Update README: Add model card metadata, ImageNet-1k metrics, and LiteRT usage example

README.md

@@ -1,8 +1,164 @@
 ---
 library_name: litert
+pipeline_tag: image-classification
 tags:
   - vision
   - image-classification
 datasets:
   - imagenet-1k
+model-index:
+  - name: squeezenet1_0
+    results:
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: ImageNet-1k
+          type: imagenet-1k
+          config: default
+          split: validation
+        metrics:
+          - name: Top 1 Accuracy (Full Precision)
+            type: accuracy
+            value: 0.5811
+          - name: Top 5 Accuracy (Full Precision)
+            type: accuracy
+            value: 0.8044
 ---
+
+# 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.
+
+
+## Model description
+
+The model was converted from a checkpoint from PyTorch Vision (`SqueezeNet1_0_Weights.IMAGENET1K_V1`). 
+
+The original model has:    
+acc@1 (on ImageNet-1K): 58.092%    
+acc@5 (on ImageNet-1K): 80.420%    
+num_params: 1,248,424   
+
+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
+import json
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+from ai_edge_litert.compiled_model import CompiledModel
+
+def preprocess(img: Image.Image) -> np.ndarray:
+   img = img.convert("RGB")
+   w, h = img.size
+
+   # Resize shortest edge to 256
+   s = 256
+   if w < h:
+       img = img.resize((s, int(round(h * s / w))), Image.BILINEAR)
+   else:
+       img = img.resize((int(round(w * s / h)), s), Image.BILINEAR)
+
+   # Central crop to 224x224
+   left = (img.size[0] - 224) // 2
+   top = (img.size[1] - 224) // 2
+   img = img.crop((left, top, left + 224, top + 224))
+
+   # Rescale to [0.0, 1.0] and Normalize
+   x = np.asarray(img, dtype=np.float32) / 255.0
+   x = (x - np.array([0.485, 0.456, 0.406], dtype=np.float32)) / np.array(
+       [0.229, 0.224, 0.225], dtype=np.float32
+   )
+
+   # Expand dimensions to create NHWC 4D tensor: (1, 224, 224, 3)
+   x = np.expand_dims(x, axis=0)
+
+   return x
+
+def main():
+   ap = argparse.ArgumentParser()
+   ap.add_argument("--image", required=True, help="Path to the input image")
+   args = ap.parse_args()
+
+   # Download the TFLite model and labels
+   model_path = hf_hub_download("litert-community/squeezenet1_0", "squeezenet1_0.tflite")
+   labels_path = hf_hub_download(
+       "huggingface/label-files", "imagenet-1k-id2label.json", repo_type="dataset"
+   )
+
+   with open(labels_path, "r", encoding="utf-8") as f:
+       id2label = {int(k): v for k, v in json.load(f).items()}
+
+   img = Image.open(args.image)
+   x = preprocess(img)
+
+   model = CompiledModel.from_file(model_path)
+   inp = model.create_input_buffers(0)
+   out = model.create_output_buffers(0)
+
+   inp[0].write(x)
+   model.run_by_index(0, inp, out)
+
+   req = model.get_output_buffer_requirements(0, 0)
+   y = out[0].read(req["buffer_size"] // np.dtype(np.float32).itemsize, np.float32)
+
+   pred = int(np.argmax(y))
+   label = id2label.get(pred, f"class_{pred}")
+
+   print(f"Top-1 class index: {pred}")
+   print(f"Top-1 label: {label}")
+
+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
+@misc{iandola2016squeezenetalexnetlevelaccuracy50x,
+      title={SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size}, 
+      author={Forrest N. Iandola and Song Han and Matthew W. Moskewicz and Khalid Ashraf and William J. Dally and Kurt Keutzer},
+      year={2016},
+      eprint={1602.07360},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/1602.07360}, 
+}
+```