# ResNet

ResNet model trained on imagenet-1k. It was introduced in the paper Deep Residual Learning for Image Recognition and first released in this repository.

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.

## Model description

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.

## 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:

>>> from transformers import AutoFeatureExtractor, ResNetForImageClassification
>>> import torch

>>> image = dataset["test"]["image"][0]

>>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-18")
>>> model = ResNetForImageClassification.from_pretrained("microsoft/resnet-18")

>>> inputs = feature_extractor(image, return_tensors="pt")

...     logits = model(**inputs).logits

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


For more code examples, we refer to the documentation.