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Tâche 3 : OCR et Détection de Falsification\n\n---\nCe notebook présente mes travaux pour la tâche 3 , axée sur la reconnaissance optique de caractères (OCR) et la détection de falsification de documents d'identité. Nous avons scindé la tache en deux. Une partie couvrant l'OCR et la seconde partie , la détecion de falsification.\n\n\n---","metadata":{"id":"157dd8f2"}},{"cell_type":"markdown","source":"## Partie 1 : OCR\n---\nCette partie se concentre sur l'implémentation d'un système d'OCR capable d'extraire des informations clés à partir des quatre types de cartes d'identité distincts fournis. Pour atteindre cet objectif, nous avons affiné un modèle de vision (`Qwen2.5-VL-7B-Instruct`) sur le jeu de données fourni, en y appliquant des augmentations afin de garantir des résultats robustes.\nNous avons également essayé le modèle final sur les données de test.\n\n---","metadata":{"id":"63026981"}},{"cell_type":"markdown","source":"### 1. Préparation de l'Environnement\n","metadata":{"id":"e2e5ee82"}},{"cell_type":"code","source":"!wget -q \"https://drive.google.com/uc?export=download&id=1mEjJxNUN0LmeeXnrGESDwe_aKx6Jb-zN\" -O kaggle.json","metadata":{"id":"eu4xiH0YtP0I","trusted":true,"execution":{"iopub.status.busy":"2025-10-09T19:07:58.465405Z","iopub.execute_input":"2025-10-09T19:07:58.465698Z","iopub.status.idle":"2025-10-09T19:08:02.413142Z","shell.execute_reply.started":"2025-10-09T19:07:58.465659Z","shell.execute_reply":"2025-10-09T19:08:02.412099Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Téléchargement du fichier zip contenant le jeu de données.","metadata":{"id":"6ce63cd5"}},{"cell_type":"code","source":"import os\nimport shutil\n\ndestination_dir = '/root/.config/kaggle/'\ndestination_file = os.path.join(destination_dir, 'kaggle.json')\nos.makedirs(destination_dir, exist_ok=True)\nshutil.copy('kaggle.json', destination_file)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-09T19:08:02.62295Z","iopub.execute_input":"2025-10-09T19:08:02.623492Z","iopub.status.idle":"2025-10-09T19:09:42.944453Z","shell.execute_reply.started":"2025-10-09T19:08:02.62346Z","shell.execute_reply":"2025-10-09T19:09:42.943703Z"},"id":"NYhB5FEhRGki","outputId":"39659dfc-af1d-45f4-88a4-f42a21cc5949","executionInfo":{"status":"ok","timestamp":1760614869180,"user_tz":-60,"elapsed":84,"user":{"displayName":"Parfait BOTCHI","userId":"13971785570063439557"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!chmod 600 /root/.config/kaggle/kaggle.json","metadata":{"id":"NLTuC0A01SSy"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!kaggle competitions download -c anip-reconnaissance-faciale-estimation-ages-ocr","metadata":{"id":"tHXzATC30-e6","executionInfo":{"status":"ok","timestamp":1760615054447,"user_tz":-60,"elapsed":179884,"user":{"displayName":"Parfait BOTCHI","userId":"13971785570063439557"}},"outputId":"41ff961f-d7a6-4e5a-a62e-2edccf54cc29"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Extraction du contenu du fichier zip dans le dossier `data`.","metadata":{"id":"dd3fa2e4"}},{"cell_type":"code","source":"!unzip -oq anip-reconnaissance-faciale-estimation-ages-ocr.zip -d data","metadata":{"id":"YwC5swphhq9v","trusted":true,"execution":{"iopub.status.busy":"2025-10-09T19:09:42.945779Z","iopub.execute_input":"2025-10-09T19:09:42.946021Z","iopub.status.idle":"2025-10-09T19:10:06.890953Z","shell.execute_reply.started":"2025-10-09T19:09:42.946001Z","shell.execute_reply":"2025-10-09T19:10:06.890063Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!rm anip-reconnaissance-faciale-estimation-ages-ocr.zip","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-09T19:10:07.521772Z","iopub.execute_input":"2025-10-09T19:10:07.522019Z","iopub.status.idle":"2025-10-09T19:10:07.654507Z","shell.execute_reply.started":"2025-10-09T19:10:07.521997Z","shell.execute_reply":"2025-10-09T19:10:07.653824Z"},"id":"TAUyzzgLRGkk"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Installation de la bibliothèque `unsloth` pour optimiser le fine-tuning du modèle.","metadata":{"id":"8344ad49"}},{"cell_type":"code","source":"!pip install -q unsloth","metadata":{"id":"rGvAWt5BuBGo","trusted":true,"execution":{"iopub.status.busy":"2025-10-07T09:39:08.568313Z","iopub.execute_input":"2025-10-07T09:39:08.569112Z","iopub.status.idle":"2025-10-07T09:43:49.778191Z","shell.execute_reply.started":"2025-10-07T09:39:08.569084Z","shell.execute_reply":"2025-10-07T09:43:49.777091Z"},"outputId":"6cc1c4b3-da57-4be5-aafa-44ee25890610"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install -q \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"","metadata":{"id":"DLLaG5OKBDPQ","trusted":true,"execution":{"iopub.status.busy":"2025-10-07T09:45:44.724485Z","iopub.execute_input":"2025-10-07T09:45:44.72477Z","iopub.status.idle":"2025-10-07T09:45:57.321476Z","shell.execute_reply.started":"2025-10-07T09:45:44.724753Z","shell.execute_reply":"2025-10-07T09:45:57.32072Z"},"outputId":"f81a1162-8bb6-492b-8771-8b7c9d67d87d"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 2. Chargement du Modèle et Configuration\n\nImportation des bibliothèques nécessaires et chargement du modèle de vision pré-entraîné (`Qwen/Qwen2.5-VL-7B-Instruct`) avec les optimisations de `unsloth`.","metadata":{"id":"e370a242"}},{"cell_type":"code","source":"from unsloth import FastVisionModel\nimport torch\nfrom trl import SFTTrainer\nfrom transformers import TrainingArguments\nfrom datasets import Dataset\nfrom peft import LoraConfig\nfrom PIL import Image as PILImage\nimport json\nimport shutil\nimport os\nfrom torchvision import transforms","metadata":{"id":"AvLOBo6quMmc","trusted":true,"execution":{"iopub.status.busy":"2025-10-07T09:44:03.529032Z","iopub.execute_input":"2025-10-07T09:44:03.529227Z","iopub.status.idle":"2025-10-07T09:44:51.119252Z","shell.execute_reply.started":"2025-10-07T09:44:03.529209Z","shell.execute_reply":"2025-10-07T09:44:51.118455Z"},"outputId":"d5fd0cca-864d-451a-84ad-bb7345107c94"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Télechargement du modèle depuis hugging face","metadata":{"id":"59c82993"}},{"cell_type":"code","source":"max_seq_length = 2048\ndtype = None\nload_in_4bit = True\n\nmodel, tokenizer = FastVisionModel.from_pretrained(\n    \"Qwen/Qwen2.5-VL-7B-Instruct\",\n    max_seq_length = max_seq_length,\n    dtype = dtype,\n    load_in_4bit = load_in_4bit,\n)\n","metadata":{"id":"-4SEUQgguYag","trusted":true,"execution":{"iopub.status.busy":"2025-10-07T09:44:51.120091Z","iopub.execute_input":"2025-10-07T09:44:51.120355Z","iopub.status.idle":"2025-10-07T09:45:44.723062Z","shell.execute_reply.started":"2025-10-07T09:44:51.120332Z","shell.execute_reply":"2025-10-07T09:45:44.722263Z"},"outputId":"97f5e667-fca5-49fe-8430-c513814a7d09"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Configuration du modèle pour le fine-tuning à l'aide de l'approche LoRA.","metadata":{"id":"d59eba7f"}},{"cell_type":"code","source":"model = FastVisionModel.get_peft_model(\n    model,\n    r = 16,\n    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n    lora_alpha = 16,\n    lora_dropout = 0,\n    bias = \"none\",\n    use_gradient_checkpointing = \"unsloth\",\n    random_state = 3407,\n    use_rslora = False,\n    loftq_config = None,\n)","metadata":{"id":"bQs9XOBjuRtd","collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 3. Préparation du Jeu de Données\n\nDéfinition des chemins vers les répertoires d'entraînement et de test.","metadata":{"id":"8cad2597"}},{"cell_type":"code","source":"TRAIN_DIR = \"data/dataset_tache_3/dataset_tache_3/train\"\nTEST_DIR = \"data/dataset_tache_3/dataset_tache_3\"\nTEST_JSON_DIR = \"./test_jsons\"","metadata":{"id":"k_YIRp4qugVw"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Définition de la fonction `load_custom_dataset` pour charger les données (images et annotations JSON) à partir des répertoires spécifiés.","metadata":{"id":"90f04405"}},{"cell_type":"code","source":"def load_custom_dataset(base_dir):\n    data = []\n    for root, dirs, files in os.walk(base_dir):\n        if 'normal' in dirs and 'gt' in dirs:\n            image_dir = os.path.join(root, 'normal')\n            json_dir = os.path.join(root, 'gt')\n            for img_file in os.listdir(image_dir):\n                if img_file.lower().endswith(('.png', '.jpg', '.jpeg')):\n                    base_name = os.path.splitext(img_file)[0]\n                    json_path = os.path.join(json_dir, base_name + '.json')\n                    if os.path.exists(json_path):\n                        with open(json_path, 'r', encoding='utf-8') as f:\n                            gt_dict = json.load(f)\n                        gt_string = json.dumps(gt_dict, ensure_ascii=False)\n                        image_path = os.path.join(image_dir, img_file)\n                        data.append({\"image\": image_path, \"ground_truth\": gt_string})\n    return Dataset.from_list(data)","metadata":{"id":"hzGGxjUguq0g"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Chargement du jeu de données d'entraînement et division en ensembles d'entraînement et d'évaluation.","metadata":{"id":"95be4f05"}},{"cell_type":"code","source":"dataset = load_custom_dataset(TRAIN_DIR)\ndataset=dataset.shuffle()\nprint(len(dataset))\ndataset = dataset.train_test_split(test_size=0.2)","metadata":{"id":"i1R0FjfQustB","outputId":"d6b71119-19cf-4de1-94d5-225b57d09c31"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dataset = dataset['train']\neval_dataset = dataset['test'] if len(dataset) > 1 else None","metadata":{"id":"yTvWIzyvrRCy"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Définition du prompt détaillé pour l'extraction des informations spécifiques à chaque type de carte d'identité.","metadata":{"id":"90a274b1"}},{"cell_type":"code","source":"EXTRACTION_PROMPT = \"\"\"Analyze the ID card image with extreme precision and extract ONLY the visibly present information.\n\n**CARD TYPE IDENTIFICATION:**\nFirst determine the exact card type by examining these key characteristics:\n- **USA**: Contains physical attributes (height, weight, eye color), address field, and MM/DD/YYYY date format\n- **RUS**: Written in Cyrillic characters, includes patronymic_name field, uses DD.MM.YYYY date format with dots\n- **EST**: Shows \"PD\" type designation, \"EST\" country codes, uses DD.MM.YYYY date format with dots\n- **ESP**: Contains second_surname field, uses DD MM YYYY date format with spaces as separators\nIT WILL NOT BE INCLUDED IN THE FINAL JSON\n\n**EXACT FIELD REQUIREMENTS FOR EACH CARD TYPE:**\n\n**USA CARD - Output this exact structure:**\n{\n  \"name\": \"Complete full name as printed on card (first, middle, last)\",\n  \"address\": \"Complete street address including city, state, and ZIP code\",\n  \"birthday\": \"Date of birth in MM/DD/YYYY format only\",\n  \"gender\": \"Single character: M for Male or F for Female\",\n  \"class\": \"License class designation (D, C, etc.)\",\n  \"issue_date\": \"Issue date in MM/DD/YYYY format only\",\n  \"expire_date\": \"Expiration date in MM/DD/YYYY format only\",\n  \"license_number\": \"Driver license number exactly as printed\",\n  \"height\": \"Height measurement with units (e.g., 5'-07\\\")\",\n  \"weight\": \"Weight measurement with units (e.g., 115 lb)\",\n  \"eye_color\": \"Eye color abbreviation (BRO, BLU, GRN, etc.)\",\n  \"DD\": \"Document discriminator number\"\n}\n\n**RUS CARD - Output this exact structure:**\n{\n  \"country_code\": \"Always 'RUS'\",\n  \"surname\": \"Family name in Cyrillic characters exactly as printed\",\n  \"given_name\": \"First name in Cyrillic characters exactly as printed\",\n  \"birthday\": \"Date of birth in DD.MM.YYYY format with dots\",\n  \"issue_date\": \"Issue date in DD.MM.YYYY format with dots\",\n  \"expire_date\": \"Expiration date in DD.MM.YYYY format with dots\",\n  \"gender\": \"Either 'ЖЕН' for female or 'МУЖ' for male\",\n  \"patronymic_name\": \"Patronymic name in Cyrillic characters exactly as printed\",\n  \"place_of_birth\": \"City/place of birth in Cyrillic characters exactly as printed\",\n  \"card_num\": \"Card identification number with spaces as shown\"\n}\n\n**EST CARD - Output this exact structure:**\n{\n  \"type\": \"Always 'PD'\",\n  \"country_code\": \"Always 'EST'\",\n  \"country\": \"Always 'EST'\",\n  \"surname\": \"Family name exactly as printed in Latin characters\",\n  \"given_name\": \"First name exactly as printed in Latin characters\",\n  \"birthday\": \"Date of birth in DD.MM.YYYY format with dots\",\n  \"gender\": \"Either 'M' for male or 'N/F' for female\",\n  \"issue_date\": \"Issue date in DD.MM.YYYY format with dots\",\n  \"expire_date\": \"Expiration date in DD.MM.YYYY format with dots\",\n  \"place_of_birth\": \"Country of birth (typically 'EST')\",\n  \"issue_authority\": \"Issuing authority abbreviation (e.g., 'PPA')\",\n  \"personal_num\": \"Personal identification number without spaces\",\n  \"card_num\": \"Card number exactly as printed\"\n}\n\n**ESP CARD - Output this exact structure:**\n{\n  \"country_code\": \"Always 'ESP'\",\n  \"surname\": \"Primary family name exactly as printed\",\n  \"given_name\": \"First name exactly as printed\",\n  \"birthday\": \"Date of birth in DD MM YYYY format with spaces\",\n  \"issue_date\": \"Issue date in DD MM YYYY format with spaces\",\n  \"expire_date\": \"Expiration date in DD MM YYYY format with spaces\",\n  \"gender\": \"Single character: 'M' for male or 'F' for female\",\n  \"second_surname\": \"Secondary family name exactly as printed\",\n  \"card_num\": \"Card identification number with letters/numbers as shown\",\n  \"personal_num\": \"Personal identification number exactly as printed\"\n}\n\n**CRITICAL EXTRACTION RULES:**\n- Extract ONLY text that is clearly visible and legible in the image\n- DO NOT invent, assume, or hallucinate any information\n- If a field is not visible or cannot be read, OMIT it completely from the JSON output\n- Preserve exact spelling, capitalization, spacing, and special characters\n- Maintain the precise date format specified for each card type\n- Output ONLY the raw JSON object without any additional text, explanations, or formatting\n- Do not translate any text - preserve original language and characters\n\nRESPOND ONLY WITH THE REQUIRED JSON OBJECT AND WITH THE EXACT STRUCTURE AS MENTIONNED.\n\"\"\"","metadata":{"id":"eI1RybTbuwee"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Définition des transformations d'augmentation de données à appliquer aux images pour l'entraînement.","metadata":{"id":"9cb6fd64"}},{"cell_type":"code","source":"augmentation = transforms.Compose([\n    transforms.RandomRotation(degrees=5),\n    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),\n    transforms.RandomAffine(degrees=0, shear=5),\n    transforms.GaussianBlur(kernel_size=(3, 3), sigma=(0.1, 0.5)),\n    transforms.RandomPerspective(distortion_scale=0.1, p=0.5),\n])","metadata":{"id":"AB5_QMRAuke_"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dataset","metadata":{"id":"dwoMNGMD9Hlt","outputId":"6ad11f81-68cc-4006-abdc-6bc74d4361ef"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Formatage des données (images et textes) en utilisant le prompt défini, préparant ainsi le jeu de données pour l'entraînement du modèle.","metadata":{"id":"490b8fe3"}},{"cell_type":"code","source":"def formatting_prompts_func(sample, is_train=False):\n    if isinstance(sample[\"image\"], str):  # If path\n        img = PILImage.open(sample[\"image\"]).convert(\"RGB\")\n    else:\n        img = sample[\"image\"]\n    if is_train:\n        img = augmentation(img)\n\n    conversation = [\n        { \"role\": \"user\",\n          \"content\" : [\n            {\"type\" : \"text\",  \"text\"  : EXTRACTION_PROMPT},\n            {\"type\" : \"image\", \"image\" : img} ]\n        },\n        { \"role\" : \"assistant\",\n          \"content\" : [\n            {\"type\" : \"text\",  \"text\"  : sample[\"ground_truth\"]} ]\n        },\n    ]\n    return { \"messages\" : conversation }","metadata":{"id":"aGnbnFqyuyvb"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dataset=[formatting_prompts_func(ex,True) for ex in train_dataset]","metadata":{"id":"QlUjMXBhRr92"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"eval_dataset=[formatting_prompts_func(ex) for ex in eval_dataset]","metadata":{"id":"jD4RvA78RKIB"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Vérification de la taille des ensembles d'entraînement et d'évaluation.","metadata":{"id":"46feda27"}},{"cell_type":"code","source":"print(len(train_dataset))\nprint(len(eval_dataset))","metadata":{"id":"LRzbETkCbM7j","outputId":"f64105d0-e0ea-4cab-9ef6-2e0f969fe297"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 6. Entraînement du Modèle\n\nConfiguration du `SFTTrainer` avec les hyperparamètres spécifiés et lancement du processus d'entraînement.","metadata":{"id":"6e790c7c"}},{"cell_type":"code","source":"from unsloth.trainer import UnslothVisionDataCollator\nfrom trl import SFTTrainer, SFTConfig\n\nFastVisionModel.for_training(model) # Enable for training!\n\ntrainer = SFTTrainer(\n    model = model,\n    tokenizer = tokenizer,\n    data_collator = UnslothVisionDataCollator(model, tokenizer), # Must use!\n    train_dataset = train_dataset,\n    eval_dataset = eval_dataset,\n    args = SFTConfig(\n        per_device_train_batch_size = 2,\n        gradient_accumulation_steps = 4,\n        warmup_steps = 5,\n        max_steps = 50,\n        learning_rate = 2e-4,\n        logging_steps = 1,\n        optim = \"adamw_8bit\",\n        weight_decay = 0.01,\n        lr_scheduler_type = \"linear\",\n        seed = 3407,\n        output_dir = \"outputs\",\n        report_to = \"none\",     # For Weights and Biases\n\n        # You MUST put the below items for vision finetuning:\n        remove_unused_columns = False,\n        dataset_text_field = \"\",\n        dataset_kwargs = {\"skip_prepare_dataset\": True},\n        max_length = 2048,\n\n        fp16_full_eval = True,\n        per_device_eval_batch_size = 2,\n        eval_accumulation_steps = 4,\n        save_strategy = \"steps\",             # save model every N steps\n        save_steps = 10,                     # how many steps until we save the model\n        save_total_limit = 3,                # keep ony 3 saved checkpoints to save disk space\n        eval_strategy = \"steps\",             # evaluate every N steps\n        eval_steps = 10,                     # how many steps until we do evaluation\n        load_best_model_at_end = True,       # MUST USE for early stopping\n        metric_for_best_model = \"eval_loss\", # metric we want to early stop on\n        greater_is_better = False,\n    ),\n)","metadata":{"id":"8p3MDLU4zUYO"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"trainer.train()","metadata":{"id":"35J8DKrzvWQI","outputId":"633b67c1-2c26-43e4-ad61-96a6170e6a25"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 7. Sauvegarde du Modèle Fine-Tuné\n\nSauvegarde du modèle et du tokenizer après l'entraînement pour une utilisation future.","metadata":{"id":"6df83b96"}},{"cell_type":"code","source":"model.save_pretrained(\"fine_tuned_qwen_vl\")\ntokenizer.save_pretrained(\"fine_tuned_qwen_vl\")","metadata":{"id":"ug8xalYavse2"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"( Entrainement éffectué depuis kaggle )","metadata":{"id":"zIeQ9--MVPoc"}},{"cell_type":"markdown","source":"### 8. Inférence et Évaluation\n\nCette section décrit le processus d'utilisation du modèle fine-tuné pour extraire des informations des cartes d'identité de test et évaluer les performances.","metadata":{"id":"a03c5e07"}},{"cell_type":"markdown","source":"Définition de la fonction `infer_on_image` pour effectuer l'inférence sur une seule image à l'aide du modèle fine-tuné et extraire la réponse au format JSON.","metadata":{"id":"1418179b"}},{"cell_type":"code","source":"def infer_on_image(image_path,prompt):\n    image = PILImage.open(image_path).convert(\"RGB\")\n\n    # Construire les messages (format chat pour Qwen-VL)\n    messages = [\n        {\n            \"role\": \"user\",\n            \"content\": [\n                {\"type\": \"image\"},\n                {\"type\": \"text\", \"text\": prompt}\n            ]\n        }\n    ]\n\n    input_text = tokenizer.apply_chat_template(\n        messages,\n        tokenize=False,\n        add_generation_prompt=True\n    )\n\n    inputs = tokenizer(\n        text=[input_text],\n        images=[image],\n        return_tensors=\"pt\",\n        padding=True\n    ).to(model.device)\n\n    gen_params = {\n        \"max_new_tokens\": 256,\n        \"use_cache\": True,\n       # \"temperature\": 1.5,\n        #\"min_p\": 0.1\n    }\n\n    with torch.no_grad():\n        output = model.generate(**inputs, **gen_params)\n\n    # Extraire UNIQUEMENT les tokens générés (réponse)\n    input_length = inputs.input_ids.shape[1]\n    response_ids = output[0][input_length:]\n\n    response = tokenizer.decode(response_ids, skip_special_tokens=True)\n    try:\n      json_string = \"{\"+response.strip().split(\"{\")[-1].split(\"}\")[0].strip()+\"}\"\n      json_object = json.loads(json_string)\n      return json_object\n    except:\n      return response\n","metadata":{"id":"2Xk1CSoHvf9V"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Exemple d'utilisation de la fonction `infer_on_image` sur une image de test.","metadata":{"id":"b176a26d"}},{"cell_type":"code","source":"test_image_path = \"data/dataset_tache_3/dataset_tache_3/train/est/normal/generated.photos_0139935.png\"\nprediction = infer_on_image(test_image_path,EXTRACTION_PROMPT)\nprint(prediction.replace(\",\", \"\\n\"))","metadata":{"id":"fiarjnSOvmuy","outputId":"f4014248-f264-4ec9-c86a-3ff750924289","executionInfo":{"status":"ok","timestamp":1763717725194,"user_tz":-60,"elapsed":204,"user":{"displayName":"Parfait BOTCHI","userId":"13971785570063439557"}}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Vérification du nombre d'images dans un des sous-dossiers de test (exemple avec le dossier 'rus').","metadata":{"id":"25104384"}},{"cell_type":"code","source":"len(os.listdir('data/dataset_tache_3/dataset_tache_3/test/rus'))","metadata":{"id":"4776PV9zFc_L","outputId":"531989b9-9a4c-4c99-a891-74ffcbecaf87"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Définition de la fonction `custom_collate_fn` pour le traitement par lots des images et des prompts lors de l'inférence.","metadata":{"id":"0323a943"}},{"cell_type":"code","source":"def custom_collate_fn(batch, processor, input_text):\n    images = [item[\"image\"] for item in batch]\n    paths = [item[\"path\"] for item in batch]\n\n    # Process images and text\n    inputs = processor(\n        text=[input_text] * len(images),\n        images=images,\n        return_tensors=\"pt\",\n        padding=True\n    )\n\n    return {\"inputs\": inputs, \"paths\": paths}","metadata":{"id":"8clYespgnol7"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Définition de la classe `ImageDataset` pour charger les images et leurs chemins de manière efficace.","metadata":{"id":"4c1e0e98"}},{"cell_type":"code","source":"class ImageDataset(Dataset):\n    def __init__(self, image_paths, prompt):\n        self.image_paths = image_paths\n        self.prompt = prompt\n        self.images = [PILImage.open(path).convert(\"RGB\") for path in tqdm(image_paths, desc=f\"Loading ...\")]\n\n    def __len__(self):\n        return len(self.image_paths)\n\n    def __getitem__(self, idx):\n        return {\"image\": self.images[idx], \"path\": self.image_paths[idx]}\n\n    def __getitems__(self, indices):\n        return [{\"image\": self.images[i], \"path\": self.image_paths[i]} for i in indices]","metadata":{"id":"dXVfVgF5ncut"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Définition de la fonction `infer_on_subfolders` pour exécuter l'inférence sur tous les sous-dossiers spécifiés du jeu de données test et sauvegarder les résultats dans un fichier JSON.","metadata":{"id":"1e115d96"}},{"cell_type":"code","source":"def infer_on_subfolders(test_dir, subfolders_prompts, batch_size=8, max_new_tokens=256, output_json=\"results.json\"):\n    results = {}\n    total_start_time = time.time()\n\n    for subfolder, prompt in subfolders_prompts.items():\n        print(f\"Traitement du dossier : {subfolder}\")\n        # Load images from subfolder (updated to .png)\n        image_paths = glob.glob(os.path.join(test_dir, subfolder, \"*.png\"))[:50]\n        print(f\"Nombre d'images trouvées : {len(image_paths)}\")\n        if not image_paths:\n            print(f\"Aucune image dans {subfolder}, skip...\")\n            continue\n\n        dataset = ImageDataset(image_paths, prompt)\n        # Use custom collate_fn\n        dataloader = DataLoader(\n            dataset,\n            batch_size=batch_size,\n            shuffle=False,\n            num_workers=2,\n            collate_fn=lambda batch: custom_collate_fn(batch, processor,\n                processor.apply_chat_template(\n                    [{\"role\": \"user\", \"content\": [\n                        {\"type\": \"image\"},\n                        {\"type\": \"text\", \"text\": prompt}\n                    ]}],\n                    tokenize=False,\n                    add_generation_prompt=True\n                )\n            )\n        )\n\n        subfolder_results = []\n        with torch.no_grad():\n            for batch in tqdm(dataloader, desc=f\"Processing {subfolder}\"):\n                inputs = batch[\"inputs\"].to('cuda')\n                paths = batch[\"paths\"]\n\n                outputs = model.generate(\n                    **inputs,\n                    max_new_tokens=max_new_tokens,\n                    use_cache=True\n                )\n\n                input_length = inputs.input_ids.shape[1]\n                for i, output in enumerate(outputs):\n                    response_ids = output[input_length:]\n                    response = processor.decode(response_ids, skip_special_tokens=True).strip()\n                    try:\n                       json_string = \"{\"+response.strip().split(\"{\")[-1].split(\"}\")[0].strip()+\"}\"\n                       json_object = json.loads(json_string)\n                       response=json_object\n                    except:\n                       response=json.loads(\"{}\")\n\n                    subfolder_results.append({\"path\": paths[i].split('/')[-1], \"response\": response})\n\n        results[subfolder] = subfolder_results\n\n    with open(output_json, \"w\", encoding=\"utf-8\") as f:\n        json.dump(results, f, indent=2, ensure_ascii=False)\n    print(f\"Résultats sauvegardés dans {output_json}\")\n\n    print(f\"Temps total : {time.time() - total_start_time:.2f} secondes pour {sum(len(v) for v in results.values())} images\")\n    return results","metadata":{"id":"oAQO2enySLI6"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from transformers import AutoProcessor\nfrom torch.utils.data import DataLoader\nimport glob\nimport time\nfrom tqdm import tqdm\nimport bitsandbytes","metadata":{"id":"SvScHeaoTXdK"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Chargement du processeur (`AutoProcessor`) nécessaire pour prétraiter les images et le texte pour le modèle.","metadata":{"id":"8d59676c"}},{"cell_type":"code","source":"model_name = \"Qwen/Qwen2.5-VL-7B-Instruct\"\nprocessor = AutoProcessor.from_pretrained(model_name)","metadata":{"collapsed":true,"id":"8uJklkdWUOx7","jupyter":{"outputs_hidden":true},"outputId":"f1e883b2-ccaf-4032-8f7a-b4a0d48b2881"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Définition des structures de prompt spécifiques pour chaque type de carte d'identité à traiter. Puisque dans les données le test, les cartes ont été scindées. Ce qui nous facilite la tâche.","metadata":{"id":"4c00751a"}},{"cell_type":"code","source":"request_head=\"Analyze the ID card image with extreme precision and extract ONLY the visibly present information.\"\nrequest_end=\"\"\"\n**CRITICAL EXTRACTION RULES:**\n- Extract ONLY text that is clearly visible and legible in the image\n- DO NOT invent, assume, or hallucinate any information\n- If a field is not visible or cannot be read, OMIT it completely from the JSON output\n- Preserve exact spelling, capitalization, spacing, and special characters\n- Maintain the precise date format specified for each card type\n- Output ONLY the raw JSON object without any additional text, explanations, or formatting\n- Do not translate any text - preserve original language and characters\n\nRESPOND ONLY AND ONLY  WITH THE REQUIRED JSON OBJECT AND WITH THE EXACT STRUCTURE AS MENTIONNED.\n\n\"\"\"\narizona_dl_struct=\"\"\"\n{\n  \"name\": \"Complete full name as printed on card (first, middle, last)\",\n  \"address\": \"Complete street address including city, state, and ZIP code\",\n  \"birthday\": \"Date of birth in MM/DD/YYYY format only\",\n  \"gender\": \"Single character: M for Male or F for Female\",\n  \"class\": \"License class designation (D, C, etc.)\",\n  \"issue_date\": \"Issue date in MM/DD/YYYY format only\",\n  \"expire_date\": \"Expiration date in MM/DD/YYYY format only\",\n  \"license_number\": \"Driver license number exactly as printed\",\n  \"height\": \"Height measurement with units (e.g., 5'-07\\\")\",\n  \"weight\": \"Weight measurement with units (e.g., 115 lb)\",\n  \"eye_color\": \"Eye color abbreviation (BRO, BLU, GRN, etc.)\",\n  \"DD\": \"Document discriminator number\"\n}\n\"\"\"\nesp_struct=\"\"\"\n{\n  \"country_code\": \"Always 'ESP'\",\n  \"surname\": \"Primary family name exactly as printed\",\n  \"given_name\": \"First name exactly as printed\",\n  \"birthday\": \"Date of birth in DD MM YYYY format with spaces\",\n  \"issue_date\": \"Issue date in DD MM YYYY format with spaces\",\n  \"expire_date\": \"Expiration date in DD MM YYYY format with spaces\",\n  \"gender\": \"Single character: 'M' for male or 'F' for female\",\n  \"second_surname\": \"Secondary family name exactly as printed\",\n  \"card_num\": \"Card identification number with letters/numbers as shown\",\n  \"personal_num\": \"Personal identification number exactly as printed\"\n}\n\"\"\"\nest_struct=\"\"\"\n{\n  \"type\": \"Always 'PD'\",\n  \"country_code\": \"Always 'EST'\",\n  \"country\": \"Always 'EST'\",\n  \"surname\": \"Family name exactly as printed in Latin characters\",\n  \"given_name\": \"First name exactly as printed in Latin characters\",\n  \"birthday\": \"Date of birth in DD.MM.YYYY format with dots\",\n  \"gender\": \"Either 'M' for male or 'N/F' for female\",\n  \"issue_date\": \"Issue date in DD.MM.YYYY format with dots\",\n  \"expire_date\": \"Expiration date in DD.MM.YYYY format with dots\",\n  \"place_of_birth\": \"Country of birth (typically 'EST')\",\n  \"issue_authority\": \"Issuing authority abbreviation (e.g., 'PPA')\",\n  \"personal_num\": \"Personal identification number without spaces\",\n  \"card_num\": \"Card number exactly as printed\"\n}\n\"\"\"\nrus_struct=\"\"\"\n{\n  \"country_code\": \"Always 'RUS'\",\n  \"surname\": \"Family name in Cyrillic characters exactly as printed\",\n  \"given_name\": \"First name in Cyrillic characters exactly as printed\",\n  \"birthday\": \"Date of birth in DD.MM.YYYY format with dots\",\n  \"issue_date\": \"Issue date in DD.MM.YYYY format with dots\",\n  \"expire_date\": \"Expiration date in DD.MM.YYYY format with dots\",\n  \"gender\": \"Either 'ЖЕН' for female or 'МУЖ' for male\",\n  \"patronymic_name\": \"Patronymic name in Cyrillic characters exactly as printed\",\n  \"place_of_birth\": \"City/place of birth in Cyrillic characters exactly as printed\",\n  \"card_num\": \"Card identification number with spaces as shown\"\n}\n\"\"\"\n\narizona_dl=request_head+arizona_dl_struct+request_end\nesp=request_head+esp_struct+request_end\nest=request_head+est_struct+request_end\nrus=request_head+rus_struct+request_end","metadata":{"id":"Qm5eFrC8UoG5"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Association des sous-dossiers de test aux prompts correspondants.","metadata":{"id":"12b5d856"}},{"cell_type":"code","source":"subfolders_prompts = {\n        \"arizona_dl\": arizona_dl,\n        \"esp\": esp,\n        \"est\": est,\n        \"rus\": rus\n}","metadata":{"id":"-ALmfOtZW68k"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Exécution de la fonction `infer_on_subfolders` pour lancer l'inférence sur le jeu de données test.","metadata":{"id":"128b8790"}},{"cell_type":"code","source":"results = infer_on_subfolders(\n        test_dir=\"data/dataset_tache_3/dataset_tache_3/test\",\n        subfolders_prompts=subfolders_prompts,\n        batch_size=8,\n        max_new_tokens=256,\n        output_json=\"results.json\"\n    )","metadata":{"id":"h1NbztX1XOBd"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Chargement des résultats de l'inférence à partir du fichier JSON sauvegardé.","metadata":{"id":"9a269ee5"}},{"cell_type":"code","source":"results=json.load(open(\"results.json\"))","metadata":{"id":"-0jQKrxAalXy"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Affichage d'un aperçu des résultats de l'inférence pour chaque type de carte.","metadata":{"id":"9f09b945"}},{"cell_type":"code","source":"for subfolder, res_list in results.items():\n        print(f\"\\nRésultats pour {subfolder} (premiers 2) :\")\n        for res in res_list[:2]:\n            print(f\"Image: {res['path']}\\nRéponse: {res['response']}\\n\")","metadata":{"id":"Flkq2l_RSAIZ","outputId":"c6eb9076-3ad1-48bc-d9dd-0441ea57acc0"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Définition de la fonction `display_images_with_json` pour visualiser les images de test avec les informations JSON extraites.","metadata":{"id":"abf0c391"}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom PIL import Image\n\ndef display_images_with_json(test_dir, json_data, num_images=2):\n    selected_ids = range(num_images)\n\n    plt.figure(figsize=(10, 4 * num_images))\n\n    for i, image_id in enumerate(selected_ids):\n        img_path = os.path.join(test_dir,json_data[i]['path'])\n        image = np.array(Image.open(img_path).convert('RGB'))\n\n        json_info = json_data.pop(i)\n\n        json_text = json_info['response']\n\n        plt.subplot(num_images, 2, 2 * i + 1)\n        plt.imshow(image)\n        plt.title(f\"Image ID: {image_id}\")\n        plt.axis('off')\n\n        plt.subplot(num_images, 2, 2 * i + 2)\n        plt.text(0.5, 0.5, json_text, fontsize=10, ha='left', va='center', wrap=True)\n        plt.axis('off')\n\n    plt.tight_layout(pad=2.0)\n    plt.show()","metadata":{"id":"Q5sLQQt9cSso"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Affichage d'exemples de résultats pour le sous-dossier 'rus' en utilisant la fonction de visualisation.","metadata":{"id":"f6d5ab9f"}},{"cell_type":"code","source":"display_images_with_json('data/dataset_tache_3/dataset_tache_3/test/rus', results['rus'])","metadata":{"id":"jNqq27nzcoqU","outputId":"20ce6d21-c412-42f4-f1c7-25acea0d6822"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Conclusion de la Partie 1 : OCR\n\nDans cette partie , nous avons mis en place le processus de fine-tuning d'un modèle de vision (`Qwen2.5-VL-7B-Instruct`) pour la tâche d'extraction d'informations à partir de différentes cartes d'identité. Les résultats obtenus démontrent la capacité du modèle à traiter différents formats, différentes langues et à extraire les informations avec une précision maximale. Le `validation loss` le prouve avec une valeur de `0.02` .","metadata":{"id":"8178a23b"}},{"cell_type":"markdown","source":"---\n---","metadata":{"id":"X0ZGuVD9zZtr"}},{"cell_type":"markdown","source":"## Partie 2 : Détection de falsification","metadata":{"id":"dlMqDu-Jeph_"}},{"cell_type":"markdown","source":"---\nCette partie se concentre sur la classification des cartes selon les différentes classes prédéfinies (`normal` `forgery_1` `forgery_2` `forgery_3` `forgery_4` ). Nous utilisons le modèle `EfficientNetV2`, la version pré-entrainée sur `IMAGENET1K_V1` afin d'effectuer cette classification.\n\n---","metadata":{"id":"8IHjQIWbwL42"}},{"cell_type":"markdown","source":"**Importations de bibliothèques**","metadata":{"id":"72d12585"}},{"cell_type":"code","source":"import glob\nimport json\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport pandas as pd\nimport random\nimport seaborn as sns\nimport shutil\nimport time\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.amp import GradScaler, autocast\nfrom torch.optim.lr_scheduler import StepLR, OneCycleLR\n\nfrom torchvision import transforms, models\n\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\nfrom sklearn.model_selection import train_test_split\n\nfrom tqdm import tqdm\n\nfrom collections import defaultdict, Counter\nfrom PIL import Image","metadata":{"id":"hBb_-F1Le6bi","trusted":true,"execution":{"iopub.status.busy":"2025-10-09T19:37:52.250341Z","iopub.execute_input":"2025-10-09T19:37:52.250826Z","iopub.status.idle":"2025-10-09T19:37:52.258134Z","shell.execute_reply.started":"2025-10-09T19:37:52.250796Z","shell.execute_reply":"2025-10-09T19:37:52.257302Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Configuration de l'environnement PyTorch pour utiliser le GPU et mieux gérer la mémoire.","metadata":{"id":"17100915"}},{"cell_type":"code","source":"os.environ[\"CUDA_LAUNCH_BLOCKING\"] = \"1\"\nos.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:19.26541Z","iopub.status.busy":"2025-10-12T19:40:19.264611Z","iopub.status.idle":"2025-10-12T19:40:19.268773Z","shell.execute_reply":"2025-10-12T19:40:19.26798Z"},"papermill":{"duration":0.013256,"end_time":"2025-10-12T19:40:19.270028","exception":false,"start_time":"2025-10-12T19:40:19.256772","status":"completed"},"tags":[],"id":"f6b408e3"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Nous définissons les extensions de fichier acceptées, les chemins de base et les types de cartes d'identité.","metadata":{"id":"5c720818"}},{"cell_type":"code","source":"extensions = ('.jpg', '.png')\nbase_dir=\"/kaggle/working/data/dataset_tache_3/dataset_tache_3\"\ncard_types = ['arizona_dl','rus','est','esp']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-09T19:10:07.662365Z","iopub.execute_input":"2025-10-09T19:10:07.662574Z","iopub.status.idle":"2025-10-09T19:10:07.67647Z","shell.execute_reply.started":"2025-10-09T19:10:07.662559Z","shell.execute_reply":"2025-10-09T19:10:07.675689Z"},"id":"Jo6XywRwwL43"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Vérification du nombre d'images normales pour un type de carte spécifique.","metadata":{"id":"f79c344e"}},{"cell_type":"code","source":"print(len(os.listdir(\"/kaggle/working/data/dataset_tache_3/dataset_tache_3/train/arizona_dl/normal\")))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-09T19:10:07.677262Z","iopub.execute_input":"2025-10-09T19:10:07.677474Z","iopub.status.idle":"2025-10-09T19:10:07.690732Z","shell.execute_reply.started":"2025-10-09T19:10:07.677451Z","shell.execute_reply":"2025-10-09T19:10:07.690029Z"},"id":"Yz-OHzOKwL43","outputId":"6b145733-2814-408a-9854-418781682b90"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Statistiques du dataset**: Fonction pour analyser et afficher les statistiques du dataset d'entraînement, incluant les images normales et falsifiées.","metadata":{"id":"91d05397"}},{"cell_type":"code","source":"def simple_dataset_stats():\n    stats = []\n    for card_type in card_types:\n        root_dir = os.path.join(base_dir,'train', card_type)\n        normal_dir = os.path.join(root_dir, 'normal')\n        forgery_dirs = [os.path.join(root_dir, f'forgery_{i}') for i in range(1, 5)]\n\n        # Compter les fichiers normaux\n        normal_files = [f for f in os.listdir(normal_dir) if f.endswith(extensions)]\n        num_normal = len(normal_files)\n\n        # Compter les fichiers dans chaque dossier forgery\n        forgery_counts = {}\n        mismatches = []\n        for i, forg_dir in enumerate(forgery_dirs, 1):\n            forg_files = [f for f in os.listdir(forg_dir) if f.endswith(extensions)]\n            forgery_counts[f'forgery_{i}'] = len(forg_files)\n\n            # Vérifier correspondance des noms\n            normal_set = set(normal_files)\n            forg_set = set(forg_files)\n            missing = normal_set - forg_set\n            extra = forg_set - normal_set\n            if missing or extra:\n                mismatches.append({\n                    'forgery': f'forgery_{i}',\n                    'missing': len(missing),\n                    'extra': len(extra)\n                })\n\n        stats.append({\n            'Type': card_type,\n            'Normal': num_normal,\n            'Forgery_1': forgery_counts['forgery_1'],\n            'Forgery_2': forgery_counts['forgery_2'],\n            'Forgery_3': forgery_counts['forgery_3'],\n            'Forgery_4': forgery_counts['forgery_4'],\n            'Total Forgery': sum(forgery_counts.values()),\n            'Mismatches': mismatches\n        })\n\n    return pd.DataFrame(stats)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-09T18:42:39.923133Z","iopub.execute_input":"2025-10-09T18:42:39.923492Z","iopub.status.idle":"2025-10-09T18:42:39.935246Z","shell.execute_reply.started":"2025-10-09T18:42:39.92347Z","shell.execute_reply":"2025-10-09T18:42:39.934481Z"},"id":"NnOON8ruwL43"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Affichage des statistiques du dataset d'entraînement.","metadata":{"id":"f503dfe9"}},{"cell_type":"code","source":"stats_df = simple_dataset_stats()\nprint(\"Statistiques du dataset:\")\ndisplay(stats_df[['Type', 'Normal', 'Forgery_1', 'Forgery_2', 'Forgery_3', 'Forgery_4', 'Total Forgery']])\nfor rep in stats_df.to_dict('records'):\n    if rep['Mismatches']:\n        print(f\"\\nMismatches pour {rep['Type']}:\")\n        for mismatch in rep['Mismatches']:\n            print(f\"  {mismatch['forgery']}: {mismatch['missing']} manquants, {mismatch['extra']} en trop\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-09T03:18:56.356247Z","iopub.execute_input":"2025-10-09T03:18:56.356857Z","iopub.status.idle":"2025-10-09T03:18:56.398988Z","shell.execute_reply.started":"2025-10-09T03:18:56.356833Z","shell.execute_reply":"2025-10-09T03:18:56.398232Z"},"id":"Iwqob4jJwL43","outputId":"b80b13eb-4d4c-4b4c-db53-d2b9c5646df9"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Visualisation d'exemples**: Fonction pour visualiser des paires d'images normales et falsifiées.","metadata":{"id":"45948daa"}},{"cell_type":"code","source":"def visualize_two_pairs_per_type(num_samples=2):\n    for card_type in card_types:\n        root_dir = os.path.join(base_dir,  'train',card_type)\n        normal_dir = os.path.join(root_dir, 'normal')\n        forgery_dirs = [os.path.join(root_dir, f'forgery_{i}') for i in range(1, 5)]\n\n        # Lister les fichiers normaux\n        normal_files = [f for f in os.listdir(normal_dir) if f.endswith(extensions)]\n        if len(normal_files) < num_samples:\n            print(f\"Attention: {card_type} a moins de {num_samples} cartes normales ({len(normal_files)})\")\n            sampled_normals = normal_files\n        else:\n            sampled_normals = random.sample(normal_files, num_samples)\n\n        plt.figure(figsize=(10, 5 * num_samples))\n        for i, normal in enumerate(sampled_normals, 1):\n            img_normal = Image.open(os.path.join(normal_dir, normal))\n            plt.subplot(num_samples, 2, i * 2 - 1)\n            plt.imshow(img_normal)\n            plt.title(f\"{card_type}: Normale\\n{normal[:15]}...\")\n            plt.axis('off')\n\n            available_forg = [j for j in range(1, 5) if os.path.exists(os.path.join(root_dir, f'forgery_{j}', normal))]\n            if not available_forg:\n                print(f\"Aucune falsification pour {normal} dans {card_type}\")\n                continue\n            forg_idx = random.choice(available_forg)\n            img_forg = Image.open(os.path.join(root_dir, f'forgery_{forg_idx}', normal))\n            plt.subplot(num_samples, 2, i * 2)\n            plt.imshow(img_forg)\n            plt.title(f\"Falsifiée (forgery_{forg_idx})\")\n            plt.axis('off')\n\n        plt.tight_layout()\n        plt.savefig(f'pair_visualization_{card_type}.png')\n        plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-09T03:19:04.354779Z","iopub.execute_input":"2025-10-09T03:19:04.355053Z","iopub.status.idle":"2025-10-09T03:19:04.363136Z","shell.execute_reply.started":"2025-10-09T03:19:04.355032Z","shell.execute_reply":"2025-10-09T03:19:04.362453Z"},"id":"ny8G8CNVwL44"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Affichage des visualisations pour chaque type de carte.","metadata":{"id":"2ed61140"}},{"cell_type":"code","source":"visualize_two_pairs_per_type(num_samples=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-09T03:19:08.251125Z","iopub.execute_input":"2025-10-09T03:19:08.251396Z","iopub.status.idle":"2025-10-09T03:19:11.745909Z","shell.execute_reply.started":"2025-10-09T03:19:08.251378Z","shell.execute_reply":"2025-10-09T03:19:11.745137Z"},"id":"L_D-Qqt8wL44"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Classe DocDataset**: Définition de la classe personnalisée `DocDataset` pour charger les images et leurs labels.","metadata":{"id":"6c7e00e5"}},{"cell_type":"code","source":"class DocDataset(Dataset):\n    def __init__(self, image_paths, labels=None, transform=None):\n        self.image_paths = image_paths\n        self.labels = labels\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.image_paths)\n\n    def __getitem__(self, idx):\n        img_path = self.image_paths[idx]\n        image = Image.open(img_path).convert('RGB')\n        if self.transform:\n            image = self.transform(image)\n        else:\n            image = transforms.ToTensor()(image)\n        if self.labels is not None:\n            label = self.labels[idx]\n            return image, label\n        return image, os.path.basename(img_path)","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:19.300598Z","iopub.status.busy":"2025-10-12T19:40:19.299922Z","iopub.status.idle":"2025-10-12T19:40:19.30543Z","shell.execute_reply":"2025-10-12T19:40:19.304739Z"},"papermill":{"duration":0.014965,"end_time":"2025-10-12T19:40:19.306635","exception":false,"start_time":"2025-10-12T19:40:19.29167","status":"completed"},"tags":[],"id":"18d62e82"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Parsing des données**: Définition du mappage des classes et de la fonction pour collecter les chemins d'images et les labels d'entraînement.","metadata":{"id":"9fd8f464"}},{"cell_type":"code","source":"class_map = {'normal': 0, 'forgery_1': 1, 'forgery_2': 2, 'forgery_3': 3, 'forgery_4': 4}\nreverse_class_map = {v: k for k, v in class_map.items()}\n\ndef parse_train_data(train_dir):\n    image_paths = []\n    labels = []\n    for country in card_types:\n        for subdir in class_map.keys():\n            path = os.path.join(train_dir, country, subdir)\n            if os.path.exists(path):\n                for filename in os.listdir(path):\n                    if filename.lower().endswith(('.jpg', '.png', '.jpeg')):\n                        image_paths.append(os.path.join(path, filename))\n                        labels.append(class_map[subdir])\n    return image_paths, labels","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:19.336412Z","iopub.status.busy":"2025-10-12T19:40:19.335721Z","iopub.status.idle":"2025-10-12T19:40:19.341308Z","shell.execute_reply":"2025-10-12T19:40:19.340556Z"},"papermill":{"duration":0.014653,"end_time":"2025-10-12T19:40:19.342476","exception":false,"start_time":"2025-10-12T19:40:19.327823","status":"completed"},"tags":[],"id":"e4a8e2ba"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Transformations d'images**: Nous utiliserons une `train_transform` pour l'entraînement et `val_transform` pour la validation.","metadata":{"id":"3ccbb3de"}},{"cell_type":"code","source":"train_transform = transforms.Compose([\n    transforms.Resize(384),\n    transforms.RandomResizedCrop(384, scale=(0.8, 1.0)),\n    transforms.RandomHorizontalFlip(p=0.5),\n    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n])\n\nval_transform = transforms.Compose([\n    transforms.Resize(384),\n    transforms.CenterCrop(384),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n])","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:19.37187Z","iopub.status.busy":"2025-10-12T19:40:19.3715Z","iopub.status.idle":"2025-10-12T19:40:19.378075Z","shell.execute_reply":"2025-10-12T19:40:19.377287Z"},"papermill":{"duration":0.015633,"end_time":"2025-10-12T19:40:19.379298","exception":false,"start_time":"2025-10-12T19:40:19.363665","status":"completed"},"tags":[],"id":"f726174d"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Chargement des données d'entraînement**: Chargement des chemins d'images et des labels pour l'ensemble d'entraînement.","metadata":{"id":"c910ab93"}},{"cell_type":"code","source":"image_paths, labels = parse_train_data(os.path.join('data/dataset_tache_3/dataset_tache_3', 'train'))\nlabels = np.array(labels)","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:19.409074Z","iopub.status.busy":"2025-10-12T19:40:19.408802Z","iopub.status.idle":"2025-10-12T19:40:19.428404Z","shell.execute_reply":"2025-10-12T19:40:19.427823Z"},"papermill":{"duration":0.02858,"end_time":"2025-10-12T19:40:19.429441","exception":false,"start_time":"2025-10-12T19:40:19.400861","status":"completed"},"tags":[],"id":"0310fcb4"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Division des données**: Division du dataset d'entraînement en ensembles d'entraînement et de validation.","metadata":{"id":"1fa44c36"}},{"cell_type":"code","source":"train_idx, val_idx = train_test_split(range(len(image_paths)), test_size=0.2, stratify=labels, random_state=42)\ntrain_paths, val_paths = [image_paths[i] for i in train_idx], [image_paths[i] for i in val_idx]\ntrain_labels, val_labels = labels[train_idx], labels[val_idx]","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:19.951464Z","iopub.status.busy":"2025-10-12T19:40:19.951204Z","iopub.status.idle":"2025-10-12T19:40:19.963793Z","shell.execute_reply":"2025-10-12T19:40:19.962926Z"},"papermill":{"duration":0.022064,"end_time":"2025-10-12T19:40:19.964999","exception":false,"start_time":"2025-10-12T19:40:19.942935","status":"completed"},"tags":[],"id":"06bed33f"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Création des DataLoaders**: Initialisation des DataLoaders pour charger les données par lots pour l'entraînement et la validation.","metadata":{"id":"3833dbe8"}},{"cell_type":"code","source":"train_dataset = DocDataset(train_paths, train_labels, train_transform)\nval_dataset = DocDataset(val_paths, val_labels, val_transform)\n\ntrain_loader = DataLoader(train_dataset, batch_size=8, shuffle=True, num_workers=0)\nval_loader = DataLoader(val_dataset, batch_size=8, shuffle=False, num_workers=0)","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:20.148572Z","iopub.status.busy":"2025-10-12T19:40:20.148182Z","iopub.status.idle":"2025-10-12T19:40:20.155462Z","shell.execute_reply":"2025-10-12T19:40:20.154463Z"},"papermill":{"duration":0.076368,"end_time":"2025-10-12T19:40:20.157249","exception":false,"start_time":"2025-10-12T19:40:20.080881","status":"completed"},"tags":[],"id":"21019ee3"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Vérification de la taille des datasets.","metadata":{"id":"7b1461c9"}},{"cell_type":"code","source":"print(len(train_dataset))\nprint(len(val_dataset))","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:20.286Z","iopub.status.busy":"2025-10-12T19:40:20.285244Z","iopub.status.idle":"2025-10-12T19:40:20.290817Z","shell.execute_reply":"2025-10-12T19:40:20.289711Z"},"papermill":{"duration":0.07679,"end_time":"2025-10-12T19:40:20.292567","exception":false,"start_time":"2025-10-12T19:40:20.215777","status":"completed"},"tags":[],"id":"e6e6680d","outputId":"b991cd00-089a-4444-8ff7-4e5ed4b7b4f3"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Définition du modèle**: Définition de l'architecture du modèle de classification basé sur EfficientNetV2.","metadata":{"id":"835fb8c7"}},{"cell_type":"code","source":"class DocClassifier(nn.Module):\n    def __init__(self, num_classes=5):\n        super().__init__()\n        self.base_model = models.efficientnet_v2_m(weights='IMAGENET1K_V1')\n        in_features = self.base_model.classifier[1].in_features\n        self.base_model.classifier = nn.Sequential(\n            nn.Dropout(0.5),\n            nn.Linear(in_features, num_classes)\n        )\n\n    def forward(self, x):\n        return self.base_model(x)","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:19.80641Z","iopub.status.busy":"2025-10-12T19:40:19.805953Z","iopub.status.idle":"2025-10-12T19:40:19.810623Z","shell.execute_reply":"2025-10-12T19:40:19.809931Z"},"papermill":{"duration":0.014339,"end_time":"2025-10-12T19:40:19.812059","exception":false,"start_time":"2025-10-12T19:40:19.79772","status":"completed"},"tags":[],"id":"a175a6bd"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Initialisation du critère de perte (CrossEntropyLoss) pour l'entraînement.","metadata":{"id":"86bbc502"}},{"cell_type":"code","source":"criterion = nn.CrossEntropyLoss()","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:19.84281Z","iopub.status.busy":"2025-10-12T19:40:19.842173Z","iopub.status.idle":"2025-10-12T19:40:19.846014Z","shell.execute_reply":"2025-10-12T19:40:19.845238Z"},"papermill":{"duration":0.013134,"end_time":"2025-10-12T19:40:19.847193","exception":false,"start_time":"2025-10-12T19:40:19.834059","status":"completed"},"tags":[],"id":"69afd8a8"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Fonction d'entraînement par époque**: Elle a pour but d'entraîner le modèle sur une époque donnée.","metadata":{"id":"5a486230"}},{"cell_type":"code","source":"def train_epoch(model, loader, optimizer, scaler, device):\n    model.train()\n    running_loss = 0.0\n    for inputs, labels in loader:\n        inputs, labels = inputs.to(device), labels.to(device)\n        optimizer.zero_grad()\n        with autocast('cuda'):\n            outputs = model(inputs)\n            loss = criterion(outputs, labels)\n        scaler.scale(loss).backward()\n        scaler.step(optimizer)\n        scaler.update()\n        running_loss += loss.item()\n    return running_loss / len(loader)","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:19.87798Z","iopub.status.busy":"2025-10-12T19:40:19.877196Z","iopub.status.idle":"2025-10-12T19:40:19.882326Z","shell.execute_reply":"2025-10-12T19:40:19.881564Z"},"papermill":{"duration":0.014224,"end_time":"2025-10-12T19:40:19.883382","exception":false,"start_time":"2025-10-12T19:40:19.869158","status":"completed"},"tags":[],"id":"2458b6c2"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Fonction de validation par époque**: Elle a pour rôle d'évaluer le modèle sur l'ensemble de validation et calculer les métriques.","metadata":{"id":"3dccc004"}},{"cell_type":"code","source":"def validate_epoch(model, loader, device):\n    model.eval()\n    val_loss = 0.0\n    preds, true_labels = [], []\n    with torch.no_grad():\n        for inputs, labels in loader:\n            inputs, labels = inputs.to(device), labels.to(device)\n            with autocast('cuda'):\n                outputs = model(inputs)\n                loss = criterion(outputs, labels)\n            val_loss += loss.item()\n            preds.extend(torch.argmax(outputs, dim=1).cpu().numpy())\n            true_labels.extend(labels.cpu().numpy())\n    acc = accuracy_score(true_labels, preds)\n    prec = precision_score(true_labels, preds, average='weighted')\n    rec = recall_score(true_labels, preds, average='weighted')\n    f1 = f1_score(true_labels, preds, average='weighted')\n    cm = confusion_matrix(true_labels, preds)\n    return val_loss / len(loader), acc, prec, rec, f1, cm","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:19.914329Z","iopub.status.busy":"2025-10-12T19:40:19.914096Z","iopub.status.idle":"2025-10-12T19:40:19.91983Z","shell.execute_reply":"2025-10-12T19:40:19.919127Z"},"papermill":{"duration":0.015113,"end_time":"2025-10-12T19:40:19.921041","exception":false,"start_time":"2025-10-12T19:40:19.905928","status":"completed"},"tags":[],"id":"5dd4c955"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Initialisation de l'entraînement**: Préparation des variables pour le processus d'entraînement, incluant le modèle, l'optimiseur, le scheduler et le scaler.","metadata":{"id":"02f081a5"}},{"cell_type":"code","source":"best_metrics = {'val_loss': float('inf'), 'acc': 0.0, 'prec': 0.0, 'rec': 0.0, 'f1': 0.0}\nbest_val_loss = float('inf')\n\ntrain_losses = []\nval_losses = []\n\nn_epochs=30\npatience = 10\nearly_stop_counter = 0","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:20.430061Z","iopub.status.busy":"2025-10-12T19:40:20.429351Z","iopub.status.idle":"2025-10-12T19:40:20.435439Z","shell.execute_reply":"2025-10-12T19:40:20.434646Z"},"papermill":{"duration":0.030714,"end_time":"2025-10-12T19:40:20.436573","exception":false,"start_time":"2025-10-12T19:40:20.405859","status":"completed"},"tags":[],"id":"ca0cbf11"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model = DocClassifier().to(device)\noptimizer = optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\nscheduler = OneCycleLR(optimizer, max_lr=1e-3, epochs=n_epochs, steps_per_epoch=len(train_loader))\nscaler = GradScaler('cuda')","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:20.687541Z","iopub.status.busy":"2025-10-12T19:40:20.687291Z","iopub.status.idle":"2025-10-12T19:40:23.627645Z","shell.execute_reply":"2025-10-12T19:40:23.626997Z"},"papermill":{"duration":3.004489,"end_time":"2025-10-12T19:40:23.629133","exception":false,"start_time":"2025-10-12T19:40:20.624644","status":"completed"},"tags":[],"id":"0b78bac5","outputId":"b7822a88-9031-4106-8bc0-e6de44c66295","executionInfo":{"status":"error","timestamp":1760616057014,"user_tz":-60,"elapsed":37,"user":{"displayName":"Parfait BOTCHI","userId":"13971785570063439557"}}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Boucle d'entraînement**: Exécution de la boucle d'entraînement principale avec validation et sauvegarde du meilleur modèle.","metadata":{"id":"98c5ebaf"}},{"cell_type":"code","source":"for epoch in range(n_epochs):\n    train_loss = train_epoch(model, train_loader, optimizer, scaler, device)\n    val_loss, acc, prec, rec, f1, cm = validate_epoch(model, val_loader, device)\n    scheduler.step()\n    train_losses.append(train_loss)\n    val_losses.append(val_loss)\n    print(f\"Epoch {epoch+1}: Train Loss {train_loss:.4f}, Val Loss {val_loss:.4f}, Acc {acc:.4f}, Recall {rec:.4f}, Precision {prec:.4f}, F1 {f1:.4f}\")\n\n    if val_loss < best_val_loss :\n        best_val_loss = val_loss\n        best_metrics = {'val_loss': val_loss, 'acc': acc, 'prec': prec, 'rec': rec, 'f1': f1}\n        best_cm=cm\n        early_stop_counter = 0\n        torch.save(model.state_dict(), 'best_model.pth')\n    else:\n        early_stop_counter += 1\n        if early_stop_counter >= patience:\n            print(f\"Early stopping déclenché à l'époque {epoch+1}\")\n            break","metadata":{"execution":{"iopub.execute_input":"2025-10-12T19:40:23.663913Z","iopub.status.busy":"2025-10-12T19:40:23.663138Z","iopub.status.idle":"2025-10-13T01:21:46.480731Z","shell.execute_reply":"2025-10-13T01:21:46.479896Z"},"papermill":{"duration":20482.837102,"end_time":"2025-10-13T01:21:46.491309","exception":false,"start_time":"2025-10-12T19:40:23.654207","status":"completed"},"tags":[],"id":"ea674671","outputId":"237b6895-95fd-4f6d-aa74-a2b6707ed1d4"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Affichage des meilleures métriques de performance obtenues sur l'ensemble de validation.","metadata":{"id":"9712bcd2"}},{"cell_type":"code","source":"print(\"\\nMeilleures métriques du modèle :\")\nprint(f\"Validation Loss: {best_metrics['val_loss']:.4f}\")\nprint(f\"Accuracy: {best_metrics['acc']:.4f}\")\nprint(f\"Precision: {best_metrics['prec']:.4f}\")\nprint(f\"Recall: {best_metrics['rec']:.4f}\")\nprint(f\"F1-Score: {best_metrics['f1']:.4f}\")","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:46.510662Z","iopub.status.busy":"2025-10-13T01:21:46.510426Z","iopub.status.idle":"2025-10-13T01:21:46.515164Z","shell.execute_reply":"2025-10-13T01:21:46.51441Z"},"papermill":{"duration":0.01585,"end_time":"2025-10-13T01:21:46.516296","exception":false,"start_time":"2025-10-13T01:21:46.500446","status":"completed"},"tags":[],"id":"2cc77372","outputId":"fc9dc460-d3a7-4f3a-857b-f13df70303c9"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Matrice de confusion du meilleur modèle sur l'ensemble de validation.","metadata":{"id":"1c635e53"}},{"cell_type":"code","source":"plt.figure(figsize=(8, 6))\nsns.heatmap(best_cm, annot=True, fmt='d', cmap='Blues', xticklabels=reverse_class_map.values(), yticklabels=reverse_class_map.values())\nplt.title('Matrice de Confusion (Validation Set)')\nplt.xlabel('Prédictions')\nplt.ylabel('Vraies Classes')\nplt.show()\nplt.close()","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:46.552663Z","iopub.status.busy":"2025-10-13T01:21:46.552458Z","iopub.status.idle":"2025-10-13T01:21:46.76337Z","shell.execute_reply":"2025-10-13T01:21:46.762721Z"},"papermill":{"duration":0.221396,"end_time":"2025-10-13T01:21:46.764418","exception":false,"start_time":"2025-10-13T01:21:46.543022","status":"completed"},"tags":[],"id":"db542a58","outputId":"4b122f9f-b0e8-4a77-f09d-c1feda690cb6"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Courbes de perte d'entraînement et de validation au fil des époques.","metadata":{"id":"9132ef91"}},{"cell_type":"code","source":"plt.figure(figsize=(10, 5))\nplt.plot(range(1, len(train_losses) + 1), train_losses, label='Train Loss')\nplt.plot(range(1, len(val_losses) + 1), val_losses, label='Validation Loss')\nplt.title('Courbes de Train et Validation Loss')\nplt.xlabel('Époque')\nplt.ylabel('Loss')\nplt.legend()\nplt.grid(True)\nplt.show()\nplt.close()","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:46.804252Z","iopub.status.busy":"2025-10-13T01:21:46.804051Z","iopub.status.idle":"2025-10-13T01:21:46.966174Z","shell.execute_reply":"2025-10-13T01:21:46.965474Z"},"papermill":{"duration":0.173366,"end_time":"2025-10-13T01:21:46.967183","exception":false,"start_time":"2025-10-13T01:21:46.793817","status":"completed"},"tags":[],"id":"5d66a97f","outputId":"ba0c830d-531d-4e02-9835-8b78bbd0db61"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Collecte des chemins d'images de l'ensemble de test.","metadata":{"id":"993e4dcc"}},{"cell_type":"code","source":"def parse_test_data(test_dir):\n    image_paths = []\n    countries = {}\n    for country in card_types:\n        path = os.path.join(test_dir, country)\n        if os.path.exists(path):\n            for filename in os.listdir(path):\n                if filename.lower().endswith(('.jpg', '.png', '.jpeg')):\n                    image_paths.append(os.path.join(path, filename))\n                    countries[ filename]=country\n    return image_paths, countries","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:47.009734Z","iopub.status.busy":"2025-10-13T01:21:47.009508Z","iopub.status.idle":"2025-10-13T01:21:47.013328Z","shell.execute_reply":"2025-10-13T01:21:47.012842Z"},"papermill":{"duration":0.015779,"end_time":"2025-10-13T01:21:47.014279","exception":false,"start_time":"2025-10-13T01:21:46.9985","status":"completed"},"tags":[],"id":"b4e8514c"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Chargement des images de test et création du DataLoader correspondant.","metadata":{"id":"9b2bf3e1"}},{"cell_type":"code","source":"test_dir = 'data/dataset_tache_3/dataset_tache_3/test'\ntest_paths,countries = parse_test_data(test_dir)\ntest_dataset = DocDataset(test_paths, transform=val_transform)\ntest_loader = DataLoader(test_dataset, batch_size=8, shuffle=False, num_workers=4)","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:47.036051Z","iopub.status.busy":"2025-10-13T01:21:47.035592Z","iopub.status.idle":"2025-10-13T01:21:47.039871Z","shell.execute_reply":"2025-10-13T01:21:47.039342Z"},"papermill":{"duration":0.016373,"end_time":"2025-10-13T01:21:47.040953","exception":false,"start_time":"2025-10-13T01:21:47.02458","status":"completed"},"tags":[],"id":"27fd55a2","executionInfo":{"status":"ok","timestamp":1760616849250,"user_tz":-60,"elapsed":162,"user":{"displayName":"Parfait BOTCHI","userId":"13971785570063439557"}},"outputId":"203ddceb-2049-48b2-9419-656a70526e53"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Vérification de la taille du dataset de test.","metadata":{"id":"3bf5249c"}},{"cell_type":"code","source":"len(test_dataset)","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:47.06262Z","iopub.status.busy":"2025-10-13T01:21:47.062243Z","iopub.status.idle":"2025-10-13T01:21:47.066423Z","shell.execute_reply":"2025-10-13T01:21:47.065904Z"},"papermill":{"duration":0.015939,"end_time":"2025-10-13T01:21:47.067356","exception":false,"start_time":"2025-10-13T01:21:47.051417","status":"completed"},"tags":[],"id":"403d2549","outputId":"e4583d3c-dfb7-428b-b58c-c184a4d38813","executionInfo":{"status":"ok","timestamp":1760620027148,"user_tz":-60,"elapsed":48,"user":{"displayName":"Parfait BOTCHI","userId":"13971785570063439557"}}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Fonction pour effectuer l'inférence sur les données de test.","metadata":{"id":"0993c416"}},{"cell_type":"code","source":"def predict(model, test_loader, device):\n    model.eval()\n    preds = []\n    filenames = []\n    with torch.no_grad():\n        for inputs, names in test_loader:\n            inputs = inputs.to(device)\n            with autocast('cuda'):\n                outputs = model(inputs)\n            preds.extend(torch.argmax(outputs, dim=1).cpu().numpy())\n            filenames.extend(names)\n    return [reverse_class_map[p] for p in preds], filenames","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:47.089168Z","iopub.status.busy":"2025-10-13T01:21:47.088995Z","iopub.status.idle":"2025-10-13T01:21:47.093329Z","shell.execute_reply":"2025-10-13T01:21:47.092627Z"},"papermill":{"duration":0.016318,"end_time":"2025-10-13T01:21:47.094421","exception":false,"start_time":"2025-10-13T01:21:47.078103","status":"completed"},"tags":[],"id":"94121c35"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Chargement du meilleur modèle et exécution de la prédiction sur l'ensemble de test.","metadata":{"id":"f80cd221"}},{"cell_type":"code","source":"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel = DocClassifier().to(device)\nmodel.load_state_dict(torch.load('best_model.pth'))\ntest_classes, test_files = predict(model, test_loader, device)","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:47.116053Z","iopub.status.busy":"2025-10-13T01:21:47.115859Z","iopub.status.idle":"2025-10-13T01:21:51.227636Z","shell.execute_reply":"2025-10-13T01:21:51.226869Z"},"papermill":{"duration":4.124138,"end_time":"2025-10-13T01:21:51.229055","exception":false,"start_time":"2025-10-13T01:21:47.104917","status":"completed"},"tags":[],"id":"5967d7eb"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Sauvegarde des résultats**: Création d'un DataFrame avec les prédictions et sauvegarde au format CSV.","metadata":{"id":"d4600510"}},{"cell_type":"code","source":"df = pd.DataFrame({'image_id': test_files, 'class': test_classes})\ndf['country'] = df['image_id'].apply(lambda x: countries.get(x, 'Unknown'))\ndf.to_csv('result2_task3.csv', index=False)\nprint(\"Fichier CSV généré : result2_task3.csv\")","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:51.252519Z","iopub.status.busy":"2025-10-13T01:21:51.252269Z","iopub.status.idle":"2025-10-13T01:21:51.265963Z","shell.execute_reply":"2025-10-13T01:21:51.265317Z"},"papermill":{"duration":0.026284,"end_time":"2025-10-13T01:21:51.266978","exception":false,"start_time":"2025-10-13T01:21:51.240694","status":"completed"},"tags":[],"id":"0d9bf9be","outputId":"e3dc6cf7-28d1-44e7-d294-3007dac0c1ab","executionInfo":{"status":"ok","timestamp":1760616982471,"user_tz":-60,"elapsed":47,"user":{"displayName":"Parfait BOTCHI","userId":"13971785570063439557"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df.head()","metadata":{"id":"_s3y-aI552Kp","executionInfo":{"status":"ok","timestamp":1760616990528,"user_tz":-60,"elapsed":42,"user":{"displayName":"Parfait BOTCHI","userId":"13971785570063439557"}},"outputId":"1c83ae39-a617-433f-d25d-639bab986432"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df=pd.read_csv('result2_task3.csv')","metadata":{"id":"t81JOtrk1yWP"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"**Visualisation des prédictions**: Fonction pour afficher des images de test avec leurs classes prédites.","metadata":{"id":"f64e5164"}},{"cell_type":"code","source":"def display_images_with_predictions(df, test_dir, num_images=9):\n\n    random_indices = random.sample(range(len(df)), num_images)\n    sampled_df = df.iloc[random_indices]\n\n    fig, axes = plt.subplots(int(np.ceil(num_images/3)), 3, figsize=(15, 5 * int(np.ceil(num_images/3))))\n    axes = axes.ravel()\n\n    for i, (_, row) in enumerate(sampled_df.iterrows()):\n        img_path = os.path.join(test_dir, row['country'], row['image_id'])\n        image = Image.open(img_path)\n        axes[i].imshow(image)\n        axes[i].set_title(f\"Pred: {row['class']}\")\n        axes[i].axis('off')\n\n\n    for j in range(i + 1, len(axes)):\n        fig.delaxes(axes[j])\n\n    plt.tight_layout()\n    plt.show()","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:51.288764Z","iopub.status.busy":"2025-10-13T01:21:51.288535Z","iopub.status.idle":"2025-10-13T01:21:51.292895Z","shell.execute_reply":"2025-10-13T01:21:51.292349Z"},"papermill":{"duration":0.016406,"end_time":"2025-10-13T01:21:51.293931","exception":false,"start_time":"2025-10-13T01:21:51.277525","status":"completed"},"tags":[],"id":"54e24d3a"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"display_images_with_predictions(df, test_dir, num_images=9)","metadata":{"execution":{"iopub.execute_input":"2025-10-13T01:21:51.31605Z","iopub.status.busy":"2025-10-13T01:21:51.315879Z","iopub.status.idle":"2025-10-13T01:21:51.318787Z","shell.execute_reply":"2025-10-13T01:21:51.318209Z"},"papermill":{"duration":0.015047,"end_time":"2025-10-13T01:21:51.319767","exception":false,"start_time":"2025-10-13T01:21:51.30472","status":"completed"},"tags":[],"id":"8672f311","executionInfo":{"status":"ok","timestamp":1760617309747,"user_tz":-60,"elapsed":4367,"user":{"displayName":"Parfait BOTCHI","userId":"13971785570063439557"}},"outputId":"5983e79f-f247-48a2-fb7a-c0a227413c51"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Conclusion Finale\n\nCe notebook a couvert la tâche 3, divisée en deux parties : l'OCR et la détection de falsification.\n\nDans la première partie, nous avons fine-tuné un modèle de vision (`Qwen2.5-VL-7B-Instruct`) pour extraire des informations clés de différentes cartes d'identité avec une bonne précision (validation loss de 0.02), démontrant sa capacité à gérer divers formats et langues.\n\nLa seconde partie s'est concentrée sur la classification des documents comme normaux ou falsifiés en utilisant `EfficientNetV2`. Un modèle unique a été entraîné pour les quatre types de cartes, atteignant une précision de 75.3% sur l'ensemble de validation en 5 heures. Bien que performant, des modèles spécialisés par type de carte pourraient améliorer encore les résultats.\n\nEn somme, les approches adoptées ont montré des performances prometteuses pour l'extraction d'informations et la détection de falsification, mais des améliorations seront apportées dans les prochaines versions.","metadata":{"id":"be8efd3c"}}]}