Deep Residual Learning for Image Recognition
Paper • 1512.03385 • Published • 16
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("leftthomas/resnet50", trust_remote_code=True)
model = AutoModelForImageClassification.from_pretrained("leftthomas/resnet50", trust_remote_code=True)Quick Links
ResNet-50
Pretrained model on ImageNet. The ResNet architecture was introduced in this paper.
Intended uses
You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head to fine-tune it on a downstream task (another classification task with different labels, image segmentation or object detection, to name a few).
Evaluation results
This model has a top1-accuracy of 76.13% and a top-5 accuracy of 92.86% in the evaluation set of ImageNet.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="leftthomas/resnet50", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")