Enhanced the README.md by adding tested code along with relevant metrics

README.md

@@ -1,8 +1,132 @@
 ---
 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.
+
+## 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
+
+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   
+
+## Use
+
+---
+```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()
+
+### 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}, 
+}
+```