---
library_name: litert
pipeline_tag: image-classification
tags:
  - vision
  - image-classification
  - google
  - computer-vision
datasets:
  - imagenet-1k
model-index:
  - name: litert-community/convnext_tiny
    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: 0.8246
          - name: Top 5 Accuracy (Full Precision)
            type: accuracy
            value: 0.9613
---

# Convnext Tiny

ConvNeXt Tiny model designed as a lightweight, pure convolutional backbone for efficient visual recognition in the "Roaring 20s." Originally introduced by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. in the modernized paper, [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545), this model "modernizes" the standard ResNet by adopting Transformer-inspired inductive biases, such as depthwise convolutions with \\(7 \times 7 \\) kernels and inverted bottlenecks. With approximately 28M parameters and 4.5 GFLOPs, it achieves accuracy levels comparable to the Swin-T Transformer while maintaining the simplicity and high throughput of a standard ConvNet.

## Model description

The model was converted from a checkpoint from PyTorch Vision. 

The original model has:    
acc@1 (on ImageNet-1K): 82.52%    
acc@5 (on ImageNet-1K): 96.146%    
num_params: 28589128
    

## 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 = 236
    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/convnext_tiny", “convnext_tiny.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{liu2022convnet2020s,
      title={A ConvNet for the 2020s}, 
      author={Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
      year={2022},
      eprint={2201.03545},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2201.03545}, 
}
```