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

README.md

@@ -1,8 +1,122 @@
 ---
-library_name: litert
+pipeline_tag: image-classification
 tags:
-  - vision
-  - image-classification
+- vision
+- image-classification
+- google
+- computer-vision
 datasets:
-  - imagenet-1k
+- imagenet-1k
+model-index:
+- name: litert-community/vgg11
+  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: 0.6897
+    - name: Top 5 Accuracy (Full Precision)
+      type: accuracy
+      value: 0.8865
 ---
+
+# VGG11
+
+VGG11 model pre-trained on ImageNet-1k. Originally introduced by Karen Simonyan and Andrew Zisserman in the influential paper, [**Very Deep Convolutional Networks for Large-Scale Image Recognition**](https://arxiv.org/abs/1409.1556) this configuration serves as the base 8-layer convolutional architecture (plus 3 fully connected layers) that proved the effectiveness of using small $3 \times 3$ filters to build deep networks while maintaining a manageable number of parameters.
+
+## 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. 
+
+The original model has:    
+acc@1 (on ImageNet-1K): 69.02%    
+acc@5 (on ImageNet-1K): 88.628%    
+num_params: 132863336
+    
+The license information of the original model was missing.
+
+## 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.expand_dims(x, axis=0)
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--image", required=True)
+    args = ap.parse_args()
+
+    model_path = hf_hub_download("litert-community/vgg11", "vgg11.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{simonyan2015deepconvolutionalnetworkslargescale,
+      title={Very Deep Convolutional Networks for Large-Scale Image Recognition}, 
+      author={Karen Simonyan and Andrew Zisserman},
+      year={2015},
+      eprint={1409.1556},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/1409.1556}, 
+}
+```