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