Basic README.md

README.md

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