README.md

metadata

library_name: litert
pipeline_tag: image-classification
tags:
  - 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: 0.874

MobileNet V3 Small

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.

Model description

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.

Available model files

File	Description
mobilenet_v3_small.tflite	Full precision LiteRT/TFLite model.
mobilenet_v3_small_dynamic_wi8_afp32.tflite	Dynamic weight-only INT8 model with FP32 activations.
mobilenet_v3_small_Google_Tensor_G5_apply_plugin.tflite	AOT-compiled artifact for the Google Tensor G5 target.
mobilenet_v3_small_int8_channelwise.tflite	Static INT8 model with channelwise INT8 weights and asymmetric INT8 activations.

Quantization

mobilenet_v3_small_int8_channelwise.tflite was produced with the STATIC_WI8_AI8 quantization recipe. Weights are signed INT8 and use symmetric channelwise quantization for weight tensors. Activations are signed INT8 with asymmetric quantization parameters.

The INT8 channelwise artifact keeps standard LiteRT/TFLite model structure before AOT compilation. Local LiteRT compiler checks fully delegated this artifact on tested compatible NPU backends. Enablement for other NPU backends is still under validation.

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:

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:

#!/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:

python classify.py --image cat.jpg

BibTeX entry and citation info

@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}
}