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README.md

@@ -1,23 +1,148 @@
 ---
 library_name: litert
+pipeline_tag: image-classification
 tags:
-  - litert
-  - tflite
   - vision
   - image-classification
+  - google
+  - computer-vision
 datasets:
   - imagenet-1k
+model-index:
+  - name: litert-community/convnext_tiny
+    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: 0.8246
+          - name: Top 5 Accuracy (Full Precision)
+            type: accuracy
+            value: 0.9613
 ---
 
-# convnext_tiny
+# Convnext Tiny
 
-LiteRT TFLite conversion of the TorchVision `convnext_tiny` model.
+ConvNeXt Tiny model designed as a lightweight, pure convolutional backbone for efficient visual recognition in the "Roaring 20s." Originally introduced by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. in the modernized paper, [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545), this model "modernizes" the standard ResNet by adopting Transformer-inspired inductive biases, such as depthwise convolutions with \\(7 \times 7 \\) kernels and inverted bottlenecks. With approximately 28M parameters and 4.5 GFLOPs, it achieves accuracy levels comparable to the Swin-T Transformer while maintaining the simplicity and high throughput of a standard ConvNet.
 
-## Files
+## Model description
 
-- `convnext_tiny.tflite`
-- `convnext_tiny_dynamic_wi8_afp32.tflite`
+The model was converted from a checkpoint from PyTorch Vision. 
 
-## Notes
+The original model has:    
+acc@1 (on ImageNet-1K): 82.52%    
+acc@5 (on ImageNet-1K): 96.146%    
+num_params: 28589128
+    
 
-These artifacts were compiled with the LiteRT GPU compatibility profile and validated with LiteRT Python GPU execution.
+## 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 = 236
+    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/convnext_tiny", “convnext_tiny.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{liu2022convnet2020s,
+      title={A ConvNet for the 2020s}, 
+      author={Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
+      year={2022},
+      eprint={2201.03545},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2201.03545}, 
+}
+```