---
library_name: litert
pipeline_tag: image-classification
tags:
  - vision
  - image-classification
  - google
  - computer-vision
datasets:
  - imagenet-1k
model-index:
  - name: litert-community/MobileNet-v3-large
    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: 0.7523
          - name: Top 5 Accuracy (Full Precision)
            type: accuracy
            value: 0.9258
---

# MobileNet V3 Large


MobileNet V3 Large 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).


## Model description

The model was converted from a checkpoint from PyTorch Vision. 

The original model has:    
acc@1 (on ImageNet-1K): 75.274%    
acc@5 (on ImageNet-1K): 92.566%    
num_params: 5,483,032 

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 = 232
   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-large", "mobilenet_v3_large.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}
}
```