1. 概述

训练一个服饰的分类器，这里采用yolo11x的神经网络架构训练。

2. 数据集准备

2.1. 公开数据集

DeepFashion2

https://github.com/switchablenorms/DeepFashion2

https://github.com/switchablenorms/DeepFashion2/blob/master/evaluation/deepfashion2_to_coco.py

Coco

https://cocodataset.org

https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco128.yaml

2.2. 数据标注工具

• roboflow数据标注工具，可以export yolo格式数据集
  https://roboflow.com/

• Refine high-quality datasets and visual AI models
  https://github.com/voxel51/fiftyone
  https://voxel51.com/

本文采用一个私有数据集，一共7w+图片，按照6:2:2分为训练数据集、验证数据集和测试数据集。

3. 训练

3.1. 训练代码

from ultralytics import YOLO
from ultralytics import settings
import wandb
from wandb.integration.ultralytics import add_wandb_callback

settings.update(dict(
    datasets_dir="/data/yolo/dataset/yolo",
    weights_dir="/data/yolo/dataset/yolo/weights",
    runs_dir="/data/yolo/dataset/yolo/runs",
    wandb=True,
    comet=False,
    clearml=False,
    tensorboard=False,
))
project = "fashion-detector-19cls"

# Load a model
model = YOLO("yolo11x.pt")
# model = YOLO("fashion-detector-19cls/yolo11x/weights/last.pt")
add_wandb_callback(model, enable_model_checkpointing=True)

# Train the model
data_config = "dataset/yolo/haier_fashion.yaml"
results = model.train(
    data=data_config, epochs=100,
    imgsz=640, device=[2,3,4], batch=60,
    project=project, name="yolo11x",
    # resume=True
)

注意：上述代码是预训练，如果做继续训练，只需要加载续训练model和resume=True即可。

3.2. WandB训练过程指标分析

Epoch=100的训练结果

Metric	Value
lr/pg0	0.0002
lr/pg1	0.0002
lr/pg2	0.0002
metrics/mAP50(B)	0.83648
metrics/mAP50-95(B)	0.6932
metrics/precision(B)	0.81303
metrics/recall(B)	0.7881
train/box_loss	0.45027
train/cls_loss	0.23466
train/dfl_loss	0.96325
val/box_loss	0.65851
val/cls_loss	0.49071
val/dfl_loss	1.08395

wandb metrics

4. 测试

• 服饰类别一共19个

Dress Coat Top Jacket Skirt Suspender Short Pant Swim-Suit Shoe Cap Glass Watch Bag Belt Glove Scarf Jewelry Non-Fashion

• 对照Test数据集与预测结果

注意：正常情况下，需要进行模型评估，通过mAP、Precission、Recall等指标评价模型的性能，尤其是泛化能力。这里先不做了。

5. 脚本说明

5.1. 准备数据

python -m "scripts.data_prepare"

5.2. 训练

python train.py

5.3. 预测

python predict.py
or
python check_data.py