Update the README.md

README.md

@@ -1,8 +1,125 @@
 ---
 library_name: litert
+pipeline_tag: image-classification
 tags:
   - vision
   - image-classification
+  - computer-vision
 datasets:
   - imagenet-1k
+model-index:
+  - name: inception_v3
+    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: 0.7727
+          - name: Top 5 Accuracy (Full Precision)
+            type: accuracy
+            value: 0.9343
 ---
+
+# Inception_v3
+
+Inception v3 model pre-trained on ImageNet-1k. It was introduced in [**Rethinking the Inception Architecture for Computer Vision**](https://arxiv.org/abs/1512.00567) by Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna.
+
+## 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 [`Inception_V3_Weights.IMAGENET1K_V1`](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.inception_v3.html#torchvision.models.Inception_V3_Weights). 
+
+The original model has:    
+acc@1 (on ImageNet-1K): 77.294%    
+acc@5 (on ImageNet-1K): 93.450%    
+num_params: 27,161,264   
+
+## 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
+    # Inception_v3 expects a resize to 342 prior to the 299 central crop
+    s = 342
+    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)
+        
+    # Central crop to 299x299
+    left = (img.size[0] - 299) // 2
+    top = (img.size[1] - 299) // 2
+    img = img.crop((left, top, left + 299, top + 299))
+    
+    # Rescale to [0.0, 1.0] and Normalize
+    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()
+
+    # Download the TFLite model and labels
+    model_path = hf_hub_download("litert-community/inception_v3", "inception_v3.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{szegedy2016rethinking,
+  title={Rethinking the inception architecture for computer vision},
+  author={Szegedy, Christian and Vanhoucke, Vincent and Ioffe, Sergey and Shlens, Jon and Wojna, Zbigniew},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={2818--2826},
+  year={2016}
+}
+```