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--- |
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license: agpl-3.0 |
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base_model: SmilingWolf/wd-convnext-tagger-v3 |
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tags: |
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- rknn |
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--- |
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# WD ConvNext Tagger v3 RKNN2 |
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## (English README see below) |
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在RK3588上运行WaifuDiffusion图像标签模型! |
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- 推理速度(RK3588): |
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- 单NPU核: 320ms |
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- 内存占用(RK3588): |
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- 0.45GB |
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## 使用方法 |
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1. 克隆或者下载此仓库到本地 |
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2. 安装依赖 |
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```bash |
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pip install numpy<2 pandas opencv-python rknn-toolkit-lite2 |
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``` |
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3. 运行 |
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```bash |
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python run_rknn.py input.jpg |
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``` |
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输出结果示例: |
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```log |
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tag_id name probs |
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0 9999999 general 0.521484 |
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5 212816 solo 0.929199 |
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12 15080 short_hair 0.520508 |
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25 540830 1boy 0.947754 |
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40 16613 jewelry 0.577148 |
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72 1300281 male_focus 0.907227 |
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130 10926 pants 0.803223 |
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346 1094664 colored_skin 0.570312 |
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373 4009 turtleneck 0.552246 |
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1532 1314823 black_sweater 0.514160 |
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``` |
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## 模型转换 |
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1. 安装依赖 |
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```bash |
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pip install numpy<2 onnxruntime rknn-toolkit2 |
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``` |
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2. 下载原始onnx模型 |
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3. 转换onnx模型到rknn模型: |
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```bash |
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python convert_rknn.py |
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``` |
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## 已知问题 |
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- int8量化后精度损失极大, 基本不可用. 不建议使用量化推理. |
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## 参考 |
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- [SmilingWolf/wd-convnext-tagger-v3](https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3) |
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## English README |
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Run WaifuDiffusion image tagging model on RK3588! |
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- Inference Speed (RK3588): |
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- Single NPU Core: 320ms |
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- Memory Usage (RK3588): |
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- 0.45GB |
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## Usage |
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1. Clone or download this repository |
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2. Install dependencies |
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```bash |
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pip install numpy<2 pandas opencv-python rknn-toolkit-lite2 |
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``` |
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3. Run |
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```bash |
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python run_rknn.py input.jpg |
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``` |
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Output example: |
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```log |
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tag_id name probs |
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0 9999999 general 0.521484 |
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5 212816 solo 0.929199 |
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12 15080 short_hair 0.520508 |
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25 540830 1boy 0.947754 |
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40 16613 jewelry 0.577148 |
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72 1300281 male_focus 0.907227 |
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130 10926 pants 0.803223 |
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346 1094664 colored_skin 0.570312 |
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373 4009 turtleneck 0.552246 |
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1532 1314823 black_sweater 0.514160 |
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``` |
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## Model Conversion |
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1. Install dependencies |
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```bash |
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pip install numpy<2 onnxruntime rknn-toolkit2 |
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``` |
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2. Download original onnx model |
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3. Convert onnx model to rknn model: |
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```bash |
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python convert_rknn.py |
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``` |
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## Known Issues |
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- Huge precision loss after int8 quantization, not recommended to use quantized inference. |
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## References |
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- [SmilingWolf/wd-convnext-tagger-v3](https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3) |
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