T03:11:43.879316Z" }, "id": "rn9VGYDGpwbi", "papermill": { "duration": 0.015328, "end_time": "2026-03-07T03:11:43.881175", "exception": false, "start_time": "2026-03-07T03:11:43.865847", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "\n", "def mixup(images, labels, alpha=0.2):\n", " \"\"\"MixUp data augmentation sur un batch numpy.\"\"\"\n", " n = len(images)\n", " lam = np.random.beta(alpha, alpha, size=n).astype(np.float32)\n", " lam = np.maximum(lam, 1 - lam) # lam toujours ≥ 0.5\n", " perm = np.random.permutation(n)\n", " mixed_x = (lam[:, None, None, None] * images\n", " + (1 - lam[:, None, None, None]) * images[perm])\n", " mixed_y = lam[:, None] * labels + (1 - lam[:, None]) * labels[perm]\n", " return mixed_x.astype(np.float32), mixed_y.astype(np.float32)\n", "\n", "\n", "def generator_images(objs, batch_size, do_shuffle=False,\n", " do_mixup=False, class_weights=None):\n", " \"\"\"\n", " Génère des batches (images [0,1], labels one-hot).\n", " - do_mixup : MixUp en entraînement\n", " - class_weights : conservé pour compatibilité API (non utilisé,\n", " label smoothing + MixUp couvrent le déséquilibre)\n", " \"\"\"\n", " num_classes = len(categories)\n", " cat_list = list(categories.values())\n", "\n", " while True:\n", " if do_shuffle:\n", " np.random.shuffle(objs)\n", "\n", " for start in range(0, len(objs), batch_size):\n", " group = objs[start : start + batch_size]\n", " images, labels = [], []\n", "\n", " for filename, obj in group:\n", " img = load_geoimage(filename)\n", " t = tf.image.convert_image_dtype(\n", " tf.convert_to_tensor(img), tf.float32)\n", " t = tf.image.resize(t, [IMG_SIZE, IMG_SIZE], method='bilinear')\n", "\n", " # Augmentation légère (active train + eval, sans effet néfaste)\n", " t = tf.image.random_flip_left_right(t)\n", " t = tf.image.random_flip_up_down(t)\n", " t = tf.image.random_brightness(t, max_delta=0.1)\n", " t = tf.clip_by_value(t, 0.0, 1.0)\n", "\n", " images.append(t.numpy())\n", " idx = cat_list.index(obj.category)\n", " one_hot = tf.keras.utils.to_categorical(idx, num_classes)\n", " labels.append(one_hot)\n", "\n", " images = np.array(images, dtype=np.float32)\n", " labels = np.array(labels, dtype=np.float32)\n", "\n", " if do_mixup and len(images) > 1:\n", " images, labels = mixup(images, labels, alpha=0.2)\n", "\n", " yield images, labels" ] }, { "cell_type": "code", "execution_count": 15, "id": "212bc76c", "metadata": { "ExecuteTime": { "end_time": "2024-10-26T00:00:27.058834Z", "start_time": "2024-10-26T00:00:27.022627Z" }, "execution": { "iopub.execute_input": "2026-03-07T03:11:43.891094Z", "iopub.status.busy": "2026-03-07T03:11:43.890597Z", "iopub.status.idle": "2026-03-07T03:11:43.928172Z", "shell.execute_reply": "2026-03-07T03:11:43.927350Z" }, "id": "Yht-QqUH-AM8", "papermill": { "duration": 0.044215, "end_time": "2026-03-07T03:11:43.929642", "exception": false, "start_time": "2026-03-07T03:11:43.885427", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calcul des poids de classes...\n" ] } ], "source": [ "from sklearn.utils.class_weight import compute_class_weight\n", "import numpy as np\n", "\n", "objs_train = [(ann.filename, obj) for ann in anns_train for obj in ann.objects]\n", "objs_valid = [(ann.filename, obj) for ann in anns_valid for obj in ann.objects]\n", "\n", "print('Calcul des poids de classes...')\n", "y_train_indices = [list(categories.values()).index(obj.category) for _, obj in objs_train]\n", "weights = compute_class_weight('balanced', classes=np.unique(y_train_indices), y=y_train_indices)\n", "class_weights = dict(enumerate(weights))\n", "\n", "# Ajustements manuels (exemple)\n", "class_weights[3] *= 1.5 # Truck\n", "class_weights[9] *= 1.5 # Helipad\n", "\n", "batch_size = 128\n", "\n", "train_generator = generator_images(objs_train, batch_size, do_shuffle=True, class_weights=class_weights)\n", "valid_generator = generator_images(objs_valid, batch_size, do_shuffle=False)\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "8c60bcb2", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T03:11:43.940127Z", "iopub.status.busy": "2026-03-07T03:11:43.939564Z", "iopub.status.idle": "2026-03-07T03:36:35.030074Z", "shell.execute_reply": "2026-03-07T03:36:35.029330Z" }, "papermill": { "duration": 1491.107372, "end_time": "2026-03-07T03:36:35.041669", "exception": false, "start_time": "2026-03-07T03:11:43.934297", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "▶ GridSearch — EfficientNetB0|freeze=True\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "I0000 00:00:1772853104.051794 24 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15511 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n", "\u001b[1m16705208/16705208\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1772853152.705937 69 service.cc:152] XLA service 0x7f6ab8005ae0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1772853152.706004 69 service.cc:160] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n", "I0000 00:00:1772853158.518578 69 cuda_dnn.cc:529] Loaded cuDNN version 91002\n", "2026-03-07 03:12:57.334616: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:12:57.532911: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:12:58.214414: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:12:58.428492: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:12:58.869549: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:12:59.084912: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "I0000 00:00:1772853205.520933 69 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "2026-03-07 03:14:15.947775: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:14:16.132583: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:14:16.572132: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:14:16.777409: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:14:17.127312: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:14:17.332401: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " val_accuracy max = 0.6814\n", "\n", "▶ GridSearch — EfficientNetB0|freeze=False\n", " val_accuracy max = 0.6629\n", "\n", "▶ GridSearch — MobileNetV2|freeze=True\n", "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n", "\u001b[1m9406464/9406464\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-03-07 03:21:33.167615: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:21:33.370350: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:22:28.331818: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:22:28.527646: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " val_accuracy max = 0.2176\n", "\n", "▶ GridSearch — MobileNetV2|freeze=False\n", " val_accuracy max = 0.4822\n", "\n", "▶ GridSearch — ResNet50V2|freeze=True\n", "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n", "\u001b[1m94668760/94668760\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 0us/step\n", " val_accuracy max = 0.3698\n", "\n", "▶ GridSearch — ResNet50V2|freeze=False\n", " val_accuracy max = 0.1963\n", "\n", "Meilleure config : backbone=EfficientNetB0, freeze_last_only=True (val_acc=0.6814)\n", "\n", "Toutes les configs :\n", " EfficientNetB0|freeze=True → 0.6814\n", " EfficientNetB0|freeze=False → 0.6629\n", " MobileNetV2|freeze=False → 0.4822\n", " ResNet50V2|freeze=True → 0.3698\n", " MobileNetV2|freeze=True → 0.2176\n", " ResNet50V2|freeze=False → 0.1963\n" ] } ], "source": [ "# ── GridSearch rapide : backbone × stratégie de gel ──────────────────\n", "import math, gc\n", "\n", "GS_EPOCHS = 5 # peu d'époques pour comparer rapidement\n", "GS_FRAC = 0.25 # fraction des données utilisée pendant la recherche\n", "\n", "n_gs_train = max(batch_size * 4, int(len(objs_train) * GS_FRAC))\n", "n_gs_valid = max(batch_size * 2, int(len(objs_valid) * GS_FRAC))\n", "gs_objs_tr = objs_train[:n_gs_train]\n", "gs_objs_va = objs_valid[:n_gs_valid]\n", "\n", "# Grille : 3 backbones × 2 stratégies = 6 configs\n", "grid = [\n", " ('EfficientNetB0', True),\n", " ('EfficientNetB0', False),\n", " ('MobileNetV2', True),\n", " ('MobileNetV2', False),\n", " ('ResNet50V2', True),\n", " ('ResNet50V2', False),\n", "]\n", "\n", "gs_results = {}\n", "for bb_name, freeze_last in grid:\n", " config_key = f\"{bb_name}|freeze={freeze_last}\"\n", " print(f\"\\n▶ GridSearch — {config_key}\")\n", " try:\n", " gs_tr_gen = generator_images(gs_objs_tr, batch_size, do_shuffle=True)\n", " gs_va_gen = generator_images(gs_objs_va, batch_size)\n", " gs_tr_steps = math.ceil(n_gs_train / batch_size)\n", " gs_va_steps = math.ceil(n_gs_valid / batch_size)\n", "\n", " m = create_transfer_model(\n", " backbone_name=bb_name,\n", " freeze_last_only=freeze_last,\n", " label_smoothing=0.1, lr=1e-3\n", " )\n", " hist = m.fit(\n", " gs_tr_gen, steps_per_epoch=gs_tr_steps,\n", " validation_data=gs_va_gen, validation_steps=gs_va_steps,\n", " epochs=GS_EPOCHS, verbose=0\n", " )\n", " best_val = max(hist.history['val_accuracy'])\n", " gs_results[config_key] = best_val\n", " print(f\" val_accuracy max = {best_val:.4f}\")\n", " del m; gc.collect()\n", " except Exception as e:\n", " print(f\" ✗ Echec : {e}\")\n", " gs_results[config_key] = 0.0\n", "\n", "# Sélection de la meilleure config\n", "best_key = max(gs_results, key=gs_results.get)\n", "best_backbone, _freeze_str = best_key.split('|freeze=')\n", "best_freeze_last_only = (_freeze_str == 'True')\n", "\n", "print(f\"\\nMeilleure config : backbone={best_backbone}, \"\n", " f\"freeze_last_only={best_freeze_last_only} \"\n", " f\"(val_acc={gs_results[best_key]:.4f})\")\n", "print(\"\\nToutes les configs :\")\n", "for k, v in sorted(gs_results.items(), key=lambda x: -x[1]):\n", " print(f\" {k:45s} → {v:.4f}\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "e36d8a59", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T03:36:35.062109Z", "iopub.status.busy": "2026-03-07T03:36:35.061792Z", "iopub.status.idle": "2026-03-07T03:36:37.940247Z", "shell.execute_reply": "2026-03-07T03:36:37.939597Z" }, "papermill": { "duration": 2.890625, "end_time": "2026-03-07T03:36:37.941877", "exception": false, "start_time": "2026-03-07T03:36:35.051252", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Création du modèle 1/3 [EfficientNetB0]\n", "Création du modèle 2/3 [EfficientNetB0]\n", "Création du modèle 3/3 [EfficientNetB0]\n" ] } ], "source": [ "train_size = len(objs_train)\n", "\n", "# Générateurs finaux avec MixUp activé pour l'entraînement\n", "train_generator = generator_images(objs_train, batch_size,\n", " do_shuffle=True, do_mixup=True)\n", "valid_generator = generator_images(objs_valid, batch_size,\n", " do_shuffle=False, do_mixup=False)\n", "\n", "models = []\n", "for i in range(n_models):\n", " print(f\"Création du modèle {i+1}/{n_models} [{best_backbone}]\")\n", " model = create_transfer_model(\n", " backbone_name=best_backbone,\n", " input_shape=(IMG_SIZE, IMG_SIZE, 3),\n", " num_classes=len(categories),\n", " freeze_last_only=best_freeze_last_only,\n", " label_smoothing=0.1,\n", " lr=1e-3\n", " )\n", " models.append(model)" ] }, { "cell_type": "code", "execution_count": 18, "id": "9df349d0", "metadata": { "ExecuteTime": { "start_time": "2024-10-26T00:00:27.913670Z" }, "editable": true, "execution": { "iopub.execute_input": "2026-03-07T03:36:37.962637Z", "iopub.status.busy": "2026-03-07T03:36:37.962090Z", "iopub.status.idle": "2026-03-07T07:13:57.662319Z", "shell.execute_reply": "2026-03-07T07:13:57.661646Z" }, "id": "TrfpdECs-AM9", "jupyter": { "is_executing": true }, "outputId": "3c216359-5935-48ff-e63a-a499e72bfdfd", "papermill": { "duration": 13039.712493, "end_time": "2026-03-07T07:13:57.664080", "exception": false, "start_time": "2026-03-07T03:36:37.951587", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Entraînement des modèles\n", "\n", "--- Modèle 1/3 ---\n", "Epoch 1/25\n", "\u001b[1m124/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.5306 - loss: 1.7622" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-03-07 03:40:48.483002: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:40:48.675407: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:40:49.227746: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:40:49.436870: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:40:49.836781: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n", "2026-03-07 03:40:50.045977: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2s/step - accuracy: 0.5313 - loss: 1.7606\n", "Epoch 1: val_accuracy improved from -inf to 0.63371, saving model to model_0.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m314s\u001b[0m 2s/step - accuracy: 0.5320 - loss: 1.7591 - val_accuracy: 0.6337 - val_loss: 1.4496 - learning_rate: 0.0010\n", "Epoch 2/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7127 - loss: 1.3575\n", "Epoch 2: val_accuracy improved from 0.63371 to 0.71657, saving model to model_0.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 1s/step - accuracy: 0.7127 - loss: 1.3576 - val_accuracy: 0.7166 - val_loss: 1.2388 - learning_rate: 0.0010\n", "Epoch 3/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7430 - loss: 1.2915\n", "Epoch 3: val_accuracy improved from 0.71657 to 0.77312, saving model to model_0.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 1s/step - accuracy: 0.7430 - loss: 1.2914 - val_accuracy: 0.7731 - val_loss: 1.1189 - learning_rate: 0.0010\n", "Epoch 4/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7766 - loss: 1.2377\n", "Epoch 4: val_accuracy improved from 0.77312 to 0.77489, saving model to model_0.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 1s/step - accuracy: 0.7765 - loss: 1.2378 - val_accuracy: 0.7749 - val_loss: 1.0814 - learning_rate: 0.0010\n", "Epoch 5/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7862 - loss: 1.2106\n", "Epoch 5: val_accuracy improved from 0.77489 to 0.78734, saving model to model_0.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 1s/step - accuracy: 0.7862 - loss: 1.2106 - val_accuracy: 0.7873 - val_loss: 1.0368 - learning_rate: 0.0010\n", "Epoch 6/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7987 - loss: 1.1861\n", "Epoch 6: val_accuracy improved from 0.78734 to 0.79694, saving model to model_0.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.7987 - loss: 1.1862 - val_accuracy: 0.7969 - val_loss: 1.0368 - learning_rate: 0.0010\n", "Epoch 7/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8075 - loss: 1.1634\n", "Epoch 7: val_accuracy did not improve from 0.79694\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 1s/step - accuracy: 0.8075 - loss: 1.1634 - val_accuracy: 0.7891 - val_loss: 1.0691 - learning_rate: 0.0010\n", "Epoch 8/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8097 - loss: 1.1501\n", "Epoch 8: val_accuracy did not improve from 0.79694\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 1s/step - accuracy: 0.8097 - loss: 1.1501 - val_accuracy: 0.7909 - val_loss: 1.0317 - learning_rate: 0.0010\n", "Epoch 9/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8272 - loss: 1.1118\n", "Epoch 9: val_accuracy did not improve from 0.79694\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.8271 - loss: 1.1119 - val_accuracy: 0.7788 - val_loss: 1.0622 - learning_rate: 0.0010\n", "Epoch 10/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8364 - loss: 1.0960\n", "Epoch 10: val_accuracy did not improve from 0.79694\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 1s/step - accuracy: 0.8363 - loss: 1.0961 - val_accuracy: 0.7834 - val_loss: 1.0629 - learning_rate: 0.0010\n", "Epoch 11/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8436 - loss: 1.0811\n", "Epoch 11: val_accuracy did not improve from 0.79694\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.8435 - loss: 1.0812 - val_accuracy: 0.7752 - val_loss: 1.0926 - learning_rate: 0.0010\n", "Epoch 12/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8475 - loss: 1.0671\n", "Epoch 12: val_accuracy improved from 0.79694 to 0.80121, saving model to model_0.keras\n", "\n", "Epoch 12: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 1s/step - accuracy: 0.8475 - loss: 1.0672 - val_accuracy: 0.8012 - val_loss: 1.0343 - learning_rate: 0.0010\n", "Epoch 13/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8695 - loss: 1.0320\n", "Epoch 13: val_accuracy improved from 0.80121 to 0.82397, saving model to model_0.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.8695 - loss: 1.0319 - val_accuracy: 0.8240 - val_loss: 1.0027 - learning_rate: 5.0000e-04\n", "Epoch 14/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8832 - loss: 0.9921\n", "Epoch 14: val_accuracy did not improve from 0.82397\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m165s\u001b[0m 1s/step - accuracy: 0.8832 - loss: 0.9921 - val_accuracy: 0.8108 - val_loss: 1.0062 - learning_rate: 5.0000e-04\n", "Epoch 15/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8940 - loss: 0.9760\n", "Epoch 15: val_accuracy did not improve from 0.82397\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m165s\u001b[0m 1s/step - accuracy: 0.8940 - loss: 0.9760 - val_accuracy: 0.8172 - val_loss: 1.0042 - learning_rate: 5.0000e-04\n", "Epoch 16/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9045 - loss: 0.9515\n", "Epoch 16: val_accuracy did not improve from 0.82397\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m165s\u001b[0m 1s/step - accuracy: 0.9045 - loss: 0.9516 - val_accuracy: 0.8193 - val_loss: 1.0059 - learning_rate: 5.0000e-04\n", "Epoch 17/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9058 - loss: 0.9515\n", "Epoch 17: val_accuracy did not improve from 0.82397\n", "\n", "Epoch 17: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 1s/step - accuracy: 0.9057 - loss: 0.9515 - val_accuracy: 0.8112 - val_loss: 1.0223 - learning_rate: 5.0000e-04\n", "Epoch 18/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9117 - loss: 0.9426\n", "Epoch 18: val_accuracy improved from 0.82397 to 0.82610, saving model to model_0.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 1s/step - accuracy: 0.9117 - loss: 0.9426 - val_accuracy: 0.8261 - val_loss: 1.0034 - learning_rate: 2.5000e-04\n", "Epoch 19/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9140 - loss: 0.9307\n", "Epoch 19: val_accuracy did not improve from 0.82610\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m165s\u001b[0m 1s/step - accuracy: 0.9140 - loss: 0.9306 - val_accuracy: 0.8211 - val_loss: 1.0071 - learning_rate: 2.5000e-04\n", "Epoch 20/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9273 - loss: 0.9033\n", "Epoch 20: val_accuracy did not improve from 0.82610\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m165s\u001b[0m 1s/step - accuracy: 0.9273 - loss: 0.9033 - val_accuracy: 0.8169 - val_loss: 1.0269 - learning_rate: 2.5000e-04\n", "Epoch 21/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9245 - loss: 0.9154\n", "Epoch 21: val_accuracy improved from 0.82610 to 0.82752, saving model to model_0.keras\n", "\n", "Epoch 21: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m169s\u001b[0m 1s/step - accuracy: 0.9245 - loss: 0.9153 - val_accuracy: 0.8275 - val_loss: 1.0117 - learning_rate: 2.5000e-04\n", "Epoch 22/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9325 - loss: 0.8982\n", "Epoch 22: val_accuracy improved from 0.82752 to 0.83001, saving model to model_0.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.9325 - loss: 0.8982 - val_accuracy: 0.8300 - val_loss: 0.9929 - learning_rate: 1.2500e-04\n", "Epoch 23/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9271 - loss: 0.9003\n", "Epoch 23: val_accuracy did not improve from 0.83001\n", "\u001b[1m125/125\u001b[0m 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accuracy: 0.9351 - loss: 0.8914 - val_accuracy: 0.8233 - val_loss: 1.0221 - learning_rate: 1.2500e-04\n", "Restoring model weights from the end of the best epoch: 22.\n", "\n", "--- Modèle 2/3 ---\n", "Epoch 1/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2s/step - accuracy: 0.5386 - loss: 1.7679\n", "Epoch 1: val_accuracy improved from -inf to 0.55654, saving model to model_1.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m303s\u001b[0m 2s/step - accuracy: 0.5393 - loss: 1.7663 - val_accuracy: 0.5565 - val_loss: 1.5997 - learning_rate: 0.0010\n", "Epoch 2/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7087 - loss: 1.3734\n", "Epoch 2: val_accuracy improved from 0.55654 to 0.72155, saving model to model_1.keras\n", "\u001b[1m125/125\u001b[0m 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- learning_rate: 0.0010\n", "Epoch 11/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8386 - loss: 1.0924\n", "Epoch 11: val_accuracy did not improve from 0.80761\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.8386 - loss: 1.0923 - val_accuracy: 0.7781 - val_loss: 1.1015 - learning_rate: 0.0010\n", "Epoch 12/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8418 - loss: 1.0709\n", "Epoch 12: val_accuracy did not improve from 0.80761\n", "\n", "Epoch 12: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.8418 - loss: 1.0709 - val_accuracy: 0.7959 - val_loss: 1.0680 - learning_rate: 0.0010\n", "Epoch 13/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8617 - loss: 1.0395\n", "Epoch 13: val_accuracy improved from 0.80761 to 0.82219, saving model to model_1.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 1s/step - accuracy: 0.8617 - loss: 1.0394 - val_accuracy: 0.8222 - val_loss: 1.0039 - learning_rate: 5.0000e-04\n", "Epoch 14/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8909 - loss: 0.9904\n", "Epoch 14: val_accuracy did not improve from 0.82219\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 1s/step - accuracy: 0.8909 - loss: 0.9903 - val_accuracy: 0.8190 - val_loss: 1.0060 - learning_rate: 5.0000e-04\n", "Epoch 15/25\n", "\u001b[1m125/125\u001b[0m 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accuracy: 0.9010 - loss: 0.9602\n", "Epoch 17: val_accuracy did not improve from 0.82219\n", "\n", "Epoch 17: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 1s/step - accuracy: 0.9011 - loss: 0.9601 - val_accuracy: 0.8176 - val_loss: 1.0232 - learning_rate: 5.0000e-04\n", "Epoch 18/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9107 - loss: 0.9406\n", "Epoch 18: val_accuracy did not improve from 0.82219\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.9107 - loss: 0.9405 - val_accuracy: 0.8186 - val_loss: 0.9962 - learning_rate: 2.5000e-04\n", "Epoch 19/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9164 - loss: 0.9278\n", "Epoch 19: val_accuracy did not improve from 0.82219\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m169s\u001b[0m 1s/step - accuracy: 0.9164 - loss: 0.9278 - val_accuracy: 0.8208 - val_loss: 1.0168 - learning_rate: 2.5000e-04\n", "Epoch 20/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9213 - loss: 0.9178\n", "Epoch 20: val_accuracy improved from 0.82219 to 0.82539, saving model to model_1.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 1s/step - accuracy: 0.9213 - loss: 0.9178 - val_accuracy: 0.8254 - val_loss: 1.0041 - learning_rate: 2.5000e-04\n", "Epoch 21/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9215 - loss: 0.9125\n", "Epoch 21: val_accuracy did not improve from 0.82539\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 1s/step - accuracy: 0.9216 - loss: 0.9125 - val_accuracy: 0.8197 - val_loss: 1.0173 - learning_rate: 2.5000e-04\n", "Epoch 22/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9256 - loss: 0.9129\n", "Epoch 22: val_accuracy improved from 0.82539 to 0.83073, saving model to model_1.keras\n", "\n", "Epoch 22: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 1s/step - accuracy: 0.9257 - loss: 0.9129 - val_accuracy: 0.8307 - val_loss: 1.0110 - learning_rate: 2.5000e-04\n", "Epoch 23/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9313 - loss: 0.8955\n", "Epoch 23: val_accuracy did not improve from 0.83073\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 1s/step - accuracy: 0.9313 - loss: 0.8955 - val_accuracy: 0.8282 - val_loss: 1.0115 - learning_rate: 1.2500e-04\n", "Epoch 24/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9301 - loss: 0.8912\n", "Epoch 24: val_accuracy did not improve from 0.83073\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m169s\u001b[0m 1s/step - accuracy: 0.9302 - loss: 0.8911 - val_accuracy: 0.8268 - val_loss: 1.0148 - learning_rate: 1.2500e-04\n", "Epoch 25/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9350 - loss: 0.8896\n", "Epoch 25: val_accuracy did not improve from 0.83073\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.9350 - loss: 0.8896 - val_accuracy: 0.8286 - val_loss: 1.0121 - learning_rate: 1.2500e-04\n", "Restoring model weights from the end of the best epoch: 22.\n", "\n", "--- Modèle 3/3 ---\n", "Epoch 1/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2s/step - accuracy: 0.5320 - loss: 1.7604\n", "Epoch 1: val_accuracy improved from -inf to 0.68528, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m303s\u001b[0m 2s/step - accuracy: 0.5328 - loss: 1.7588 - val_accuracy: 0.6853 - val_loss: 1.3242 - learning_rate: 0.0010\n", "Epoch 2/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7041 - loss: 1.3827\n", "Epoch 2: val_accuracy improved from 0.68528 to 0.74858, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m175s\u001b[0m 1s/step - accuracy: 0.7042 - loss: 1.3825 - val_accuracy: 0.7486 - val_loss: 1.1786 - learning_rate: 0.0010\n", "Epoch 3/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7418 - loss: 1.2935\n", "Epoch 3: val_accuracy improved from 0.74858 to 0.77312, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m176s\u001b[0m 1s/step - accuracy: 0.7418 - loss: 1.2935 - val_accuracy: 0.7731 - val_loss: 1.0772 - learning_rate: 0.0010\n", "Epoch 4/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7570 - loss: 1.2583\n", "Epoch 4: val_accuracy improved from 0.77312 to 0.77881, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 1s/step - accuracy: 0.7570 - loss: 1.2582 - val_accuracy: 0.7788 - val_loss: 1.0841 - learning_rate: 0.0010\n", "Epoch 5/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7750 - loss: 1.2263\n", "Epoch 5: val_accuracy improved from 0.77881 to 0.77987, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.7750 - loss: 1.2262 - val_accuracy: 0.7799 - val_loss: 1.0717 - learning_rate: 0.0010\n", "Epoch 6/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7851 - loss: 1.2004\n", "Epoch 6: val_accuracy improved from 0.77987 to 0.79765, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 1s/step - accuracy: 0.7851 - loss: 1.2003 - val_accuracy: 0.7977 - val_loss: 1.0176 - learning_rate: 0.0010\n", "Epoch 7/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.7997 - loss: 1.1670\n", "Epoch 7: val_accuracy did not improve from 0.79765\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m164s\u001b[0m 1s/step - accuracy: 0.7998 - loss: 1.1670 - val_accuracy: 0.7884 - val_loss: 1.0652 - learning_rate: 0.0010\n", "Epoch 8/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8081 - loss: 1.1545\n", "Epoch 8: val_accuracy did not improve from 0.79765\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 1s/step - accuracy: 0.8082 - loss: 1.1544 - val_accuracy: 0.7959 - val_loss: 1.0173 - learning_rate: 0.0010\n", "Epoch 9/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8186 - loss: 1.1295\n", "Epoch 9: val_accuracy improved from 0.79765 to 0.79908, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 1s/step - accuracy: 0.8186 - loss: 1.1295 - val_accuracy: 0.7991 - val_loss: 1.0460 - learning_rate: 0.0010\n", "Epoch 10/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8258 - loss: 1.1122\n", "Epoch 10: val_accuracy did not improve from 0.79908\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 1s/step - accuracy: 0.8258 - loss: 1.1122 - val_accuracy: 0.7969 - val_loss: 1.0338 - learning_rate: 0.0010\n", "Epoch 11/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8358 - loss: 1.0984\n", "Epoch 11: val_accuracy improved from 0.79908 to 0.80263, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 1s/step - accuracy: 0.8358 - loss: 1.0983 - val_accuracy: 0.8026 - val_loss: 1.0191 - learning_rate: 0.0010\n", "Epoch 12/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8431 - loss: 1.0683\n", "Epoch 12: val_accuracy improved from 0.80263 to 0.81046, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.8431 - loss: 1.0683 - val_accuracy: 0.8105 - val_loss: 1.0055 - learning_rate: 0.0010\n", "Epoch 13/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8471 - loss: 1.0744\n", "Epoch 13: val_accuracy did not improve from 0.81046\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m164s\u001b[0m 1s/step - accuracy: 0.8471 - loss: 1.0743 - val_accuracy: 0.7756 - val_loss: 1.0785 - learning_rate: 0.0010\n", "Epoch 14/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8623 - loss: 1.0479\n", "Epoch 14: val_accuracy did not improve from 0.81046\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 1s/step - accuracy: 0.8623 - loss: 1.0479 - val_accuracy: 0.7891 - val_loss: 1.0821 - learning_rate: 0.0010\n", "Epoch 15/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8612 - loss: 1.0403\n", "Epoch 15: val_accuracy did not improve from 0.81046\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.8613 - loss: 1.0403 - val_accuracy: 0.7909 - val_loss: 1.0840 - learning_rate: 0.0010\n", "Epoch 16/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8665 - loss: 1.0254\n", "Epoch 16: val_accuracy improved from 0.81046 to 0.81188, saving model to model_2.keras\n", "\n", "Epoch 16: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 1s/step - accuracy: 0.8666 - loss: 1.0254 - val_accuracy: 0.8119 - val_loss: 1.0296 - learning_rate: 0.0010\n", "Epoch 17/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.8878 - loss: 0.9864\n", "Epoch 17: val_accuracy improved from 0.81188 to 0.81650, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 1s/step - accuracy: 0.8879 - loss: 0.9864 - val_accuracy: 0.8165 - val_loss: 1.0071 - learning_rate: 5.0000e-04\n", "Epoch 18/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9048 - loss: 0.9513\n", "Epoch 18: val_accuracy improved from 0.81650 to 0.82468, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 1s/step - accuracy: 0.9048 - loss: 0.9512 - val_accuracy: 0.8247 - val_loss: 1.0061 - learning_rate: 5.0000e-04\n", "Epoch 19/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9131 - loss: 0.9427\n", "Epoch 19: val_accuracy did not improve from 0.82468\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m165s\u001b[0m 1s/step - accuracy: 0.9131 - loss: 0.9426 - val_accuracy: 0.8044 - val_loss: 1.0376 - learning_rate: 5.0000e-04\n", "Epoch 20/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9149 - loss: 0.9319\n", "Epoch 20: val_accuracy did not improve from 0.82468\n", "\n", "Epoch 20: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 1s/step - accuracy: 0.9149 - loss: 0.9319 - val_accuracy: 0.8144 - val_loss: 1.0357 - learning_rate: 5.0000e-04\n", "Epoch 21/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9209 - loss: 0.9078\n", "Epoch 21: val_accuracy did not improve from 0.82468\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.9209 - loss: 0.9078 - val_accuracy: 0.8236 - val_loss: 1.0208 - learning_rate: 2.5000e-04\n", "Epoch 22/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9245 - loss: 0.9071\n", "Epoch 22: val_accuracy improved from 0.82468 to 0.82752, saving model to model_2.keras\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m170s\u001b[0m 1s/step - accuracy: 0.9245 - loss: 0.9071 - val_accuracy: 0.8275 - val_loss: 1.0112 - learning_rate: 2.5000e-04\n", "Epoch 23/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9307 - loss: 0.8999\n", "Epoch 23: val_accuracy did not improve from 0.82752\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m165s\u001b[0m 1s/step - accuracy: 0.9307 - loss: 0.8999 - val_accuracy: 0.8240 - val_loss: 1.0276 - learning_rate: 2.5000e-04\n", "Epoch 24/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9310 - loss: 0.8958\n", "Epoch 24: val_accuracy did not improve from 0.82752\n", "\n", "Epoch 24: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 1s/step - accuracy: 0.9310 - loss: 0.8958 - val_accuracy: 0.8257 - val_loss: 1.0138 - learning_rate: 2.5000e-04\n", "Epoch 25/25\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.9336 - loss: 0.8952\n", "Epoch 25: val_accuracy did not improve from 0.82752\n", "\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m167s\u001b[0m 1s/step - accuracy: 0.9336 - loss: 0.8951 - val_accuracy: 0.8275 - val_loss: 1.0091 - learning_rate: 1.2500e-04\n", "Restoring model weights from the end of the best epoch: 22.\n" ] } ], "source": [ "import math\n", "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n", "\n", "epochs = 25\n", "train_steps = math.ceil(len(objs_train) / batch_size)\n", "valid_steps = math.ceil(len(objs_valid) / batch_size)\n", "\n", "print('Entraînement des modèles')\n", "for i, model in enumerate(models):\n", " print(f\"\\n--- Modèle {i+1}/{n_models} ---\")\n", "\n", " ckpt = ModelCheckpoint(\n", " f'model_{i}.keras', monitor='val_accuracy',\n", " save_best_only=True, verbose=1\n", " )\n", " early = EarlyStopping(\n", " monitor='val_accuracy', patience=8,\n", " restore_best_weights=True, verbose=1\n", " )\n", " reduce_lr = ReduceLROnPlateau(\n", " monitor='val_loss', factor=0.5,\n", " patience=4, min_lr=1e-6, verbose=1\n", " )\n", "\n", " model.fit(\n", " train_generator,\n", " steps_per_epoch=train_steps,\n", " validation_data=valid_generator,\n", " validation_steps=valid_steps,\n", " epochs=epochs,\n", " callbacks=[ckpt, early, reduce_lr]\n", " )" ] }, { "cell_type": "markdown", "id": "d380c2b5", "metadata": { "editable": true, "id": "8IMMO_mT-AM9", "papermill": { "duration": 0.386195, "end_time": "2026-03-07T07:13:58.441790", "exception": false, "start_time": "2026-03-07T07:13:58.055595", "status": "completed" }, "tags": [] }, "source": [ "#### Validation\n", "Compute validation metrics." ] }, { "cell_type": "code", "execution_count": 19, "id": "e76e7e1e", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T07:13:59.310361Z", "iopub.status.busy": "2026-03-07T07:13:59.310049Z", "iopub.status.idle": "2026-03-07T07:13:59.321318Z", "shell.execute_reply": "2026-03-07T07:13:59.320729Z" }, "id": "HAanJ-V0-AM1", "papermill": { "duration": 0.394705, "end_time": "2026-03-07T07:13:59.322681", "exception": false, "start_time": "2026-03-07T07:13:58.927976", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "\n", "def draw_confusion_matrix(cm, categories):\n", " # Draw confusion matrix\n", " fig = plt.figure(figsize=[6.4*pow(len(categories), 0.5), 4.8*pow(len(categories), 0.5)])\n", " ax = fig.add_subplot(111)\n", " cm = cm.astype('float') / np.maximum(cm.sum(axis=1)[:, np.newaxis], np.finfo(np.float64).eps)\n", " im = ax.imshow(cm, interpolation='nearest', cmap=plt.colormaps['Blues'])\n", " ax.figure.colorbar(im, ax=ax)\n", " ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]), xticklabels=list(categories.values()), yticklabels=list(categories.values()), ylabel='Annotation', xlabel='Prediction')\n", " # Rotate the tick labels and set their alignment\n", " plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n", " # Loop over data dimensions and create text annotations\n", " thresh = cm.max() / 2.0\n", " for i in range(cm.shape[0]):\n", " for j in range(cm.shape[1]):\n", " ax.text(j, i, format(cm[i, j], '.2f'), ha=\"center\", va=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\", fontsize=int(20-pow(len(categories), 0.5)))\n", " fig.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "id": "e10a743f", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T07:14:00.086388Z", "iopub.status.busy": "2026-03-07T07:14:00.085503Z", "iopub.status.idle": "2026-03-07T07:15:07.884420Z", "shell.execute_reply": "2026-03-07T07:15:07.883785Z" }, "id": "pjVniKBGpwbj", "outputId": "2d6f21b4-70ed-45cc-8f3d-484ff8eff8a9", "papermill": { "duration": 68.184387, "end_time": "2026-03-07T07:15:07.886144", "exception": false, "start_time": "2026-03-07T07:13:59.701757", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chargement des données de validation...\n", "Lancement de la prédiction sur 2812 objets...\n", "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 404ms/step\n", "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 400ms/step\n", "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 396ms/step\n" ] } ], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "\n", "y_true, y_pred = [], []\n", "all_images = []\n", "temp_true_labels = []\n", "\n", "print(\"Chargement des données de validation...\")\n", "for ann in anns_valid:\n", " image_raw = load_geoimage(ann.filename)\n", " image_tensor = tf.convert_to_tensor(image_raw)\n", " image_tensor = tf.image.convert_image_dtype(image_tensor, tf.float32)\n", " image_resized = tf.image.resize(image_tensor, [IMG_SIZE, IMG_SIZE], method='bilinear')\n", "\n", " for obj_pred in ann.objects:\n", " all_images.append(image_resized.numpy())\n", " temp_true_labels.append(obj_pred.category)\n", "\n", "if all_images:\n", " X_valid = np.array(all_images)\n", " print(f\"Lancement de la prédiction sur {len(X_valid)} objets...\")\n", "\n", " # Moyenne des prédictions des 3 modèles\n", " all_predictions = np.mean([\n", " model.predict(X_valid, batch_size=128, verbose=1) for model in models\n", " ], axis=0)\n", "\n", " category_names = list(categories.values())\n", " for i in range(len(all_predictions)):\n", " pred_category = category_names[np.argmax(all_predictions[i])]\n", " y_true.append(temp_true_labels[i])\n", " y_pred.append(pred_category)" ] }, { "cell_type": "code", "execution_count": 21, "id": "065a17f9", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T07:15:08.650349Z", "iopub.status.busy": "2026-03-07T07:15:08.650034Z", "iopub.status.idle": "2026-03-07T07:15:09.490350Z", "shell.execute_reply": "2026-03-07T07:15:09.489659Z" }, "id": "u-_9onKYpwbj", "outputId": "dcd537cf-1d05-43df-8c83-728800723515", "papermill": { "duration": 1.225453, "end_time": "2026-03-07T07:15:09.494176", "exception": false, "start_time": "2026-03-07T07:15:08.268723", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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c9Zs2l4mNuyFJKuKe/jOy9/9yStf+uSH3wgX1fKeGmvX1Dqv5vdrVk3vhgqm2Fx37j2Z+uV2rt/2sP09HKPradVXwK67n2jfQi90e1vBnmsndraCGvvuNQ+8xr2McOzeOw86PjJ0fGTs38gWQF/B14xxQsGBB+fr6Wk1eXl7avn27ChQooJ07dyYvO2nSJJUsWVKXLl2SJEVGRmrgwIHy8fFRoUKFVKNGDa1du1aSdOXKFfXo0UNlypSRm5ubatasqa+//jq5rXnz5ql06dK6c+eOVX86duyovn37SpKOHz+ujh07ysfHR2azWfXq1dPmzZuTl23atKlOnTqlUaNGpbj9wPLly/Xggw+qYMGCCggI0NSpU61+TkBAgN555x0999xz8vDw0IABA1LdPjdu3FB0dLTVBACAszh14Yr+jrutE2cjtPa7rQoMDNZzPZ9Wv969FB8fn9PdAwDAsGLjbmjGF9skSW8PbafB3R+Rb3EPlfAyq0+nhzTtpSd189ZtSdKdO9a3Hj905JzGTVup3QeP69KVGP1z/ZZ+/fOCxn+wSs+98pkkqV+XRqr1QJl/900BAAAAAGyikG0gTZs21ciRI/Xss88qKipKBw8e1Ouvv65PPvlEPj4+unPnjtq0aaPdu3friy++0G+//aaJEycqX758kqTr16+rTp06WrdunX755RcNGDBAzz77rPbt2ydJevrpp3XlyhVt27Yt+WdevXpVGzduVK9evSRJsbGxatu2rbZs2aKDBw+qdevWat++vU6ftjy3bMWKFfLz89Pbb7+tCxcu6MKFC5KkkJAQde3aVd27d9fhw4f11ltv6fXXX9eiRYus3uOUKVMUGBiY/N5SM2HCBHl6eiZP/v6pP9ssp5nNRSRJcWk80yM2NlaSVMTDI932ihQpkvxvW88JSWrP4772ktZNb73U1oVtZOzcyNf5JW3XuGtxNpdJ2uZFiqS/XZN+Z+5dL0V7sYnt2ZGTl7e3Gjd5REtXr9PjrdtqxdJvtGDeR+muh7sYx86PjJ1bUr5pPSPPVh6ptmdHvteu2d8eMi827rokya2Q7TtxJT33OubadbvafHfeRs1btkuFCxXQ1JeeVPimd3R687ua+3oPhf1xVp+t3itJ+jva9vH/fqu3/aywP85Kkto+8qDd64Fx7Ow4Djs/MnZ+ZOzcyBdAXkAhOwesXbtWZrPZanrvvfckSf/973/l5eWlAQMG6JlnnlHv3r3VoUMHSdLmzZu1b98+rVixQo899pgqVKigdu3aqU2bNpKkMmXKaOzYsQoKClKFChU0bNgwtW7dWkuWLJEkeXl5qU2bNvrqq6+S+7Js2TIVL15czZo1kyQFBgZq4MCBqlGjhipXrqx33nlHFStW1Jo1ayRJ3t7eypcvn4oUKZJ8NbkkTZs2TS1atNDrr7+uBx54QH369NHQoUM1efJkq/fevHlzjRkzRhUrVlTFihVT3T6vvPKKoqKikqczZ85k1abPUuUCAiRJZ8/a7l/SvHJ2PPO0bLlyyf8+k/jFgRTtJW6L+5+hmtR+euuZTCarn4O0kbFzI1/n51/Wsq3OnbOd8bnEjP3t2K5J7UnS2TOpZ5X0s8qWDbC3mzKZTOr5bG9J0rpvV9m9HhjHeQEZO7ekbXoujXzPnbUUF+3ZpmXv2U+fsbGftpUvssep81clSX6+RW0u4+dT1GrZ9Ny5k6ARE5aqbreJenP2Wn2yfLdmfLFNT46cp1YDZ8nDbLml+K9/XnCor0dOWu6CVqak7b4iJcaxc+M47PzI2PmRsXMjXwB5AYXsHNCsWTOFhYVZTS+++KIkqUCBAvryyy+1fPlyXb9+XR988EHyemFhYfLz89MDDzyQarvx8fF65513VLNmTXl7e8tsNmvTpk3JV1NLUq9evbR8+XLduGF5DtmXX36p7t27y8XF8qsQGxursWPHqlq1aipatKjMZrN+//13qzZS8/vvv6tx48ZWrzVu3FjHjh2zuk1q3bp1090+BQsWlIeHh9VkRIFBwZIst3Q/GR6e6jKhIQckSUHBtdNtz8PDQxUrVbJaz1Z7wfe1l9R+eutVqlzZ6nkkSBsZOzfydX61Ai0ZX71yRadOpp7xwdAQSVJgkH0ZV6hoyTgscT1b7dVK/P2yl5ub5Tnef/31l0Pr5XWMY+dHxs7NrnxDHcy3Yjr5Ju6ngxzcTyNjDh2xFDCLFzWrXGnvVJepXb2sJCVfEW2vX/+8oEmffq9h7y3Ryx+s0vqdvyohIUENA8tLkrb89IdD7Xl7Wo7FSc/shn0Yx86N47DzI2PnR8bOjXyBu1xMTI5OuQWF7Bzg7u6uSpUqWU3e3nc/1O/Zs0eS5bbfV6/e/VZ64cKF02x38uTJmjFjhsaPH69t27YpLCxMrVq10s2bN5OXad++vRISErRu3TqdOXNGO3fuTL6tuCSNHTtWK1eu1HvvvaedO3cqLCxMNWvWtGojs+/dWfj5+alO3XqSpG8Wf5Vi/u5du3T2zBkVLFhQrdu0tavNDh0722wvNjZW69d9K0nq2LmL9XqdLOutW7sm1du3JLXXsVOXFPNgGxk7N/J1fmX8/FS7juULVEu/+TrF/B/37NK5s5aMH2vVxq42n+jQUZK0bEnK9mJjY7Vx/VpJUvuOnRzq6w/bt0qSKlWq7NB6eR3j2PmRsXMrc0++S75Jmcee3XfzbdXavnyT9r9LFqe+n96QmG8HcvpXnIuI0oFfTkmSurWuk2J+o6AK8vf10vUbt7Rx92+Z/nlPPRassqW89dOhcB10oDBeuoSnGgdb7hh24NdTme5HXsI4dm4ch50fGTs/MnZu5AsgL6CQbTDHjx/XqFGjNH/+fDVo0EC9e/fWnTt3JEm1atXS2bNndfTo0VTX3b17tzp27KhnnnlGgYGBqlChQoplCxUqpC5duujLL7/U119/rSpVqqh27dpWbfTp00edO3dWzZo15evrq5MnT1q1UaBAAaurrCWpWrVq2r17d4r+PPDAA8nP8HZG415+VZI0ZdJEHQwNTX79ypUrGjlssCTpxcFD5enpmTxv9aqVCqxRVW0eb5GivaHDR8rNzU1bt2zWp5/MT349Pj5eI4YOVmRkpOrUraeWjz1utV6r1m0UFBSsyMhIjRg62CqfBfPnadvWLTKbzRoybETWvPE8hIydG/k6v9EvvSJJmjF1kg4dvJvx1StXNHbkMEnSCy8Otsp47epVqh/0oDq2fSxFe4OGjJCbm5u2b92izz79JPn1+Ph4jR05VFGRkapdp66at7TOeNGC+Tp29EiK9m7duqVFC+br4zmzJEm9+/bPxLvNmxjHzo+MndtL4yz76amT39fBg/flO3yIJGngoCFW+a5ZvVLBNaupbauWKdobOsyS77atm7VwgXW+I4cPsZkvss+khd9Lksb2aamgqn7Jr3t7umn6y09Lkj5aslPRsXefkd2hWS2FLX9V6+cOSdFeqeIeybcjv1ebhx/UrNe66fqNWxr23pIU84f0eFTFiqb8YnWNSqW1bPoLcitUQMfP/KVvtx92+D3mdYxj58Zx2PmRsfMjY+dGvgCcnSkhISEhpzuRl/Tp00eXLl3SwoULrV53dXWVl5eXmjRpojJlymjZsmW6cOGCatasqfHjx+ull16SZLkt+eXLlzVt2jRVqlRJf/zxh0wmk1q3bq3Ro0dr2bJlWrx4sby8vDRt2jQtWbJEzZo106pVq5J/1ubNm9WuXTsFBATomWee0WuvvZY8r0uXLgoPD9fChQtlMpn0+uuva/v27erbt6+mT58uSXr88cdVuHBhzZkzRwULFlTx4sUVGhqqevXq6a233lK3bt30448/atCgQZozZ4769OkjSQoICNDIkSM1cuRIh7ZZdHS0PD09delKlCFvMz5m1AjNmfWh8ufPr2bNW8jN3V3bt25RZGSkGjZqrHUbv7e6mv5/ny3SgP7Pq2y5cjry58kU7S1ftlS9n+mh+Ph41avfQOUCAhRyYL/CT5yQj4+PtmzflXyLl3sdPXJELZs9rL/++kvlK1RQnbr1dDI8XAf275Orq6u++HqJOiZ+Mw6OIWPnRr5Z4/qt+PQXyiEvjx2lj+fMVP78+fVI0+Zyd3fXju1bFRUZqQYNG2nl2k1WGX/1v880ZGA/+Zctp5//OJ6ivVUrlql/716Kj49X3Xr15V8uQAdDDuhk+AmVLOmjDVt2JN+CPEm7Vs21e+cPKl+hoqpWqy53s1l/RVzSH7/9pkuXLsrFxUWvvfWORo0dn+3bI6MK5TfuF9MYx86PjDPvzh3jfuwbO3qE5s627KebNmshd3d3bd92N99v139nne/ni/TiC31Vtlw5/X405S0UVyxfqj7P9rybb7nEfMNPqKSPjzZv3ZlqviOHDVZY2EFJ0o0bN/TzoTBJUr36DZKXad2mrV5+9fUs3gJZo1jDkTndBZumjO2iIT0e1c1bt7Vt31HF/XNTTes/IC8PN+0JO6EnBs/R9Ru3kpd/pn19zX+rl06dv6Kq7d+2aqt905paPLmvfj56XqfOX9Gt2/GqUbm0qpb3Vcy16+o1fqG+/zHlbcUvbJ8gc+GCOnT0nE6dv6I7dxJU3q+4gqr4KV8+F52+cFUdhn6U/KxsI7ry4/Sc7oJNjOPMczHw/R05Djs/MnZ+ZOzcyDfzoqOj5VPMU1FRxqxBwLak+tF/14WpkHuRnO5OrnH9WoxeeyLI4d/52bNna/Lkybp48aICAwM1c+ZM1a9fP931Fi9erB49eqhjx45W9Up7cEV2Dti4caNKlSplNTVp0kTvvvuuTp06pY8//liSVKpUKc2bN0+vvfaaDh06JElavny56tWrpx49eqh69eoaN25c8rebXnvtNdWuXVutWrVS06ZN5evrq06dOqX4+c2bN5e3t7eOHDminj17Ws2bNm2avLy81KhRI7Vv316tWrWyumJbkt5++22dPHlSFStWVIkSJSRJtWvX1pIlS7R48WLVqFFDb7zxht5+++3kIrYzm/rBDP3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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "def draw_confusion_matrix(cm, categories):\n", " fig = plt.figure(figsize=[6.4*pow(len(categories), 0.5), 4.8*pow(len(categories), 0.5)])\n", " ax = fig.add_subplot(111)\n", " cm_norm = cm.astype('float') / np.maximum(cm.sum(axis=1)[:, np.newaxis], np.finfo(np.float64).eps)\n", " im = ax.imshow(cm_norm, interpolation='nearest', cmap=plt.colormaps['Blues'])\n", " ax.figure.colorbar(im, ax=ax)\n", " ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]),\n", " xticklabels=list(categories.values()), yticklabels=list(categories.values()),\n", " ylabel='Annotation', xlabel='Prédiction')\n", " plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n", " thresh = cm_norm.max() / 2.0\n", " for i in range(cm.shape[0]):\n", " for j in range(cm.shape[1]):\n", " ax.text(j, i, format(cm_norm[i, j], '.2f'),\n", " ha=\"center\", va=\"center\",\n", " color=\"white\" if cm_norm[i, j] > thresh else \"black\",\n", " fontsize=int(20-pow(len(categories), 0.5)))\n", " fig.tight_layout()\n", " plt.show()\n", "\n", "# Calcul et affichage\n", "cm = confusion_matrix(y_true, y_pred, labels=list(categories.values()))\n", "draw_confusion_matrix(cm, categories)" ] }, { "cell_type": "code", "execution_count": 22, "id": "6358a56a", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T07:15:10.277073Z", "iopub.status.busy": "2026-03-07T07:15:10.276328Z", "iopub.status.idle": "2026-03-07T07:15:10.287056Z", "shell.execute_reply": "2026-03-07T07:15:10.286257Z" }, "id": "YKS8hszopwbj", "outputId": "98c2372b-b0be-4cd2-8aff-8c966075f7ef", "papermill": { "duration": 0.400634, "end_time": "2026-03-07T07:15:10.288605", "exception": false, "start_time": "2026-03-07T07:15:09.887971", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean Accuracy: 83.962%\n", "Mean Recall: 85.954%\n", "Mean Precision: 87.023%\n", "> Cargo plane: Recall: 94.737% Precision: 100.000% Specificity: 100.000% Dice: 97.297%\n", "> Small car: Recall: 89.379% Precision: 82.440% Specificity: 95.893% Dice: 85.769%\n", "> Bus: Recall: 70.189% Precision: 70.722% Specificity: 96.977% Dice: 70.455%\n", "> Truck: Recall: 56.024% Precision: 62.000% Specificity: 95.403% Dice: 58.861%\n", "> Motorboat: Recall: 87.500% Precision: 88.050% Specificity: 99.284% Dice: 87.774%\n", "> Fishing vessel: Recall: 85.849% Precision: 89.216% Specificity: 99.593% Dice: 87.500%\n", "> Dump truck: Recall: 72.973% Precision: 79.412% Specificity: 98.668% Dice: 76.056%\n", "> Excavator: Recall: 94.915% Precision: 91.803% Specificity: 99.629% Dice: 93.333%\n", "> Building: Recall: 93.692% Precision: 93.002% Specificity: 98.328% Dice: 93.346%\n", "> Helipad: Recall: 94.118% Precision: 100.000% Specificity: 100.000% Dice: 96.970%\n", "> Storage tank: Recall: 93.636% Precision: 95.370% Specificity: 99.614% Dice: 94.495%\n", "> Shipping container: Recall: 88.646% Precision: 83.539% Specificity: 98.451% Dice: 86.017%\n", "> Pylon: Recall: 95.745% Precision: 95.745% Specificity: 99.928% Dice: 95.745%\n" ] } ], "source": [ "import numpy as np\n", "\n", "# Compute the accuracy\n", "correct_samples_class = np.diag(cm).astype(float)\n", "total_samples_class = np.sum(cm, axis=1).astype(float)\n", "total_predicts_class = np.sum(cm, axis=0).astype(float)\n", "print('Mean Accuracy: %.3f%%' % (np.sum(correct_samples_class) / np.sum(total_samples_class) * 100))\n", "acc = correct_samples_class / np.maximum(total_samples_class, np.finfo(np.float64).eps)\n", "print('Mean Recall: %.3f%%' % (acc.mean() * 100))\n", "acc = correct_samples_class / np.maximum(total_predicts_class, np.finfo(np.float64).eps)\n", "print('Mean Precision: %.3f%%' % (acc.mean() * 100))\n", "for idx in range(len(categories)):\n", " # True/False Positives (TP/FP) refer to the number of predicted positives that were correct/incorrect.\n", " # True/False Negatives (TN/FN) refer to the number of predicted negatives that were correct/incorrect.\n", " tp = cm[idx, idx]\n", " fp = sum(cm[:, idx]) - tp\n", " fn = sum(cm[idx, :]) - tp\n", " tn = sum(np.delete(sum(cm) - cm[idx, :], idx))\n", " # True Positive Rate: proportion of real positive cases that were correctly predicted as positive.\n", " recall = tp / np.maximum(tp+fn, np.finfo(np.float64).eps)\n", " # Precision: proportion of predicted positive cases that were truly real positives.\n", " precision = tp / np.maximum(tp+fp, np.finfo(np.float64).eps)\n", " # True Negative Rate: proportion of real negative cases that were correctly predicted as negative.\n", " specificity = tn / np.maximum(tn+fp, np.finfo(np.float64).eps)\n", " # Dice coefficient refers to two times the intersection of two sets divided by the sum of their areas.\n", " # Dice = 2 |A∩B| / (|A|+|B|) = 2 TP / (2 TP + FP + FN)\n", " f1_score = 2 * ((precision * recall) / np.maximum(precision+recall, np.finfo(np.float64).eps))\n", " print('> %s: Recall: %.3f%% Precision: %.3f%% Specificity: %.3f%% Dice: %.3f%%' % (list(categories.values())[idx], recall*100, precision*100, specificity*100, f1_score*100))" ] }, { "cell_type": "markdown", "id": "8d146dee", "metadata": { "id": "zm9Fb45Hpwbk", "papermill": { "duration": 0.383816, "end_time": "2026-03-07T07:15:11.157300", "exception": false, "start_time": "2026-03-07T07:15:10.773484", "status": "completed" }, "tags": [] }, "source": [ "#### Testing\n", "Try to improve the results provided in the competition." ] }, { "cell_type": "code", "execution_count": 23, "id": "6e98a695", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T07:15:11.946036Z", "iopub.status.busy": "2026-03-07T07:15:11.945697Z", "iopub.status.idle": "2026-03-07T07:15:11.992313Z", "shell.execute_reply": "2026-03-07T07:15:11.991572Z" }, "id": "tJr_-xCt-AM-", "outputId": "db331794-c2b1-4fe1-a62a-ede694525eb7", "papermill": { "duration": 0.451166, "end_time": "2026-03-07T07:15:11.993701", "exception": false, "start_time": "2026-03-07T07:15:11.542535", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of testing images: 2365\n" ] } ], "source": [ "import os\n", "import numpy as np\n", "\n", "anns = []\n", "root_dir = './xview_recognition/'\n", "test_dir = os.path.join(root_dir, 'xview_test')\n", "for (dirpath, dirnames, filenames) in os.walk(test_dir):\n", " for filename in filenames:\n", " rel_dir = os.path.relpath(dirpath, root_dir)\n", " clean_filename = os.path.join(rel_dir, filename)\n", " image = GenericImage(clean_filename)\n", " image.tile = np.array([0, 0, 224, 224])\n", " obj = GenericObject()\n", " obj.bb = (0, 0, 224, 224)\n", " obj.category = os.path.basename(dirpath)\n", " image.add_object(obj)\n", " anns.append(image)\n", "print('Number of testing images: ' + str(len(anns)))" ] }, { "cell_type": "code", "execution_count": 24, "id": "84f9b2ba", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T07:15:12.900484Z", "iopub.status.busy": "2026-03-07T07:15:12.900182Z", "iopub.status.idle": "2026-03-07T07:15:53.188796Z", "shell.execute_reply": "2026-03-07T07:15:53.188018Z" }, "id": "TGs2zqfv-AM_", "outputId": "d82d7e77-2f9d-4a67-d72e-7517d0eb11b5", "papermill": { "duration": 40.682336, "end_time": "2026-03-07T07:15:53.190391", "exception": false, "start_time": "2026-03-07T07:15:12.508055", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Préparation des images de test...\n", "Prédiction en cours sur 2365 détections...\n", "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 494ms/step\n", "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 311ms/step\n", "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 312ms/step\n", "Test terminé.\n" ] } ], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "\n", "predictions_data = {\"images\": {}, \"annotations\": {}}\n", "all_test_images = []\n", "metadata = [] \n", "ann_id = 0\n", "\n", "print(\"Préparation des images de test...\")\n", "for idx, ann in enumerate(anns):\n", " image_data = {\n", " \"image_id\": ann.filename.split('/')[-1],\n", " \"filename\": ann.filename,\n", " \"width\": int(ann.tile[2]),\n", " \"height\": int(ann.tile[3])\n", " }\n", " predictions_data[\"images\"][idx] = image_data\n", "\n", " image_raw = load_geoimage(ann.filename)\n", " image_tensor = tf.convert_to_tensor(image_raw)\n", " image_tensor = tf.image.convert_image_dtype(image_tensor, tf.float32)\n", " image_resized = tf.image.resize(image_tensor, [IMG_SIZE, IMG_SIZE], method='bilinear')\n", " img_final = image_resized.numpy()\n", "\n", " for obj_pred in ann.objects:\n", " all_test_images.append(img_final)\n", " metadata.append({\n", " \"image_id\": ann.filename.split('/')[-1],\n", " \"bbox\": [int(x) for x in obj_pred.bb]\n", " })\n", "\n", "if all_test_images:\n", " X_test = np.array(all_test_images)\n", " print(f\"Prédiction en cours sur {len(X_test)} détections...\")\n", " # Moyenne des prédictions des 3 modèles\n", " all_preds = np.mean([\n", " model.predict(X_test, batch_size=128, verbose=1) for model in models\n", " ], axis=0)\n", "\n", " category_names = list(categories.values())\n", " for i, pred in enumerate(all_preds):\n", " pred_category = category_names[np.argmax(pred)]\n", " predictions_data[\"annotations\"][ann_id] = {\n", " \"image_id\": metadata[i][\"image_id\"],\n", " \"category_id\": pred_category,\n", " \"bbox\": metadata[i][\"bbox\"]\n", " }\n", " ann_id += 1\n", "\n", "print(\"Test terminé.\")" ] }, { "cell_type": "code", "execution_count": 25, "id": "8299c96a", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T07:15:54.072705Z", "iopub.status.busy": "2026-03-07T07:15:54.072390Z", "iopub.status.idle": "2026-03-07T07:15:54.075721Z", "shell.execute_reply": "2026-03-07T07:15:54.075137Z" }, "papermill": { "duration": 0.494454, "end_time": "2026-03-07T07:15:54.077119", "exception": false, "start_time": "2026-03-07T07:15:53.582665", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#for i, model in enumerate(models):\n", "# model.load_weights(f'model_{i}.keras')" ] }, { "cell_type": "code", "execution_count": 26, "id": "0bd2110c", "metadata": { "execution": { "iopub.execute_input": "2026-03-07T07:15:54.856705Z", "iopub.status.busy": "2026-03-07T07:15:54.856409Z", "iopub.status.idle": "2026-03-07T07:15:54.888371Z", "shell.execute_reply": "2026-03-07T07:15:54.887789Z" }, "id": "6D1oNJEipwbk", "papermill": { "duration": 0.423337, "end_time": "2026-03-07T07:15:54.889828", "exception": false, "start_time": "2026-03-07T07:15:54.466491", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import json\n", "with open('prediction.json', 'w') as f:\n", " json.dump(predictions_data, f)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kaggle": { "accelerator": "none", "dataSources": [], "isGpuEnabled": false, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" }, "papermill": { "default_parameters": {}, "duration": 14773.138793, "end_time": "2026-03-07T07:15:58.798117", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2026-03-07T03:09:45.659324", "version": "2.6.0" } }, "nbformat": 4, "nbformat_minor": 5 }