| | ---
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| | license: apache-2.0
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| | tags:
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| | - vision
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| | - image-classification
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| | datasets:
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| | - imagenet-1k
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| | widget:
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| | - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
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| | example_title: Tiger
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| | - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
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| | example_title: Teapot
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| | - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
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| | example_title: Palace
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| | ---
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| |
|
| | # EfficientNet (b7 model)
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| |
|
| | EfficientNet model trained on ImageNet-1k at resolution 600x600. It was introduced in the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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| | ](https://arxiv.org/abs/1905.11946) by Mingxing Tan and Quoc V. Le, and first released in [this repository](https://github.com/keras-team/keras).
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| |
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| | 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.
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| |
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| | ## Model description
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| |
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| | EfficientNet is a mobile friendly pure convolutional model (ConvNet) that proposes a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient.
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| |
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| | 
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| |
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| | ## Intended uses & limitations
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| |
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| | You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=efficientnet) to look for
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| | fine-tuned versions on a task that interests you.
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| |
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| | ### How to use
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| |
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| | Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
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| |
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| | ```python
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| | import torch
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| | from datasets import load_dataset
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| | from transformers import EfficientNetImageProcessor, EfficientNetForImageClassification
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| |
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| | dataset = load_dataset("huggingface/cats-image")
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| | image = dataset["test"]["image"][0]
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| |
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| | preprocessor = EfficientNetImageProcessor.from_pretrained("google/efficientnet-b7")
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| | model = EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7")
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| |
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| | inputs = preprocessor(image, return_tensors="pt")
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| |
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| | with torch.no_grad():
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| | logits = model(**inputs).logits
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| |
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| | # model predicts one of the 1000 ImageNet classes
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| | predicted_label = logits.argmax(-1).item()
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| | print(model.config.id2label[predicted_label]),
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| | ```
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| |
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| | For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/efficientnet).
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| |
|
| | ### BibTeX entry and citation info
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| |
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| | ```bibtex
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| | @article{Tan2019EfficientNetRM,
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| | title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
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| | author={Mingxing Tan and Quoc V. Le},
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| | journal={ArXiv},
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| | year={2019},
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| | volume={abs/1905.11946}
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| | }
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| | ``` |
