# Deep Layer Aggregation 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. 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. ## How do I use this model on an image? To load a pretrained model: ```python import timm model = timm.create_model('dla102', pretrained=True) model.eval() ``` To load and preprocess the image: ```python 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 ``` To get the model predictions: ```python 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]) ``` To get the top-5 predictions class names: ```python # 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)] ``` 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. 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. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```python model = timm.create_model('dla102', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh. ## Citation ```BibTeX @misc{yu2019deep, title={Deep Layer Aggregation}, author={Fisher Yu and Dequan Wang and Evan Shelhamer and Trevor Darrell}, year={2019}, eprint={1707.06484}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- Type: model-index Collections: - Name: DLA Paper: Title: Deep Layer Aggregation URL: https://paperswithcode.com/paper/deep-layer-aggregation Models: - Name: dla102 In Collection: DLA Metadata: FLOPs: 7192952808 Parameters: 33270000 File Size: 135290579 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 Training Resources: 8x GPUs ID: dla102 LR: 0.1 Epochs: 120 Layers: 102 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#L410 Weights: http://dl.yf.io/dla/models/imagenet/dla102-d94d9790.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.03% Top 5 Accuracy: 93.95% - Name: dla102x In Collection: DLA Metadata: FLOPs: 5886821352 Parameters: 26310000 File Size: 107552695 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 Training Resources: 8x GPUs ID: dla102x LR: 0.1 Epochs: 120 Layers: 102 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#L418 Weights: http://dl.yf.io/dla/models/imagenet/dla102x-ad62be81.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.51% Top 5 Accuracy: 94.23% - Name: dla102x2 In Collection: DLA Metadata: FLOPs: 9343847400 Parameters: 41280000 File Size: 167645295 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 Training Resources: 8x GPUs ID: dla102x2 LR: 0.1 Epochs: 120 Layers: 102 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#L426 Weights: http://dl.yf.io/dla/models/imagenet/dla102x2-262837b6.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.44% Top 5 Accuracy: 94.65% - Name: dla169 In Collection: DLA Metadata: FLOPs: 11598004200 Parameters: 53390000 File Size: 216547113 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 Training Resources: 8x GPUs ID: dla169 LR: 0.1 Epochs: 120 Layers: 169 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#L434 Weights: http://dl.yf.io/dla/models/imagenet/dla169-0914e092.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.69% Top 5 Accuracy: 94.33% - Name: dla34 In Collection: DLA Metadata: FLOPs: 3070105576 Parameters: 15740000 File Size: 63228658 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: dla34 LR: 0.1 Epochs: 120 Layers: 32 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#L362 Weights: http://dl.yf.io/dla/models/imagenet/dla34-ba72cf86.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 74.62% Top 5 Accuracy: 92.06% - Name: dla46_c In Collection: DLA Metadata: FLOPs: 583277288 Parameters: 1300000 File Size: 5307963 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: dla46_c LR: 0.1 Epochs: 120 Layers: 46 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#L369 Weights: http://dl.yf.io/dla/models/imagenet/dla46_c-2bfd52c3.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 64.87% Top 5 Accuracy: 86.29% - Name: dla46x_c In Collection: DLA Metadata: FLOPs: 544052200 Parameters: 1070000 File Size: 4387641 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: dla46x_c LR: 0.1 Epochs: 120 Layers: 46 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#L378 Weights: http://dl.yf.io/dla/models/imagenet/dla46x_c-d761bae7.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 65.98% Top 5 Accuracy: 86.99% - Name: dla60 In Collection: DLA Metadata: FLOPs: 4256251880 Parameters: 22040000 File Size: 89560235 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: dla60 LR: 0.1 Epochs: 120 Layers: 60 Dropout: 0.2 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#L394 Weights: http://dl.yf.io/dla/models/imagenet/dla60-24839fc4.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 77.04% Top 5 Accuracy: 93.32% - Name: dla60_res2net In Collection: DLA Metadata: FLOPs: 4147578504 Parameters: 20850000 File Size: 84886593 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: dla60_res2net Layers: 60 Crop Pct: '0.875' Image Size: '224' Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L346 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net_dla60_4s-d88db7f9.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.46% Top 5 Accuracy: 94.21% - Name: dla60_res2next In Collection: DLA Metadata: FLOPs: 3485335272 Parameters: 17030000 File Size: 69639245 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: dla60_res2next Layers: 60 Crop Pct: '0.875' Image Size: '224' Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L354 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% -->