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.
Image: model image
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 AutoImageProcessor, AutoModelForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-18")
>>> model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-18")
>>> inputs = image_processor(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])
tiger catFor more code examples, we refer to the documentation .
microsoft <huggingface.co/microsoft>Newsgroups: alt.freeweights.visionSubject: 
Transformers