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pytorch-image-models/modelindex/models/dla.md

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# Summary
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('dla60', 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. `dla60`. 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('dla60', pretrained=True).reset_classifier(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}
}
```
<!--
Models:
- Name: dla60
Metadata:
FLOPs: 4256251880
Epochs: 120
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: ''
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
File Size: 89560235
Tasks:
- Image Classification
Training Time: ''
ID: dla60
LR: 0.1
Layers: 60
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L394
Config: ''
In Collection: DLA
- Name: dla46_c
Metadata:
FLOPs: 583277288
Epochs: 120
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: ''
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
File Size: 5307963
Tasks:
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Training Time: ''
ID: dla46_c
LR: 0.1
Layers: 46
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L369
Config: ''
In Collection: DLA
- Name: dla102x2
Metadata:
FLOPs: 9343847400
Epochs: 120
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
File Size: 167645295
Tasks:
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Training Time: ''
ID: dla102x2
LR: 0.1
Layers: 102
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L426
Config: ''
In Collection: DLA
- Name: dla102
Metadata:
FLOPs: 7192952808
Epochs: 120
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
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- Max Pooling
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- Residual Connection
- Softmax
File Size: 135290579
Tasks:
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Training Time: ''
ID: dla102
LR: 0.1
Layers: 102
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L410
Config: ''
In Collection: DLA
- Name: dla102x
Metadata:
FLOPs: 5886821352
Epochs: 120
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
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- DLA Residual Block
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- Residual Connection
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File Size: 107552695
Tasks:
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Training Time: ''
ID: dla102x
LR: 0.1
Layers: 102
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L418
Config: ''
In Collection: DLA
- Name: dla169
Metadata:
FLOPs: 11598004200
Epochs: 120
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x GPUs
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
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- Residual Connection
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File Size: 216547113
Tasks:
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Training Time: ''
ID: dla169
LR: 0.1
Layers: 169
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L434
Config: ''
In Collection: DLA
- Name: dla46x_c
Metadata:
FLOPs: 544052200
Epochs: 120
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: ''
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
File Size: 4387641
Tasks:
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Training Time: ''
ID: dla46x_c
LR: 0.1
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Image Size: '224'
Weight Decay: 0.0001
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L378
Config: ''
In Collection: DLA
- Name: dla60_res2net
Metadata:
FLOPs: 4147578504
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: ''
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
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- DLA Residual Block
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- Max Pooling
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- Residual Connection
- Softmax
File Size: 84886593
Tasks:
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Training Time: ''
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
Config: ''
In Collection: DLA
- Name: dla60_res2next
Metadata:
FLOPs: 3485335272
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: ''
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
File Size: 69639245
Tasks:
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Training Time: ''
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
Config: ''
In Collection: DLA
- Name: dla34
Metadata:
FLOPs: 3070105576
Epochs: 120
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: ''
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
File Size: 63228658
Tasks:
- Image Classification
Training Time: ''
ID: dla34
LR: 0.1
Layers: 32
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L362
Config: ''
In Collection: DLA
- Name: dla60x
Metadata:
FLOPs: 3544204264
Epochs: 120
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: ''
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
File Size: 70883139
Tasks:
- Image Classification
Training Time: ''
ID: dla60x
LR: 0.1
Layers: 60
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L402
Config: ''
In Collection: DLA
- Name: dla60x_c
Metadata:
FLOPs: 593325032
Epochs: 120
Batch Size: 256
Training Data:
- ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: ''
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
File Size: 5454396
Tasks:
- Image Classification
Training Time: ''
ID: dla60x_c
LR: 0.1
Layers: 60
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L386
Config: ''
In Collection: DLA
Collections:
- Name: DLA
Paper:
title: Deep Layer Aggregation
url: https://paperswithcode.com//paper/deep-layer-aggregation
type: model-index
Type: model-index
-->