DINOV3训练篇

# 1、创建虚拟环境
conda create -n timm python=3.12 -y
# 2、激活虚拟环境
conda activate timm
# 3、安装timm及其他依赖库
pip install timm==1.0.20 -i https://pypi.mirrors.ustc.edu.cn/simple
# 4、重装PyTorch（Windows）
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
# 5、验证是否安装成功
python -c "import torch; print(torch.cuda.is_available())"

# 1、激活环境
conda activate timm
# 2、设置Huggingface镜像
## Windows
set HF_ENDPOINT=https://hf-mirror.com
## Linux
export HF_ENDPOINT=https://hf-mirror.com
# 3、训练
python classifier-train.py

python classifier-test.py

# 1、激活环境
conda activate timm
# 2、安装matplotlib库
pip install matplotlib==3.10.6 -i https://pypi.mirrors.ustc.edu.cn/simple
# 3、设置Huggingface镜像
## Windows
set HF_ENDPOINT=https://hf-mirror.com
## Linux
export HF_ENDPOINT=https://hf-mirror.com
# 4、训练
python segment-train.py

python segment-test.py

# 下载地址
http://images.cocodataset.org/zips/train2017.zip 
http://images.cocodataset.org/annotations/annotations_trainval2017.zip
http://images.cocodataset.org/zips/val2017.zip 
# 目录结构
data/coco/
├── train2017/          # 训练图像
├── val2017/            # 验证图像
└── annotations/        # 标注
    ├── instances_train2017.json
    └── instances_val2017.json

# 1、激活环境
conda activate timm
# 2、安装pycocotools库
pip install pycocotools==2.0.10 -i https://pypi.mirrors.ustc.edu.cn/simple
# 3、设置Huggingface镜像
## Windows
set HF_ENDPOINT=https://hf-mirror.com
## Linux
export HF_ENDPOINT=https://hf-mirror.com
# 4、训练
python detection-train.py

python detection-test.py

# 1、数据准备
git clone https://github.com/lightly-ai/dataset_clothing_images.git my_data_dir
# 删除数据目录下的.git目录
# Linux执行以下命令，Windows上直接删除.git目录
rm -rf my_data_dir/.git
# 2、安装lightly-train库
conda activate timm
pip install lightly-train -i https://pypi.mirrors.ustc.edu.cn/simple
# 3、蒸馏
python lightly-train-dinov3.py
# 4、微调
python lightly-train-finetun.py
# 5、分类
python lightly-train-infer.py

# 1、创建虚拟环境
conda create -n lightly-train python=3.12 -y
# 2、激活虚拟环境
conda activate lightly-train
# 3、安装PyTorch（Windows）
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
# 4、验证是否安装成功
python -c "import torch; print(torch.cuda.is_available())"
# 5、安装依赖库
pip install lightly-train==0.11.4 matplotlib==3.10.7 -i https://pypi.mirrors.ustc.edu.cn/simple

python lightly-train-detr.py

python lightly-train-segment.py

# 1、创建虚拟环境
conda create -n lightly-train python=3.12 -y
# 2、激活虚拟环境
conda activate lightly-train
# 3、安装PyTorch（Windows）
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
# 4、安装依赖库
pip install lightly-train==0.11.4 ultralytics==8.3.221 -i https://pypi.mirrors.ustc.edu.cn/simple

# 激活虚拟环境
conda activate lightly-train
# 蒸馏
python lightly-train-yolov8s.py

# 激活虚拟环境
conda activate lightly-train
# 查看分类头信息
python yolov8s-dinov3-info.py
# 微调
yolo detect train model=out/my_experiment/exported_models/exported_last.pt data="coco8.yaml"
# 检测
yolo detect predict model=runs/detect/train/weights/best.pt source='test06.png'