README.md

---
library_name: litert
pipeline_tag: image-classification
tags:
  - vision
  - image-classification
  - google
  - computer-vision
datasets:
  - imagenet-1k
base_model:
  - google/efficientnet-b6
model-index:
  - name: litert-community/efficientnet_b6
    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:  0.8400
          - name: Top 5 Accuracy (Full Precision)
            type: accuracy
            value:  0.9691
          - name: Top 1 Accuracy (Dynamic Quantized wi8 afp32)
            type: accuracy
            value:  0.8383
          - name: Top 5 Accuracy (Dynamic Quantized wi8 afp32)
            type: accuracy
            value:  0.9677
---

# EfficientNet B6

EfficientNet B6 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): 84.008%    
acc@5 (on ImageNet-1K): 96.916%    
num_params: 43,040,704

## 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 = 528
   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] - 528) // 2
   top = (img.size[1] - 528) // 2
   img = img.crop((left, top, left + 528, top + 528))


   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_b6", "efficientnet_b6.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}
}
```