README.md

---
library_name: litert
pipeline_tag: image-classification
tags:
  - vision
  - image-classification
  - google
  - computer-vision
datasets:
  - imagenet-1k
base_model:
  - google/efficientnet-b2
model-index:
  - name: litert-community/efficientnet_b2
    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: 0.8061
          - name: Top 5 Accuracy (Full Precision)
            type: accuracy
            value: 0.9530
---

# EfficientNet B2

EfficientNet B2 model pre-trained on ImageNet-1k. Originally introduced by Tan and Le in the influential paper,[ **EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks**](https://arxiv.org/abs/1905.11946) this model utilizes compound scaling to systematically balance network depth, width, and resolution, enabling superior accuracy with significantly higher efficiency than traditional architectures.

## Model description

The model was converted from a checkpoint from PyTorch Vision. 

The original model has:    
acc@1 (on ImageNet-1K): 80.608%    
acc@5 (on ImageNet-1K): 95.31%    
num_params: 9,109,994

### Quantized variant

`efficientnet_b2_weight_only_wi8_afp32.tflite` is a weight-only int8
quantization of the same weights (about 3.6x smaller than float32).
Weight-only quantization is used instead of dynamic-range quantization
because EfficientNet's SE and SiLU layers are sensitive to activation
quantization; in a spot check against the float model the weight-only
file keeps the top-1 predictions on real photos with a minimum logit
correlation of 0.999.

## 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 = 288
   if w < h:
       img = img.resize((s, int(round(h * s / w))), Image.BICUBIC)
   else:
       img = img.resize((int(round(w * s / h)), s), Image.BICUBIC)
   left = (img.size[0] - 288) // 2
   top = (img.size[1] - 288) // 2
   img = img.crop((left, top, left + 288, top + 288))


   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.transpose(x, (2, 0, 1))


def main():
   ap = argparse.ArgumentParser()
   ap.add_argument("--image", required=True)
   args = ap.parse_args()


   model_path = hf_hub_download("litert-community/efficientnet_b2", "efficientnet_b2.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{Tan2019EfficientNetRM,
  title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
  author={Mingxing Tan and Quoc V. Le},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.11946}
}
```