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+ ---
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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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+ ---
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+
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+ # ResNet-50 v1.5
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+
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+ 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.
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+
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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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+
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+ ## Model description
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+
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+ 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.
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+
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+ 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 smallperformance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).
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+
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+ ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/resnet_architecture.png)
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+
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+ ## Intended uses & limitations
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+
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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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+
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+ ### How to use
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+
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+ Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
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+
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+ ```python
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+ from transformers import AutoFeatureExtractor, ResNetForImageClassification
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+ import torch
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+ from datasets import load_dataset
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+
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+ dataset = load_dataset("huggingface/cats-image")
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+ image = dataset["test"]["image"][0]
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+
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+ feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
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+ model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
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+
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+ inputs = feature_extractor(image, return_tensors="pt")
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+
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+ with torch.no_grad():
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+ logits = model(**inputs).logits
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+
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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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+ ```
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+
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+ For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet).
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ @inproceedings{he2016deep,
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+ title={Deep residual learning for image recognition},
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+ author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
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+ booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
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+ pages={770--778},
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+ year={2016}
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+ }
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+ ```