EfficientNet B5 model pre-trained on ImageNet-1k. Originally introduced by Tan and Le in the influential paper, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 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): 83.444%
acc@5 (on ImageNet-1K): 96.628%
num_params: 30,389,784

Quantized variant

efficientnet_b5_weight_only_wi8_afp32.tflite is a weight-only int8 quantization of the same weights (about 3.7x 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 1.000.

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:

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 = 456
   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] - 456) // 2
   top = (img.size[1] - 456) // 2
   img = img.crop((left, top, left + 456, top + 456))


   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_b5", "efficientnet_b5.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:

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}
}