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

README.md

@@ -1,8 +1,125 @@
 ---
 library_name: litert
+pipeline_tag: image-classification
 tags:
   - vision
   - image-classification
+  - google
+  - computer-vision
 datasets:
   - imagenet-1k
----
+model-index:
+  - name: ResNet34
+    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: 0.7330
+          - name: Top 5 Accuracy (Full Precision)
+            type: accuracy
+            value: 0.9142
+          - name: Top 1 Accuracy (Dynamic Quantized wi8 afp32)
+            type: accuracy
+            value: 0.7328
+          - name: Top 5 Accuracy (Dynamic Quantized wi8 afp32)
+            type: accuracy
+            value:  0.9141
+---
+
+# ResNet 34
+
+The ResNet-34 architecture is a convolutional neural network pre-trained on the ImageNet-1k dataset. Originally introduced by He et al. in the landmark paper, [**Deep Residual Learning for Image Recognition**](https://arxiv.org/pdf/1512.03385), this model utilizes residual mapping to overcome the vanishing gradient problem, enabling the training of substantially deeper networks.
+
+
+## Model description
+
+The model was converted from a checkpoint from PyTorch Vision. 
+
+The original model has:    
+acc@1 (on ImageNet-1K): 73.314%    
+acc@5 (on ImageNet-1K): 91.42%    
+num_params: 21,797,672  
+
+## 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 = 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)
+   left = (img.size[0] - 224) // 2
+   top = (img.size[1] - 224) // 2
+   img = img.crop((left, top, left + 224, top + 224))
+
+   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/resnet34", "resnet34.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
+@inproceedings{he2016deep, 
+title={Deep residual learning for image recognition}, 
+author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, 
+booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, 
+year={2016} 
+}
+```