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

README.md

@@ -1,8 +1,146 @@
 ---
 library_name: litert
+pipeline_tag: image-classification
 tags:
   - vision
   - image-classification
+  - google
+  - computer-vision
 datasets:
   - imagenet-1k
+model-index:
+  - name: litert-community/vgg19
+    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: 0.7238
+          - name: Top 5 Accuracy (Full Precision)
+            type: accuracy
+            value: 0.9089
 ---
+
+# VGG19
+
+VGG19 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 19-layer architecture represents the deepest configuration of the VGG series, utilizing an extensive stack of \\(3 \times 3 \\) convolutional layers to extract increasingly complex features and achieve high accuracy on large-scale visual recognition tasks.
+
+## Model description
+
+The model was converted from a checkpoint from PyTorch Vision. 
+
+The original model has:    
+acc@1 (on ImageNet-1K): 72.376%    
+acc@5 (on ImageNet-1K): 90.876%    
+num_params: 143667240
+
+
+## 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.
+
+
+## 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, 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/vgg19", "vgg19.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{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}, 
+}
+```