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--- |
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tags: |
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- image-classification |
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- timm |
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- dpn |
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license: apache-2.0 |
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datasets: |
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- imagenet |
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--- |
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# `dpn92` from `rwightman/pytorch-image-models` |
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From [`rwightman/pytorch-image-models`](https://github.com/rwightman/pytorch-image-models): |
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``` |
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""" PyTorch implementation of DualPathNetworks |
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Based on original MXNet implementation https://github.com/cypw/DPNs with |
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many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs. |
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This implementation is compatible with the pretrained weights from cypw's MXNet implementation. |
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Hacked together by / Copyright 2020 Ross Wightman |
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""" |
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``` |
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## Model description |
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[Dual Path Networks](https://arxiv.org/abs/1707.01629) |
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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("julien-c/timm-dpn92") |
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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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To be completed |
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## Evaluation results |
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To be completed |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{rw2019timm, |
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author = {Ross Wightman}, |
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title = {PyTorch Image Models}, |
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year = {2019}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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doi = {10.5281/zenodo.4414861}, |
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howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} |
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} |
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``` |
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and |
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```bibtex |
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@misc{chen2017dual, |
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title={Dual Path Networks}, |
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author={Yunpeng Chen and Jianan Li and Huaxin Xiao and Xiaojie Jin and Shuicheng Yan and Jiashi Feng}, |
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year={2017}, |
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eprint={1707.01629}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
