readme: ---
  license: apache-2.0
  tags:
  - vision
  - image-classification
  datasets:
  - imagenet-1k
  ---
  
  # ResNet-101 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).
  
  ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/resnet_architecture.png)
  
  ## 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 AutoFeatureExtractor, ResNetForImageClassification
  import torch
  from datasets import load_dataset
  
  dataset = load_dataset("huggingface/cats-image")
  image = dataset["test"]["image"][0]
  
  feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-101")
  model = ResNetForImageClassification.from_pretrained("microsoft/resnet-101")
  
  inputs = feature_extractor(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}
  }
  ```
  