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612 lines
16 KiB
612 lines
16 KiB
# Deep Layer Aggregation
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Extending “shallow” skip connections, **Dense Layer Aggregation (DLA)** incorporates more depth and sharing. The authors introduce two structures for deep layer aggregation (DLA): iterative deep aggregation (IDA) and hierarchical deep aggregation (HDA). These structures are expressed through an architectural framework, independent of the choice of backbone, for compatibility with current and future networks.
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IDA focuses on fusing resolutions and scales while HDA focuses on merging features from all modules and channels. IDA follows the base hierarchy to refine resolution and aggregate scale stage-bystage. HDA assembles its own hierarchy of tree-structured connections that cross and merge stages to aggregate different levels of representation.
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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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```py
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>>> import timm
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>>> model = timm.create_model('dla102', 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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```py
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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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```py
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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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```py
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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. `dla102`. 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](../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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```py
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>>> model = timm.create_model('dla102', pretrained=True, num_classes=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](../scripts) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{yu2019deep,
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title={Deep Layer Aggregation},
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author={Fisher Yu and Dequan Wang and Evan Shelhamer and Trevor Darrell},
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year={2019},
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eprint={1707.06484},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Type: model-index
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Collections:
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- Name: DLA
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Paper:
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Title: Deep Layer Aggregation
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URL: https://paperswithcode.com/paper/deep-layer-aggregation
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Models:
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- Name: dla102
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In Collection: DLA
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Metadata:
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FLOPs: 7192952808
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Parameters: 33270000
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File Size: 135290579
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- DLA Bottleneck Residual Block
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- DLA Residual Block
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 8x GPUs
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ID: dla102
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LR: 0.1
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Epochs: 120
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Layers: 102
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L410
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Weights: http://dl.yf.io/dla/models/imagenet/dla102-d94d9790.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.03%
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Top 5 Accuracy: 93.95%
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- Name: dla102x
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In Collection: DLA
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Metadata:
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FLOPs: 5886821352
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Parameters: 26310000
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File Size: 107552695
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- DLA Bottleneck Residual Block
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- DLA Residual Block
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 8x GPUs
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ID: dla102x
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LR: 0.1
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Epochs: 120
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Layers: 102
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L418
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Weights: http://dl.yf.io/dla/models/imagenet/dla102x-ad62be81.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.51%
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Top 5 Accuracy: 94.23%
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- Name: dla102x2
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In Collection: DLA
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Metadata:
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FLOPs: 9343847400
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Parameters: 41280000
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File Size: 167645295
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- DLA Bottleneck Residual Block
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- DLA Residual Block
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 8x GPUs
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ID: dla102x2
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LR: 0.1
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Epochs: 120
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Layers: 102
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L426
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Weights: http://dl.yf.io/dla/models/imagenet/dla102x2-262837b6.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.44%
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Top 5 Accuracy: 94.65%
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- Name: dla169
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In Collection: DLA
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Metadata:
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FLOPs: 11598004200
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Parameters: 53390000
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File Size: 216547113
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- DLA Bottleneck Residual Block
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- DLA Residual Block
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 8x GPUs
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ID: dla169
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LR: 0.1
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Epochs: 120
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Layers: 169
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L434
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Weights: http://dl.yf.io/dla/models/imagenet/dla169-0914e092.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.69%
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Top 5 Accuracy: 94.33%
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- Name: dla34
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In Collection: DLA
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Metadata:
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FLOPs: 3070105576
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Parameters: 15740000
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File Size: 63228658
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- DLA Bottleneck Residual Block
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- DLA Residual Block
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: dla34
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LR: 0.1
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Epochs: 120
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Layers: 32
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L362
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Weights: http://dl.yf.io/dla/models/imagenet/dla34-ba72cf86.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 74.62%
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Top 5 Accuracy: 92.06%
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- Name: dla46_c
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In Collection: DLA
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Metadata:
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FLOPs: 583277288
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Parameters: 1300000
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File Size: 5307963
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- DLA Bottleneck Residual Block
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- DLA Residual Block
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: dla46_c
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LR: 0.1
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Epochs: 120
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Layers: 46
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L369
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Weights: http://dl.yf.io/dla/models/imagenet/dla46_c-2bfd52c3.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 64.87%
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Top 5 Accuracy: 86.29%
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- Name: dla46x_c
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In Collection: DLA
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Metadata:
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FLOPs: 544052200
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Parameters: 1070000
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File Size: 4387641
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- DLA Bottleneck Residual Block
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- DLA Residual Block
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: dla46x_c
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LR: 0.1
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Epochs: 120
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Layers: 46
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L378
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Weights: http://dl.yf.io/dla/models/imagenet/dla46x_c-d761bae7.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 65.98%
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Top 5 Accuracy: 86.99%
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- Name: dla60
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In Collection: DLA
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Metadata:
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FLOPs: 4256251880
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Parameters: 22040000
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File Size: 89560235
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- DLA Bottleneck Residual Block
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- DLA Residual Block
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: dla60
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LR: 0.1
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Epochs: 120
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Layers: 60
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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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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L394
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Weights: http://dl.yf.io/dla/models/imagenet/dla60-24839fc4.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 77.04%
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Top 5 Accuracy: 93.32%
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- Name: dla60_res2net
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In Collection: DLA
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Metadata:
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FLOPs: 4147578504
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Parameters: 20850000
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File Size: 84886593
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- DLA Bottleneck Residual Block
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- DLA Residual Block
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: dla60_res2net
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Layers: 60
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L346
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net_dla60_4s-d88db7f9.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.46%
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Top 5 Accuracy: 94.21%
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- Name: dla60_res2next
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In Collection: DLA
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Metadata:
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FLOPs: 3485335272
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Parameters: 17030000
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File Size: 69639245
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- DLA Bottleneck Residual Block
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- DLA Residual Block
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: dla60_res2next
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Layers: 60
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L354
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next_dla60_4s-d327927b.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 78.44%
|
|
Top 5 Accuracy: 94.16%
|
|
- Name: dla60x
|
|
In Collection: DLA
|
|
Metadata:
|
|
FLOPs: 3544204264
|
|
Parameters: 17350000
|
|
File Size: 70883139
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Convolution
|
|
- DLA Bottleneck Residual Block
|
|
- DLA Residual Block
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Block
|
|
- Residual Connection
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Techniques:
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Data:
|
|
- ImageNet
|
|
ID: dla60x
|
|
LR: 0.1
|
|
Epochs: 120
|
|
Layers: 60
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Batch Size: 256
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L402
|
|
Weights: http://dl.yf.io/dla/models/imagenet/dla60x-d15cacda.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 78.25%
|
|
Top 5 Accuracy: 94.02%
|
|
- Name: dla60x_c
|
|
In Collection: DLA
|
|
Metadata:
|
|
FLOPs: 593325032
|
|
Parameters: 1320000
|
|
File Size: 5454396
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Convolution
|
|
- DLA Bottleneck Residual Block
|
|
- DLA Residual Block
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Block
|
|
- Residual Connection
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Techniques:
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Data:
|
|
- ImageNet
|
|
ID: dla60x_c
|
|
LR: 0.1
|
|
Epochs: 120
|
|
Layers: 60
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Batch Size: 256
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L386
|
|
Weights: http://dl.yf.io/dla/models/imagenet/dla60x_c-b870c45c.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 67.91%
|
|
Top 5 Accuracy: 88.42%
|
|
--> |