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322 lines
8.9 KiB
322 lines
8.9 KiB
# Summary
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**DenseNet** is a type of convolutional neural network that utilises dense connections between layers, through [Dense Blocks](http://www.paperswithcode.com/method/dense-block), where we connect *all layers* (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.
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The **DenseNet Blur** variant in this collection by Ross Wightman employs [Blur Pooling](http://www.paperswithcode.com/method/blur-pooling)
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## How do I use this model on an image?
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To load a pretrained model:
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```python
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import timm
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model = timm.create_model('densenetblur121d', pretrained=True)
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model.eval()
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```
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To load and preprocess the image:
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```python
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import urllib
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from PIL import Image
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from timm.data import resolve_data_config
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from timm.data.transforms_factory import create_transform
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config = resolve_data_config({}, model=model)
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transform = create_transform(**config)
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url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
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urllib.request.urlretrieve(url, filename)
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img = Image.open(filename).convert('RGB')
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tensor = transform(img).unsqueeze(0) # transform and add batch dimension
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```
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To get the model predictions:
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```python
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import torch
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with torch.no_grad():
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out = model(tensor)
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probabilities = torch.nn.functional.softmax(out[0], dim=0)
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print(probabilities.shape)
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# prints: torch.Size([1000])
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```
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To get the top-5 predictions class names:
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```python
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# Get imagenet class mappings
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url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
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urllib.request.urlretrieve(url, filename)
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with open("imagenet_classes.txt", "r") as f:
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categories = [s.strip() for s in f.readlines()]
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# Print top categories per image
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top5_prob, top5_catid = torch.topk(probabilities, 5)
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for i in range(top5_prob.size(0)):
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print(categories[top5_catid[i]], top5_prob[i].item())
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# prints class names and probabilities like:
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# [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
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```
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Replace the model name with the variant you want to use, e.g. `densenetblur121d`. You can find the IDs in the model summaries at the top of this page.
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To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use.
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## How do I finetune this model?
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You can finetune any of the pre-trained models just by changing the classifier (the last layer).
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```python
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model = timm.create_model('densenetblur121d', pretrained=True).reset_classifier(NUM_FINETUNE_CLASSES)
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```
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To finetune on your own dataset, you have to write a training loop or adapt [timm's training
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script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@article{DBLP:journals/corr/HuangLW16a,
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author = {Gao Huang and
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Zhuang Liu and
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Kilian Q. Weinberger},
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title = {Densely Connected Convolutional Networks},
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journal = {CoRR},
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volume = {abs/1608.06993},
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year = {2016},
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url = {http://arxiv.org/abs/1608.06993},
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archivePrefix = {arXiv},
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eprint = {1608.06993},
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timestamp = {Mon, 10 Sep 2018 15:49:32 +0200},
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biburl = {https://dblp.org/rec/journals/corr/HuangLW16a.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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```
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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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<!--
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Models:
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- Name: densenetblur121d
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Metadata:
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FLOPs: 3947812864
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Training Data:
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- ImageNet
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Blur Pooling
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- Convolution
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- Dense Block
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- Dense Connections
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- Dropout
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- Max Pooling
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- ReLU
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- Softmax
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File Size: 32456500
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Tasks:
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- Image Classification
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ID: densenetblur121d
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L305
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In Collection: DenseNet
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- Name: tv_densenet121
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Metadata:
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FLOPs: 3641843200
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Epochs: 90
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Batch Size: 32
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Architecture:
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- 1x1 Convolution
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Dense Block
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- Dense Connections
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- Dropout
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- Max Pooling
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- ReLU
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- Softmax
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File Size: 32342954
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Tasks:
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- Image Classification
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ID: tv_densenet121
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LR: 0.1
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Crop Pct: '0.875'
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LR Gamma: 0.1
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Momentum: 0.9
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Image Size: '224'
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LR Step Size: 30
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Weight Decay: 0.0001
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L379
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In Collection: DenseNet
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- Name: densenet121
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Metadata:
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FLOPs: 3641843200
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Epochs: 90
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- Kaiming Initialization
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Resources: ''
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Architecture:
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- 1x1 Convolution
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Dense Block
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- Dense Connections
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- Dropout
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- Max Pooling
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- ReLU
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- Softmax
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File Size: 32376726
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Tasks:
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- Image Classification
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Training Time: ''
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ID: densenet121
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LR: 0.1
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Layers: 121
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Dropout: 0.2
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L295
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Config: ''
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In Collection: DenseNet
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- Name: densenet201
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Metadata:
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FLOPs: 5514321024
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Epochs: 90
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- Kaiming Initialization
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- Nesterov Accelerated Gradient
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- Weight Decay
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Architecture:
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- 1x1 Convolution
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Dense Block
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- Dense Connections
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- Dropout
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- Max Pooling
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- ReLU
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- Softmax
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File Size: 81131730
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Tasks:
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- Image Classification
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ID: densenet201
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LR: 0.1
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Layers: 201
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Dropout: 0.2
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L337
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In Collection: DenseNet
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- Name: densenet169
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Metadata:
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FLOPs: 4316945792
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Epochs: 90
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- Kaiming Initialization
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- Nesterov Accelerated Gradient
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- Weight Decay
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Architecture:
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- 1x1 Convolution
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Dense Block
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- Dense Connections
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- Dropout
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- Max Pooling
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- ReLU
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- Softmax
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File Size: 57365526
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Tasks:
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- Image Classification
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ID: densenet169
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LR: 0.1
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Layers: 169
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Dropout: 0.2
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L327
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In Collection: DenseNet
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- Name: densenet161
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Metadata:
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FLOPs: 9931959264
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Epochs: 90
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- Kaiming Initialization
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- Nesterov Accelerated Gradient
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- Weight Decay
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Architecture:
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- 1x1 Convolution
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Dense Block
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- Dense Connections
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- Dropout
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- Max Pooling
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- ReLU
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- Softmax
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File Size: 115730790
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Tasks:
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- Image Classification
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ID: densenet161
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LR: 0.1
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Layers: 161
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Dropout: 0.2
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L347
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In Collection: DenseNet
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Collections:
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- Name: DenseNet
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Paper:
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title: Densely Connected Convolutional Networks
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url: https://paperswithcode.com//paper/densely-connected-convolutional-networks
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type: model-index
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Type: model-index
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--> |