Upload GPU-compatible LiteRT artifacts

README.md

@@ -1,158 +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/MobileNet-v3-small
-    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: 0.6762
-          - name: Top 5 Accuracy (Full Precision)
-            type: accuracy
-            value: 0.8740
 ---
 
-# MobileNet V3 Small
+# MobileNet-v3-small
 
+LiteRT TFLite conversion of the TorchVision `mobilenet_v3_small` model.
 
-MobileNet V3 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).
+## Files
 
+- `mobilenet_v3_small.tflite`
+- `mobilenet_v3_small_dynamic_wi8_afp32.tflite`
 
-## Model description
+## Notes
 
-The model was converted from a checkpoint from PyTorch Vision. 
-
-The original model has:    
-acc@1 (on ImageNet-1K): 67.668%    
-acc@5 (on ImageNet-1K): 87.402%    
-num_params: 2,542,856 
-
-
-The license information of the original model was missing.
-
-
-## 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 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-small", "mobilenet_v3_small.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
-@article{DBLP:journals/corr/abs-1905-02244,
-  author       = {Andrew Howard and
-                  Mark Sandler and
-                  Grace Chu and
-                  Liang{-}Chieh Chen and
-                  Bo Chen and
-                  Mingxing Tan and
-                  Weijun Wang and
-                  Yukun Zhu and
-                  Ruoming Pang and
-                  Vijay Vasudevan and
-                  Quoc V. Le and
-                  Hartwig Adam},
-  title        = {Searching for MobileNetV3},
-  journal      = {CoRR},
-  volume       = {abs/1905.02244},
-  year         = {2019},
-  url          = {http://arxiv.org/abs/1905.02244},
-  eprinttype    = {arXiv},
-  eprint       = {1905.02244},
-  timestamp    = {Thu, 27 May 2021 16:20:51 +0200},
-  biburl       = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
-  bibsource    = {dblp computer science bibliography, https://dblp.org}
-}
-```
+These artifacts were compiled with the LiteRT GPU compatibility profile and validated with LiteRT Python GPU execution.

mobilenet_v3_small.tflite

@@ -1,3 +1,3 @@
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-size 10335220
+oid sha256:c7a8324a93d991d6b972d431c2219026d371b72f683d32da61f432b11e336a54
+size 10184432

mobilenet_v3_small_dynamic_wi8_afp32.tflite

@@ -0,0 +1,3 @@
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+oid sha256:a93f73c57e09b1f0224ee9965ae92af6c7da16373b2b02f9db232721f5b0ced6
+size 2741424