readme: ---
  tags:
  - image-classification
  - timm
  - resnet
  license: apache-2.0
  datasets:
  - imagenet
  widget:
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
    example_title: Tiger
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
    example_title: Teapot
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
    example_title: Palace
  ---
  
  # ResNet-50d
  
  Pretrained model on [ImageNet](http://www.image-net.org/). The ResNet architecture was introduced in
  [this paper](https://arxiv.org/abs/1512.03385) and is adapted with the ResNet-D trick from
  [this paper](https://arxiv.org/abs/1812.01187)
  
  
  ## Model description
  
  ResNet are deep convolutional neural networks using residual connections. Each layer is composed of two convolutions
  with a ReLU in the middle, but the output is the sum of the input with the output of the convolutional blocks.
  
  ![](https://neurohive.io/wp-content/uploads/2019/01/resnet-e1548261477164.png)
  
  This way, there is a direct connection from the original inputs to even the deepest layers in the network.
  
  ## Intended uses & limitations
  
  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).
  
  ### How to use
  
  You can use this model with the usual factory method in `timm`:
  
  ```python
  import PIL
  import timm
  import torch
  
  model = timm.create_model("sgugger/resnet50d")
  img = PIL.Image.open(path_to_an_image)
  img = img.convert("RGB")
  
  config = model.default_cfg
  
  if isinstance(config["input_size"], tuple):
      img_size = config["input_size"][-2:]
  else:
      img_size = config["input_size"]
  
  transform = timm.data.transforms_factory.transforms_imagenet_eval(
      img_size=img_size,
      interpolation=config["interpolation"],
      mean=config["mean"],
      std=config["std"],
  )
  
  input_tensor = transform(cat_img)
  input_tensor = input_tensor.unsqueeze(0)
  # ^ batch size = 1
  with torch.no_grad():
      output = model(input_tensor)
  
  probs = output.squeeze(0).softmax(dim=0)
  ```
  
  ### Limitations and bias
  
  The training images in the dataset are usually photos clearly representing one of the 1,000 labels. The model will
  probably not generalize well on drawings or images containing multiple objects with different labels.
  
  The training images in the dataset come mostly from the US (45.4%) and Great Britain (7.6%). As such the model or
  models created by fine-tuning this model will work better on images picturing scenes from these countries (see 
  [this paper](https://arxiv.org/abs/1906.02659) for examples).
  
  More generally, [recent research](https://arxiv.org/abs/2010.15052) has shown that even models trained in an
  unsupervised fashion on ImageNet (i.e. without using the labels) will pick up racial and gender bias represented in
  the training images.
  
  ## Training data
  
  This model was pretrained on [ImageNet](http://www.image-net.org/), a dataset consisting of 14 millions of
  hand-annotated images with 1,000 categories.
  
  ## Training procedure
  
  To be completed
  
  ### Preprocessing
  
  The images are resized using bicubic interpolation to 224x224 and normalized with the usual ImageNet statistics.
  
  ## Evaluation results
  
  This model has a top1-accuracy of 80.53% and a top-5 accuracy of 95.16% in the evaluation set of ImageNet
  
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{DBLP:journals/corr/HeZRS15,
    author    = {Kaiming He and
                 Xiangyu Zhang and
                 Shaoqing Ren and
                 Jian Sun},
    title     = {Deep Residual Learning for Image Recognition},
    journal   = {CoRR},
    volume    = {abs/1512.03385},
    year      = {2015},
    url       = {http://arxiv.org/abs/1512.03385},
    archivePrefix = {arXiv},
    eprint    = {1512.03385},
    timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```
  