Updated the README.md

README.md

@@ -1,21 +1,131 @@
 ---
 library_name: litert
+pipeline_tag: image-classification
 tags:
   - vision
   - image-classification
+  - google
+  - computer-vision
 datasets:
   - imagenet-1k
 base_model:
   - google/efficientnet-b2
+model-index:
+  - name: EfficientNet_B2
+    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: 0.8061
+          - name: Top 5 Accuracy (Full Precision)
+            type: accuracy
+            value: 0.9530
+          - name: Top 1 Accuracy (Dynamic Quantized wi8 afp32)
+            type: accuracy
+            value: 0.8012
+          - name: Top 5 Accuracy (Dynamic Quantized wi8 afp32)
+            type: accuracy
+            value: 0.9501
 ---
+
 # EfficientNet B2
 
-EfficientNet B2 model pre-trained on ImageNet-1k.
+EfficientNet B2 model pre-trained on ImageNet-1k. Originally introduced by Tan and Le in the influential paper,[ **EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks**](https://arxiv.org/abs/1905.11946) this model utilizes compound scaling to systematically balance network depth, width, and resolution, enabling superior accuracy with significantly higher efficiency than traditional architectures.
+
+## Model description
+
+The model was converted from a checkpoint from PyTorch Vision. 
+
+The original model has:    
+acc@1 (on ImageNet-1K): 80.608%    
+acc@5 (on ImageNet-1K): 95.31%    
+num_params: 9,109,994
 
 ## 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.
 
+
+## Use
+
+```python
+#!/usr/bin/env python3
+import argparse, 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
+   s = 288
+   if w < h:
+       img = img.resize((s, int(round(h * s / w))), Image.BICUBIC)
+   else:
+       img = img.resize((int(round(w * s / h)), s), Image.BICUBIC)
+   left = (img.size[0] - 288) // 2
+   top = (img.size[1] - 288) // 2
+   img = img.crop((left, top, left + 288, top + 288))
+
+
+   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
+   )
+   return np.transpose(x, (2, 0, 1))
+
+
+def main():
+   ap = argparse.ArgumentParser()
+   ap.add_argument("--image", required=True)
+   args = ap.parse_args()
+
+
+   model_path = hf_hub_download("litert-community/efficientnet_b2", "efficientnet_b2.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