ResNet-50 是一个深度卷积神经网络架构，常用于图像识别任务

--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k --- # ResNet-50 v1.5 ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. 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 (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models. This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).  ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, ResNetForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50") inputs = 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]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet). ### BibTeX entry and citation info ```bibtex @inproceedings{he2016deep, title={Deep residual learning for image recognition}, author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, year={2016} } ```