115 lines
3.4 KiB
Python
115 lines
3.4 KiB
Python
import time
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import torch
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from ultralytics import YOLO
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# ===== Model Configuration =====
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MODELS = [
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{"name": "YOLOv8x", "model_path": "weights/yolov8x.pt", "epochs": 100},
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{"name": "YOLOv9e", "model_path": "weights/yolov9e.pt", "epochs": 100},
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{"name": "YOLOv10x", "model_path": "weights/yolov10x.pt", "epochs": 100},
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{"name": "YOLO11x", "model_path": "weights/yolo11x.pt", "epochs": 100},
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{"name": "YOLO12x", "model_path": "weights/yolo12x.pt", "epochs": 100},
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]
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# ===== Model Training Function =====
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def train_model(model_config, dataset_yaml="dataset/tinyperson.yaml"):
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print(f"\n{"="*60}")
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print(f"Training {model_config['name']} on TinyPerson dataset")
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print(f"{"="*60}\n")
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# ----- Load Model -----
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model = YOLO(model_config["model_path"])
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# ----- Configure Training Parameters -----
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hyperparams = {
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"data": dataset_yaml,
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"epochs": model_config["epochs"],
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"imgsz": 640,
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"batch": 0.90,
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"device": 0 if torch.cuda.is_available() else "cpu",
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"workers": 8,
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"lr0": 0.01,
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"lrf": 0.001,
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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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"project": "results",
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"name": model_config["name"],
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"exist_ok": True,
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"patience": 50,
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"optimizer": "SGD",
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"cos_lr": True,
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"box": 7.5,
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"cls": 0.5,
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"hsv_h": 0.015,
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"hsv_s": 0.7,
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"hsv_v": 0.4,
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"fliplr": 0.5,
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"mosaic": 0.0,
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"mixup": 0.0,
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"scale": 0.3,
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"rect": False,
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"save": True,
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"save_period": 10,
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}
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# ----- Execute Training -----
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start_time = time.time()
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results = model.train(**hyperparams)
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# ----- Report Training Results -----
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duration = time.time() - start_time
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hours, remainder = divmod(duration, 3600)
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minutes, seconds = divmod(remainder, 60)
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print(f"\n[✓] Training completed in {int(hours)}h {int(minutes)}m {int(seconds)}s")
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output_path = f"results/{model_config['name']}/weights/best.pt"
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print(f"[i] Model saved to {output_path}")
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return str(output_path)
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# ===== Main Training Execution =====
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def main():
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trained_models = {}
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start_time = time.time()
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print("\n" + "=" * 60)
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print("YOLO-TinyPerson Training")
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print("=" * 60 + "\n")
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# ----- Check GPU Availability -----
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if torch.cuda.is_available():
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gpu_name = torch.cuda.get_device_name(0)
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gpu_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3)
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print(f"[i] Training on GPU: {gpu_name} with {gpu_memory:.2f} GB memory")
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else:
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print(
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"[!] No GPU available. Training on CPU (not recommended for YOLO training)"
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)
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# ----- Train Each Model -----
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for model_config in MODELS:
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model_path = train_model(model_config)
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trained_models[model_config["name"]] = model_path
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# ----- Training Summary -----
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total_duration = time.time() - start_time
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hours, remainder = divmod(total_duration, 3600)
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minutes, seconds = divmod(remainder, 60)
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print(f"\n{"="*60}")
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print(f"[✓] All models trained in {int(hours)}h {int(minutes)}m {int(seconds)}s")
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print(f"{"="*60}")
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print("\n[i] Trained Models Summary:")
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print("-" * 60)
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for name, path in trained_models.items():
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print(f"[✓] {name}: {path}")
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print("-" * 60)
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# ===== Script Entry Point =====
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if __name__ == "__main__":
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main()
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