Add pipeline tag and usage example, remove library_tag

Hi!

This PR improves the model card by:
* Adding the `pipeline_tag: image-classification` to the main metadata block so the model can be found at https://huggingface.co/models?pipeline_tag=image-classification.
* Removing the redundant `library_tag` metadata.
* Adding a sample usage snippet using `timm`.

Feel free to merge if it looks good!

README.md

@@ -1,17 +1,56 @@
 ---
+library_name: timm
+license: mit
 tags:
 - image-classification
 - timm
-library_tag: timm
-license: mit
-library_name: timm
 ---
+
 # LSNet: See Large, Focus Small
 
 Paper: https://arxiv.org/abs/2503.23135
 
 Code: https://github.com/THU-MIG/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
+
 ```bibtex
 @misc{wang2025lsnetlargefocussmall,
       title={LSNet: See Large, Focus Small},