Update README.md

README.md

@@ -10,7 +10,7 @@ Experimental onnx conversion of kaloscope model.
 [🤗 Space Demo](https://huggingface.co/spaces/DraconicDragon/Kaloscope-artist-style-classifier) for ONNX & PyTorch inference implementation (incl. timm+lsnet; OpenVINO accelerate CPU inference; no Triton required - refer to [ska.py](https://huggingface.co/spaces/DraconicDragon/Kaloscope-artist-style-classifier/blob/main/lsnet/ska.py) or [here](https://github.com/spawner1145/comfyui-lsnet/pull/2))  
 Barebones ONNX inference script (no timm or lsnet; scores are a tiny bit different - probably different img preprocessing): [onnx_barebones_inference.py](https://huggingface.co/DraconicDragon/Kaloscope-onnx-ema/blob/main/onnx_barebones_inference.py)
 
-- `lsnet_xl_artist-dynamo-opset18_merged.onnx`: dynamo=True, dynamic_axes=None, opset_version=None (torch 2.8.0 used here defaults to 18 when None), optimization/constant folding enabled
+- `kaloscope_1-0.onnx`: Exported from `best_checkpoint.pth` original Kaloscope release | dynamo=True, dynamic_axes=None, opset_version=None (torch 2.8.0 used here defaults to 18 when None), optimization/constant folding enabled
   - 'merged' in name because torch.onnx.export gave me 2 files - .onnx.data and a small .onnx file - I used [merge_onnx_ext_data.py](https://huggingface.co/DraconicDragon/Kaloscope-onnx-ema/blob/main/merge_onnx_ext_data.py) to merge them (`external_data=False` can be set for torch.onnx.export I think but didn't use it)  
 
 Conversion script used: [kaloscope_pth2onnx.py](https://huggingface.co/DraconicDragon/Kaloscope-onnx-ema/blob/main/convert_scripts/kaloscope_pth2onnx.py) | Related Info: [README.md](https://huggingface.co/DraconicDragon/Kaloscope-onnx-ema/blob/main/convert_scripts/README.md)  
@@ -100,11 +100,9 @@ pip install torch torchvision timm
 ```python
 import torch
 from timm.models import create_model
-
 # 加载模型
 model = create_model('lsnet_t_artist', pretrained=True, num_classes=31770)
 model.eval()
-
 # 推理
 with torch.no_grad():
     output = model(input_tensor)
@@ -138,7 +136,9 @@ with torch.no_grad():
 - 基于Danbooru数据集训练
 - 支持31,770个艺术家类别
 - 达到84.2%的分类准确率
-
+### v1.1 (2025年10月)
+- 150epoch
+- 达到85.6%的分类准确率
 ---
 
 **免责声明**: 本模型仅供研究和教育用途。在商业应用中使用时，请确保遵守相关法律法规和伦理准则。