---
library_name: litert
pipeline_tag: image-classification
tags:
  - vision
  - image-classification
  - google
  - computer-vision
datasets:
  - imagenet-1k
model-index:
  - name: litert-community/shufflenet_v2_x2_0
    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: 0.7622
          - name: Top 5 Accuracy (Full Precision)
            type: accuracy
            value: 0.9297
---

# ShuffleNet V2 x2.0

ShuffleNet V2 x2.0 model designed for high-performance applications on edge devices where both accuracy and latency are critical. Originally introduced by Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun in the influential paper, [**ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design**](https://arxiv.org/abs/1807.11164) this model scales the architecture to a 2.0x complexity multiplier, doubling the feature channels to significantly boost representational power. It achieves a Top-1 accuracy of approximately 76.2% on ImageNet-1K with roughly 0.58 GFLOPs and 7.4M parameters, maintaining high hardware efficiency through its optimized "channel split" and "channel shuffle" building blocks.

## Model description

The model was converted from a checkpoint from PyTorch Vision. 

The original model has:    
acc@1 (on ImageNet-1K): 76.23%    
acc@5 (on ImageNet-1K): 93.006%    
num_params: 7393996

## 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 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/shufflenet_v2_x2_0", "shufflenet_v2_x2_0.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{ma2018shufflenetv2practicalguidelines,
      title={ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design}, 
      author={Ningning Ma and Xiangyu Zhang and Hai-Tao Zheng and Jian Sun},
      year={2018},
      eprint={1807.11164},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1807.11164}, 
}
```