65 lines
2.2 KiB
Markdown
65 lines
2.2 KiB
Markdown
---
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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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# ResNet
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ResNet model trained on imagenet-1k. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) and first released in [this repository](https://github.com/KaimingHe/deep-residual-networks).
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Disclaimer: The team releasing ResNet 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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## Model description
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ResNet introduced residual connections, they allow to train networks with an unseen number of layers (up to 1000). ResNet won the 2015 ILSVRC & COCO competition, one important milestone in deep computer vision.
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## Intended uses & limitations
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You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for
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fine-tuned versions on a task that interests you.
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### How to use
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Here is how to use this model:
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```python
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>>> from transformers import AutoImageProcessor, AutoModelForImageClassification
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>>> import torch
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>>> from datasets import load_dataset
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>>> dataset = load_dataset("huggingface/cats-image")
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>>> image = dataset["test"]["image"][0]
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>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-18")
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>>> model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-18")
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>>> inputs = image_processor(image, return_tensors="pt")
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>>> with torch.no_grad():
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... logits = model(**inputs).logits
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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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tiger cat
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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/resnet). |