DenseNet
DenseNet 是一种卷积神经网络，它利用层之间密集的连接，通过 密集块，我们在其中将所有层（具有匹配的特征图大小）直接相互连接。为了保持前馈性质，每一层从所有先前层获得额外的输入，并将自己的特征图传递给所有后续层。
此集合中的 DenseNet Blur 变体由 Ross Wightman 采用 模糊池化
如何在图像上使用此模型？
加载预训练模型
>>> import timm
>>> model = timm.create_model('densenet121', pretrained=True)
>>> model.eval()加载和预处理图像
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension获取模型预测
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])获取前 5 个预测类别名称
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]将模型名称替换为要使用的变体，例如 densenet121。您可以在此页面顶部的模型摘要中找到 ID。
要使用此模型提取图像特征，请遵循 timm 特征提取示例，只需更改要使用的模型名称即可。
如何微调此模型？
您可以通过更改分类器（最后一层）来微调任何预训练模型。
>>> model = timm.create_model('densenet121', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)要在自己的数据集上进行微调，您必须编写一个训练循环或调整 timm 的训练脚本 以使用您的数据集。
如何训练此模型？
您可以遵循 timm 食谱脚本 来从头开始训练新模型。
引用
@article{DBLP:journals/corr/HuangLW16a,
author = {Gao Huang and
Zhuang Liu and
Kilian Q. Weinberger},
title = {Densely Connected Convolutional Networks},
journal = {CoRR},
volume = {abs/1608.06993},
year = {2016},
url = {http://arxiv.org/abs/1608.06993},
archivePrefix = {arXiv},
eprint = {1608.06993},
timestamp = {Mon, 10 Sep 2018 15:49:32 +0200},
biburl = {https://dblp.org/rec/journals/corr/HuangLW16a.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}@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}}
}