dpn92
from rwightman/pytorch-image-models
From 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
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
:
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 for examples).
More generally, recent research 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, 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
@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
@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}
}
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