Efficientnet B3

🚀 EfficientNet (b3 model)

An EfficientNet model trained on ImageNet - 1k at a resolution of 300x300, offering high - performance image classification.

🚀 Quick Start

EfficientNet is a mobile - friendly pure convolutional model (ConvNet). It was introduced in the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le, and first released in this repository.

Disclaimer: The team releasing EfficientNet did not write a model card for this model, so this model card has been written by the Hugging Face team.

✨ Features

• EfficientNet proposes a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient.

📚 Documentation

Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for fine - tuned versions on a task that interests you.

How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

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-b3")
model = EfficientNetForImageClassification.from_pretrained("google/efficientnet-b3")

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, we 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 model is licensed under the Apache - 2.0 license.