Update README.md

README.md

@@ -4,7 +4,6 @@ license: mit
 tags:
 - image-classification
 - timm
-library_tag: timm
 pipeline_tag: image-classification
 ---
 
@@ -14,6 +13,45 @@ Paper: https://arxiv.org/abs/2503.23135
 
 Code: https://github.com/jameslahm/lsnet
 
+## Usage
+
+```python
+import timm
+import torch
+from PIL import Image
+import requests
+from timm.data import resolve_data_config, create_transform
+
+# Load the model
+model = timm.create_model(
+    'hf_hub:jameslahm/lsnet_b',
+    pretrained=True
+)
+model.eval()
+
+# Load and transform image
+# Example using a URL:
+url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+img = Image.open(requests.get(url, stream=True).raw)
+
+config = resolve_data_config({}, model=model)
+transform = create_transform(**config)
+input_tensor = transform(img).unsqueeze(0) # transform and add batch dimension
+
+# Make prediction
+with torch.no_grad():
+    output = model(input_tensor)
+probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+# Get top 5 predictions
+top5_prob, top5_catid = torch.topk(probabilities, 5)
+# Assuming you have imagenet labels list 'imagenet_labels'
+# for i in range(top5_prob.size(0)):
+#     print(imagenet_labels[top5_catid[i]], top5_prob[i].item())
+```
+
+## Citation
+If our code or models help your work, please cite our paper:
 ```bibtex
 @misc{wang2025lsnetlargefocussmall,
       title={LSNet: See Large, Focus Small}, 
@@ -32,7 +70,7 @@ Code: https://github.com/jameslahm/lsnet
 Official PyTorch implementation of **LSNet**. CVPR 2025.
 
 <p align="center">
-  <img src="figures/throughput.svg" width=60%> <br>
+  <img src="https://raw.githubusercontent.com/THU-MIG/lsnet/refs/heads/master/figures/throughput.svg" width=60%> <br>
   Models are trained on ImageNet-1K and the throughput
  is tested on a Nvidia RTX3090.
 </p>
@@ -103,9 +141,9 @@ model = timm.create_model(
 ```
 
 ## Downstream Tasks
-[Object Detection and Instance Segmentation](detection/README.md)<br>
-[Semantic Segmentation](segmentation/README.md)<br>
-[Robustness Evaluation](README_robustness.md)
+[Object Detection and Instance Segmentation](https://github.com/THU-MIG/lsnet/blob/master/detection/README.md)<br>
+[Semantic Segmentation](https://github.com/THU-MIG/lsnet/blob/master/segmentation/README.md)<br>
+[Robustness Evaluation](https://github.com/THU-MIG/lsnet/blob/master/README_robustness.md)
 
 ## Acknowledgement
 
@@ -113,19 +151,4 @@ Classification (ImageNet) code base is partly built with [EfficientViT](https://
 
 The detection and segmentation pipeline is from [MMCV](https://github.com/open-mmlab/mmcv) ([MMDetection](https://github.com/open-mmlab/mmdetection) and [MMSegmentation](https://github.com/open-mmlab/mmsegmentation)). 
 
-Thanks for the great implementations! 
-
-## Citation
-
-If our code or models help your work, please cite our paper:
-```BibTeX
-@misc{wang2025lsnetlargefocussmall,
-      title={LSNet: See Large, Focus Small}, 
-      author={Ao Wang and Hui Chen and Zijia Lin and Jungong Han and Guiguang Ding},
-      year={2025},
-      eprint={2503.23135},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2503.23135}, 
-}
-```
+Thanks for the great implementations!