litert-community/efficientnet_b1

EfficientNet B1

EfficientNet B1 model pre-trained on ImageNet-1k. Originally introduced by Tan and Le in the influential paper, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networksthis 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 original model has:
acc@1 (on ImageNet-1K): 79.838%
acc@5 (on ImageNet-1K): 94.934%
num_params: 7,794,184

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.

Use

#!/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 = 255
   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] - 240) // 2
   top = (img.size[1] - 240) // 2
   img = img.crop((left, top, left + 240, top + 240))


   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_b1", "efficientnet_b1.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()

BibTeX entry and citation info

@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}
}