Merge branch 'pr/2'

README.md

@@ -26,13 +26,10 @@ model-index:
             value: 0.8044
 ---
 
-# squeezenet1_0
+# Squeezenet1_0
 
 SqueezeNet 1.0 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360) by Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer.
 
-## Intended uses & limitations
-
-The model files were converted from pretrained weights from PyTorch Vision. The models may have their own licenses or terms and conditions derived from PyTorch Vision and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
 
 ## Model description
 
@@ -43,9 +40,34 @@ acc@1 (on ImageNet-1K): 58.092%
 acc@5 (on ImageNet-1K): 80.420%    
 num_params: 1,248,424   
 
-## Use
+This model is released under the BSD 3-Clause License, inheriting the license of the `torchvision` repository from which it was converted.
+
+
+## Intended uses & limitations
+
+The model files were converted from pretrained weights from PyTorch Vision. The models may have their own licenses or terms and conditions derived from PyTorch Vision and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
+
+The preprocessing script below has been adjusted to handle standard ImageNet resize (256) and central crop (224) requirements. It explicitly strips away the legacy PyTorch `(B, C, H, W)` layout and adds the required Batch dimension to match the LiteRT **`(B, H, W, C)`** (NHWC) runtime expectation. 
+
+## How to Use
+
+​​**1. Install Dependencies** 
+
+Ensure your Python environment is set up with the required libraries. Run the following command in your terminal
+
+```bash 
+pip install numpy Pillow huggingface_hub ai-edge-litert
+```
+
+**2. Prepare Your Image** 
+
+The script expects an image file to analyze. Make sure you have an image (e.g., cat.jpg or car.png) saved in the same working directory as your script.
+
+
+**3. Save the Script** 
+
+Create a new file named `classify.py`, paste the script below into it, and save the file:
 
----
 ```python
 #!/usr/bin/env python3
 import argparse
@@ -117,8 +139,18 @@ def main():
 
 if __name__ == "__main__":
    main()
+```
+
+**4. Execute the Python Script**
+
+Run the below command:
+
+```bash 
+python classify.py --image cat.jpg
+```
+
+### BibTeX entry and citation info
 
-### BibTeX Entry and Citation Info
 ```bibtex
 @misc{iandola2016squeezenetalexnetlevelaccuracy50x,
       title={SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size},