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

README.md

@@ -1,32 +1,37 @@
 ---
 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/MobileNet-v3-large
-  results:
-  - task:
-      type: image-classification
-      name: Image Classification
-    dataset:
-      name: ImageNet-1K
-      type: imagenet-1k
-      split: validation
-      args:
-        split: validation
-    metrics:
-    - type: accuracy
-      value: 0.75292
-      name: Top-1 Accuracy
+  - name: litert-community/MobileNet-v3-large
+    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: 0.7523
+          - name: Top 5 Accuracy (Full Precision)
+            type: accuracy
+            value: 0.9258
 ---
 
 # MobileNet V3 Large
 
 
-MobileNet V3 model pre-trained on ImageNet-1k at resolution 224x224
+MobileNet V3 Large model pre-trained on ImageNet-1k at resolution 224x224. Originally introduced by Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, and Hartwig Adam in the paper, [**Searching for MobileNetV3**](https://arxiv.org/abs/1905.02244).
 
 
 ## Model description
@@ -37,6 +42,7 @@ The original model has:
 acc@1 (on ImageNet-1K): 75.274%    
 acc@5 (on ImageNet-1K): 92.566%    
 num_params: 5,483,032 
+
 The license information of the original model was missing.
 
 
@@ -44,6 +50,85 @@ The license information of the original model was missing.
 
 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 = 232
+   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 x
+
+def main():
+   ap = argparse.ArgumentParser()
+   ap.add_argument("--image", required=True)
+   args = ap.parse_args()
+
+   model_path = hf_hub_download("litert-community/MobileNet-v3-large", "mobilenet_v3_large.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
@@ -71,4 +156,4 @@ The model files were converted from pretrained weights from PyTorch Vision. The
   biburl       = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
   bibsource    = {dblp computer science bibliography, https://dblp.org}
 }
-```
+```