169 lines
4.5 KiB
Python
169 lines
4.5 KiB
Python
#!/usr/bin/env python3
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"""
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Configuration file for YOLOv8 French ID Card Detection
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"""
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import os
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from pathlib import Path
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# Base directories
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BASE_DIR = Path(__file__).parent
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DATA_DIR = BASE_DIR / "data"
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LOGS_DIR = BASE_DIR / "logs"
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# Data configuration
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DATA_YAML_PATH = DATA_DIR / "data.yaml"
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# Logging configuration
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TRAINING_LOG_PATH = LOGS_DIR / "training.log"
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INFERENCE_LOG_PATH = LOGS_DIR / "inference.log"
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EVAL_LOG_PATH = LOGS_DIR / "eval.log"
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# Results directories (sử dụng runs từ YOLO)
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INFERENCE_RESULTS_DIR = Path("runs/detect")
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EVALUATION_RESULTS_DIR = Path("runs/val")
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VISUALIZATION_RESULTS_DIR = Path("runs/detect")
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# Default configurations
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DEFAULT_TRAINING_CONFIG = {
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'epochs': 100,
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'batch': 16, # Sửa từ batch_size thành batch
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'imgsz': 640,
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'patience': 50,
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'save_period': 10,
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'device': 'auto',
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'project': 'runs/train',
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'exist_ok': True,
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'pretrained': True,
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'optimizer': 'auto',
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'verbose': False, # Giảm verbose
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'seed': 42,
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'deterministic': True,
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'single_cls': True,
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'rect': False,
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'cos_lr': True,
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'close_mosaic': 10,
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'resume': False,
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'amp': True,
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'fraction': 1.0,
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'cache': False,
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'lr0': 0.01,
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'lrf': 0.01,
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'momentum': 0.937,
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'weight_decay': 0.0005,
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'warmup_epochs': 3.0,
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'warmup_momentum': 0.8,
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'warmup_bias_lr': 0.1,
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'box': 7.5,
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'cls': 0.5,
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'dfl': 1.5,
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'pose': 12.0,
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'kobj': 2.0,
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'label_smoothing': 0.0,
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'nbs': 64,
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'overlap_mask': False, # Tắt mask để tránh tải YOLOv11
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'mask_ratio': 4,
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'dropout': 0.0,
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'val': True,
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'plots': True,
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'save': True,
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'save_json': False,
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'save_hybrid': False,
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'conf': 0.001,
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'iou': 0.6,
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'max_det': 300,
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'half': True,
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'dnn': False,
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'plots': True,
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'source': None,
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'show': False,
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'save_txt': False,
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'save_conf': False,
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'save_crop': False,
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'show_labels': True,
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'show_conf': True,
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'vid_stride': 1,
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'line_thickness': 3,
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'visualize': False,
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'augment': True, # Bật augmentation giống Roboflow
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'hsv_s': 0.61, # Saturation augmentation ~61% (Roboflow: Between -61% and +61%)
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'hsv_h': 0.015, # Hue augmentation
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'hsv_v': 0.4, # Value augmentation
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'degrees': 0.0, # Không xoay ảnh
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'translate': 0.1, # Dịch chuyển nhẹ
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'scale': 0.5, # Scale augmentation
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'shear': 0.0, # Không shear
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'perspective': 0.0, # Không perspective
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'flipud': 0.0, # Không flip vertical
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'fliplr': 0.5, # Flip horizontal 50%
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'mosaic': 1.0, # Bật mosaic augmentation
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'mixup': 0.0, # Không dùng mixup
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'copy_paste': 0.0, # Không copy paste
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'erasing': 0.08,
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'agnostic_nms': False,
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'classes': None,
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'retina_masks': False,
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'boxes': True,
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'format': 'torchscript',
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'keras': False,
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'optimize': False,
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'int8': False,
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'dynamic': False,
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'simplify': False,
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'opset': 17,
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'workspace': 4,
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'nms': False,
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}
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DEFAULT_INFERENCE_CONFIG = {
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'conf_threshold': 0.25,
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'iou_threshold': 0.45,
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'max_det': 300,
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'line_thickness': 3,
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'show_labels': True,
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'show_conf': True,
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}
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def create_directories():
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"""Create all necessary directories"""
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directories = [
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LOGS_DIR,
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]
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for directory in directories:
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directory.mkdir(parents=True, exist_ok=True)
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print("Directories created successfully")
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def get_best_model_path(model_size: str = 'n') -> str:
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"""Get path to best trained model from runs/train"""
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runs_dir = Path('runs/train')
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if not runs_dir.exists():
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return None
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training_runs = list(runs_dir.glob(f'yolov8_{model_size}_french_id_card'))
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if not training_runs:
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return None
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latest_run = max(training_runs, key=lambda x: x.stat().st_mtime)
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best_model_path = latest_run / 'weights' / 'best.pt'
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return str(best_model_path) if best_model_path.exists() else None
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def get_exported_model_path(model_size: str = 'n', format: str = 'onnx') -> str:
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"""Get path to exported model"""
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return str(Path("runs/export") / f"yolov8_{model_size}_french_id_card.{format}")
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def get_latest_training_run():
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"""Get path to latest training run"""
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runs_dir = Path('runs/train')
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if not runs_dir.exists():
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return None
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training_runs = list(runs_dir.glob('yolov8_*_french_id_card'))
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if not training_runs:
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return None
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return max(training_runs, key=lambda x: x.stat().st_mtime)
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if __name__ == '__main__':
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create_directories() |