Efficientnet B4

🚀 EfficientNet (b4 model)

An EfficientNet model trained on ImageNet-1k at a resolution of 380x380, offering high - performance image classification.

🚀 Quick Start

The EfficientNet model presented here is trained on the ImageNet - 1k dataset at a resolution of 380x380. It was first introduced in the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le, and initially released in this repository.

Disclaimer: The team that released EfficientNet did not create a model card for this model. This model card is written by the Hugging Face team.

✨ Features

EfficientNet is a mobile - friendly pure convolutional model (ConvNet). It proposes a novel scaling method that uniformly scales all dimensions of depth, width, and resolution using a simple yet highly effective compound coefficient.

📚 Documentation

Intended uses & limitations

You can utilize the raw model for image classification. Check out the model hub to find fine - tuned versions for tasks that interest you.

How to use

Here is a demonstration of using this model to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes:

Basic Usage

import torch
from datasets import load_dataset
from transformers import EfficientNetImageProcessor, EfficientNetForImageClassification

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

preprocessor = EfficientNetImageProcessor.from_pretrained("google/efficientnet-b4")
model = EfficientNetForImageClassification.from_pretrained("google/efficientnet-b4")

inputs = preprocessor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),

For more code examples, refer to the documentation.

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}
}

📄 License

This project is licensed under the Apache - 2.0 license.