Upload GPU-compatible LiteRT artifacts

README.md

@@ -1,142 +1,23 @@
 ---
 library_name: litert
-pipeline_tag: image-classification
 tags:
+  - litert
+  - tflite
   - vision
   - image-classification
-  - google
-  - computer-vision
 datasets:
   - imagenet-1k
-model-index:
-  - name: litert-community/resnet18
-    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: 0.6977
-          - name: Top 5 Accuracy (Full Precision)
-            type: accuracy
-            value: 0.8913
-          - name: Top 1 Accuracy (Dynamic Quantized wi8 afp32)
-            type: accuracy
-            value: 0.6967
-          - name: Top 5 Accuracy (Dynamic Quantized wi8 afp32)
-            type: accuracy
-            value: 0.8901
 ---
 
-# ResNet 18
+# resnet18
 
-The ResNet-18 architecture is a convolutional neural network pre-trained on the ImageNet-1k dataset. Originally introduced by He et al. in the landmark paper, [**Deep Residual Learning for Image Recognition**](https://arxiv.org/pdf/1512.03385), this model utilizes residual mapping to overcome the vanishing gradient problem, enabling the training of substantially deeper networks.
+LiteRT TFLite conversion of the TorchVision `resnet18` model.
 
+## Files
 
-## Model description
+- `resnet18.tflite`
+- `resnet18_dynamic_wi8_afp32.tflite`
 
-The model was converted from a checkpoint from PyTorch Vision. 
+## Notes
 
-The original model has:    
-acc@1 (on ImageNet-1K): 69.758%    
-acc@5 (on ImageNet-1K): 89.078%    
-num_params: 11,689,512   
-
-## 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.transpose(x, (2, 0, 1))
-
-def main():
-   ap = argparse.ArgumentParser()
-   ap.add_argument("--image", required=True)
-   args = ap.parse_args()
-
-   model_path = hf_hub_download("litert-community/resnet18", "resnet18.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
-@inproceedings{he2016deep, 
-title={Deep residual learning for image recognition}, 
-author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, 
-booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, 
-year={2016} 
-}
-```
+These artifacts were compiled with the LiteRT GPU compatibility profile and validated with LiteRT Python GPU execution.

resnet18.tflite

@@ -1,3 +1,3 @@
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+oid sha256:068bd4bd67a18c0dcc55f91b73fb035317c26cccf94328528d6dd1788bc4a0f6
+size 46750944

resnet18_dynamic_wi8_afp32.tflite

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+size 11784928