4.訓練/建模

Colab

https://drive.google.com/file/d/1bUt5F33b1mQtRzUdxJ0W2vYfL6URHFoH/view?usp=sharing

官方 CLI

# Start training from a pretrained *.pt modelyolo classify train data=cifar10 model=yolov8n-cls.pt epochs=10 imgsz=32

Python - train.py

#!/usr/bin/env python3# -*- coding: utf-8 -*-"""Created on Wed Apr 1 15:44:47 2026
Usage：python 2_train_cls.py \  --data /data/cls/my_data_path \  --model yolo11m-cls.pt \  --project runs_cls \  --name exp_v2 \  --epochs 100 \  --batch 64 \  --device 0  Input/path/to/dataset
"""
import argparsefrom ultralytics import YOLOimport torch
# =============================================================================# ★ 預設值（CLI 未指定時使用）# =============================================================================SRC_MODEL = "/path/to/yolo11n-cls.pt"DATASET = "/path/to/dataset"PROJECT_DIR = "runs_cls"EXP_NAME = "cls_exp1"EPOCHS = 3IMG_SIZE = 224BATCH_SIZE = 16DEVICE = "mps" # Device: 0,1,... for CUDA, 'cpu' or 'mps' for Apple GPU"WORKERS = 4 # Dataloader workersLR_RATE = 0.01 # Initial learning ratePATIENCE = 10 # Early stopping patience
def parse_args():    parser = argparse.ArgumentParser(description="YOLO11 Classification Training CLI")    parser.add_argument("--data", type=str, default=DATASET)    parser.add_argument("--model", type=str, default=SRC_MODEL)    parser.add_argument("--project", type=str, default=PROJECT_DIR)    parser.add_argument("--name", type=str, default=EXP_NAME)    parser.add_argument("--epochs", type=int, default=EPOCHS)    parser.add_argument("--imgsz", type=int, default=IMG_SIZE)    parser.add_argument("--batch", type=int, default=BATCH_SIZE)    parser.add_argument("--device", type=str, default=DEVICE)    parser.add_argument("--workers", type=int, default=WORKERS)    parser.add_argument("--lr0", type=float, default=LR_RATE)    parser.add_argument("--patience", type=int, default=PATIENCE)    return parser.parse_args()

def main():    args = parse_args()
    # 自動檢查 Apple MPS / CUDA    if args.device.lower() == "mps":        device_str = "mps" if torch.backends.mps.is_available() else "cpu"    elif args.device.isdigit():        device_str = int(args.device) if torch.cuda.is_available() else "cpu"    else:        device_str = "cpu"
    print("===== YOLO11-CLS Training Config =====")    for k, v in vars(args).items():        print(f"{k}: {v}")    print(f"Using device: {device_str}")    print("======================================")
    # 載入模型    model = YOLO(args.model)
    # 開始訓練    model.train(        data=args.data,        epochs=args.epochs,        imgsz=args.imgsz,        batch=args.batch,        device=device_str,        workers=args.workers,        lr0=args.lr0,        patience=args.patience,        project=args.project,        name=args.name    )

if __name__ == "__main__":    main()

訓練 + Augmentation

"""
Function:    訓練 + Augmentation"""
import argparsefrom ultralytics import YOLOimport torchimport os
# =============================================================================# ★ 預設值（CLI 未指定時使用）# =============================================================================SRC_MODEL = "/path/to/model.pt"DATASET = "/path/to/dataset"PROJECT_DIR = "runs_cls"EXP_NAME = "cls_aug"EPOCHS = 3IMG_SIZE = 224BATCH_SIZE = 16DEVICE = "mps" # Device: 0,1,... for CUDA, 'cpu' or 'mps' for Apple GPU"WORKERS = 4 # Dataloader workersLR_RATE = 0.01 # Initial learning ratePATIENCE = 10 # Early stopping patience
def parse_args():    parser = argparse.ArgumentParser(description="YOLO11 Classification Training CLI with AutoAugmentation")
    # 基本參數    parser.add_argument("--data", type=str, default=DATASET, help="Path to dataset directory")    parser.add_argument("--model", type=str, default=SRC_MODEL, help="Model path")    parser.add_argument("--project", type=str, default=PROJECT_DIR, help="Project folder")    parser.add_argument("--name", type=str, default=EXP_NAME, help="Experiment name")    parser.add_argument("--epochs", type=int, default=EPOCHS, help="Training epochs")    parser.add_argument("--imgsz", type=int, default=IMG_SIZE, help="Image size")    parser.add_argument("--batch", type=int, default=BATCH_SIZE, help="Batch size")    parser.add_argument("--device", type=str, default=DEVICE, help="Device: 0,1,... for CUDA, 'cpu' or 'mps'")    parser.add_argument("--workers", type=int, default=WORKERS, help="Dataloader workers")    parser.add_argument("--lr0", type=float, default=LR_RATE, help="Initial learning rate")    parser.add_argument("--patience", type=int, default=PATIENCE, help="Early stopping patience")
    # Data Augmentation 參數    parser.add_argument("--augment", type=bool, default=True, help="Enable data augmentation")    parser.add_argument("--degrees", type=float, default=10.0, help="Random rotation ± degrees")    parser.add_argument("--translate", type=float, default=0.1, help="Random translation fraction")    parser.add_argument("--scale", type=float, default=0.1, help="Random scaling fraction")    parser.add_argument("--shear", type=float, default=2.0, help="Random shear angle")    parser.add_argument("--perspective", type=float, default=0.0, help="Random perspective fraction")    parser.add_argument("--hsv_h", type=float, default=0.015, help="Hue augmentation")    parser.add_argument("--hsv_s", type=float, default=0.7, help="Saturation augmentation")    parser.add_argument("--hsv_v", type=float, default=0.4, help="Value augmentation")
    return parser.parse_args()
def main():    args = parse_args()
    # 選擇 device    if args.device.lower() == "mps":        device_str = "mps" if torch.backends.mps.is_available() else "cpu"    elif args.device.isdigit():        device_str = int(args.device) if torch.cuda.is_available() else "cpu"    else:        device_str = "cpu"
    print("===== YOLO11-CLS Training Config =====")    for k, v in vars(args).items():        print(f"{k}: {v}")    print(f"Using device: {device_str}")    print("======================================")
    # 載入模型    model = YOLO(args.model)
    # 開始訓練    model.train(        data=args.data,        epochs=args.epochs,        imgsz=args.imgsz,        batch=args.batch,        device=device_str,        workers=args.workers,        lr0=args.lr0,        patience=args.patience,        project=args.project,        name=args.name,        augment=args.augment,        degrees=args.degrees,        translate=args.translate,        scale=args.scale,        shear=args.shear,        perspective=args.perspective,        hsv_h=args.hsv_h,        hsv_s=args.hsv_s,        hsv_v=args.hsv_v    )
if __name__ == "__main__":    main()

Disable Augmentations

#!/usr/bin/env python3# -*- coding: utf-8 -*-"""Created on Wed Apr 1 16:57:05 2026
Spyder 友善版本：直接修改下方預設參數後執行，無需 CLI 參數。
Usage：    在 Spyder 中直接按 F5 或 Run 執行即可。
Input/path/to/dataset
Function:    訓練（無任何資料增強）"""
from ultralytics import YOLOimport torchimport os
# =============================================================================# ★ 預設參數（直接在此修改）# =============================================================================SRC_MODEL = "/path/to/model.pt"DATASET = "/path/to/dataset"PROJECT_DIR = "runs_cls"EPOCHS = 3BATCH_SIZE = 16DEVICE = "mps" # "mps" | "cpu" | "0" | "0,1" (CUDA GPU index)WORKERS = 4LR_RATE = 0.001PATIENCE = 30
# imgsz：正方形用單一整數，非正方形用 [H, W], 例如IMGSZ = [IMG_HEIGHT, IMG_WIDTH]IMGSZ       = 224
# 自動產生實驗名稱dir_name = os.path.basename(DATASET)EXP_NAME = f"{dir_name}_{IMG_WIDTH}x{IMG_HEIGHT}_{EPOCHS}"

# =============================================================================# ★ 主程式# =============================================================================
# 選擇 deviceif DEVICE.lower() == "mps":    device_str = "mps" if torch.backends.mps.is_available() else "cpu"elif DEVICE.isdigit():    device_str = int(DEVICE) if torch.cuda.is_available() else "cpu"else:    device_str = "cpu"
print("===== YOLO11-CLS Training Config =====")print(f"model : {SRC_MODEL}")print(f"data : {DATASET}")print(f"project : {PROJECT_DIR}")print(f"name : {EXP_NAME}")print(f"epochs : {EPOCHS}")print(f"imgsz : {IMGSZ}")print(f"batch : {BATCH_SIZE}")print(f"device : {device_str}")print(f"workers : {WORKERS}")print(f"lr0 : {LR_RATE}")print(f"patience: {PATIENCE}")print("augment : DISABLED (all augmentation parameters set to 0)")print("======================================")
# 載入模型model = YOLO(SRC_MODEL)
# 開始訓練 - 完全關閉所有資料增強model.train(    data = DATASET,    epochs = EPOCHS,    imgsz = IMGSZ,    batch = BATCH_SIZE,    device = device_str,    workers = WORKERS,    lr0 = LR_RATE,    patience = PATIENCE,    project = PROJECT_DIR,    name = EXP_NAME,
    # --- 關閉所有資料增強 ---    augment = False, # 關閉內建增強模式    hsv_h = 0.0, # 色相    hsv_s = 0.0, # 飽和度    hsv_v = 0.0, # 明度    degrees = 0.0, # 旋轉    translate = 0.0, # 平移    scale = 0.0, # 縮放    shear = 0.0, # 剪切    perspective = 0.0, # 透視    flipud = 0.0, # 上下翻轉    fliplr = 0.0, # 左右翻轉    bgr = 0.0, # BGR 通道翻轉    mosaic = 0.0, # Mosaic    mixup = 0.0, # Mixup    cutmix = 0.0, # CutMix    copy_paste = 0.0, # Copy-Paste    auto_augment = None, # ★ 關閉 RandAugment/AutoAugment（預設 randaugment，造成幾何變換）    erasing = 0.0, # ★ 關閉隨機擦除（預設 0.4，造成黑色方塊遮擋）)