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284 lines
8.7 KiB
284 lines
8.7 KiB
# SWSL ResNeXt
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A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width.
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The models in this collection utilise semi-weakly supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification.
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Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
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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('swsl_resnext101_32x16d', 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. `swsl_resnext101_32x16d`. 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('swsl_resnext101_32x16d', 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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@article{DBLP:journals/corr/abs-1905-00546,
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author = {I. Zeki Yalniz and
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Herv{\'{e}} J{\'{e}}gou and
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Kan Chen and
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Manohar Paluri and
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Dhruv Mahajan},
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title = {Billion-scale semi-supervised learning for image classification},
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journal = {CoRR},
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volume = {abs/1905.00546},
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year = {2019},
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url = {http://arxiv.org/abs/1905.00546},
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archivePrefix = {arXiv},
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eprint = {1905.00546},
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timestamp = {Mon, 28 Sep 2020 08:19:37 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-1905-00546.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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Type: model-index
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Collections:
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- Name: SWSL ResNext
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Paper:
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Title: Billion-scale semi-supervised learning for image classification
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URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for
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Models:
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- Name: swsl_resnext101_32x16d
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In Collection: SWSL ResNext
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Metadata:
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FLOPs: 46623691776
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Parameters: 194030000
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File Size: 777518664
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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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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt 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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- IG-1B-Targeted
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- ImageNet
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Training Resources: 64x GPUs
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ID: swsl_resnext101_32x16d
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LR: 0.0015
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Epochs: 30
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Layers: 101
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Crop Pct: '0.875'
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Batch Size: 1536
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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/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L1009
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Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.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: 83.34%
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Top 5 Accuracy: 96.84%
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- Name: swsl_resnext101_32x4d
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In Collection: SWSL ResNext
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Metadata:
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FLOPs: 10298145792
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Parameters: 44180000
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File Size: 177341913
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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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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt 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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- IG-1B-Targeted
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- ImageNet
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Training Resources: 64x GPUs
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ID: swsl_resnext101_32x4d
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LR: 0.0015
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Epochs: 30
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Layers: 101
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Crop Pct: '0.875'
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Batch Size: 1536
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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/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L987
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Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.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: 83.22%
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Top 5 Accuracy: 96.77%
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- Name: swsl_resnext101_32x8d
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In Collection: SWSL ResNext
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Metadata:
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FLOPs: 21180417024
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Parameters: 88790000
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File Size: 356056638
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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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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt 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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- IG-1B-Targeted
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- ImageNet
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Training Resources: 64x GPUs
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ID: swsl_resnext101_32x8d
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LR: 0.0015
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Epochs: 30
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Layers: 101
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Crop Pct: '0.875'
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Batch Size: 1536
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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/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L998
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Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.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: 84.27%
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Top 5 Accuracy: 97.17%
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- Name: swsl_resnext50_32x4d
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In Collection: SWSL ResNext
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Metadata:
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FLOPs: 5472648192
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Parameters: 25030000
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File Size: 100428550
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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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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt 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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- IG-1B-Targeted
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- ImageNet
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Training Resources: 64x GPUs
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ID: swsl_resnext50_32x4d
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LR: 0.0015
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Epochs: 30
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Layers: 50
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Crop Pct: '0.875'
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Batch Size: 1536
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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/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L976
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Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.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: 82.17%
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Top 5 Accuracy: 96.23%
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--> |