122 lines
8.6 KiB
Markdown
122 lines
8.6 KiB
Markdown
# Rotation Prediction
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## Unsupervised Representation Learning by Predicting Image Rotation
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<!-- [ABSTRACT] -->
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Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic feature learning, i.e., learning without requiring manual annotation effort, is of crucial importance in order to successfully harvest the vast amount of visual data that are available today. In our work we propose to learn image features by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input. We demonstrate both qualitatively and quantitatively that this apparently simple task actually provides a very powerful supervisory signal for semantic feature learning. We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. Specifically, our results on those benchmarks demonstrate dramatic improvements w.r.t. prior state-of-the-art approaches in unsupervised representation learning and thus significantly close the gap with supervised feature learning.
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<!-- [IMAGE] -->
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<div align="center">
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<img />
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</div>
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## Citation
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<!-- [ALGORITHM] -->
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```bibtex
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@inproceedings{komodakis2018unsupervised,
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title={Unsupervised representation learning by predicting image rotations},
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author={Komodakis, Nikos and Gidaris, Spyros},
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booktitle={ICLR},
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year={2018}
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}
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```
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## Models and Benchmarks
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[Back to model_zoo.md](../../../docs/model_zoo.md)
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In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.
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### VOC SVM / Low-shot SVM
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The **Best Layer** indicates that the best results are obtained from which layers feature map. For example, if the **Best Layer** is **feature3**, its best result is obtained from the second stage of ResNet (1 for stem layer, 2-5 for 4 stage layers).
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Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
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| Model | Config | Best Layer | SVM | k=1 | k=2 | k=4 | k=8 | k=16 | k=32 | k=64 | k=96 |
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| --------- | ---------------------------------------------------------------------------- | ---------- | --- | --- | --- | --- | --- | ---- | ---- | ---- | ---- |
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| [model]() | [resnet50_8xb16-steplr-70e](rotation-pred_resnet50_8xb16-steplr-70e_in1k.py) | | | | | | | | | | |
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### ImageNet Linear Evaluation
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The **Feature1 - Feature5** don't have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to [resnet50_mhead_8xb32-steplr-90e.py](../../benchmarks/classification/imagenet/resnet50_mhead_8xb32-steplr-90e_in1k.py) for details of config.
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The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [file name]() for details of config.
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| Model | Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
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| --------- | ---------------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |
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| [model]() | [resnet50_8xb16-steplr-70e](rotation-pred_resnet50_8xb16-steplr-70e_in1k.py) | | | | | | |
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### iNaturalist2018 Linear Evaluation
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Please refer to [resnet50_mhead_8xb32-steplr-84e_inat18.py](../../benchmarks/classification/inaturalist2018/resnet50_mhead_8xb32-steplr-84e_inat18.py) and [file name]() for details of config.
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| Model | Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
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| --------- | ---------------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |
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| [model]() | [resnet50_8xb16-steplr-70e](rotation-pred_resnet50_8xb16-steplr-70e_in1k.py) | | | | | | |
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### Places205 Linear Evaluation
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Please refer to [resnet50_mhead_8xb32-steplr-28e_places205.py](../../benchmarks/classification/inaturalist2018/resnet50_mhead_8xb32-steplr-28e_places205.py) and [file name]() for details of config.
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| Model | Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
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| --------- | ---------------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |
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| [model]() | [resnet50_8xb16-steplr-70e](rotation-pred_resnet50_8xb16-steplr-70e_in1k.py) | | | | | | |
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#### Semi-Supervised Classification
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- In this benchmark, the necks or heads are removed and only the backbone CNN is evaluated by appending a linear classification head. All parameters are fine-tuned.
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- When training with 1% ImageNet, we find hyper-parameters especially the learning rate greatly influence the performance. Hence, we prepare a list of settings with the base learning rate from `{0.001, 0.01, 0.1}` and the learning rate multiplier for the head from `{1, 10, 100}`. We choose the best performing setting for each method. The setting of parameters are indicated in the file name. The learning rate is indicated like `1e-1`, `1e-2`, `1e-3` and the learning rate multiplier is indicated like `head1`, `head10`, `head100`.
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- Please use --deterministic in this benchmark.
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Please refer to the directories `configs/benchmarks/classification/imagenet/imagenet_1percent/` of 1% data and `configs/benchmarks/classification/imagenet/imagenet_10percent/` 10% data for details.
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| Model | Pretrain Config | Fine-tuned Config | Top-1 (%) | Top-5 (%) |
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| --------- | ---------------------------------------------------------------------------- | ----------------- | --------- | --------- |
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| [model]() | [resnet50_8xb16-steplr-70e](rotation-pred_resnet50_8xb16-steplr-70e_in1k.py) | | | |
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### Detection
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The detection benchmarks includes 2 downstream task datasets, **Pascal VOC 2007 + 2012** and **COCO2017**. This benchmark follows the evluation protocols set up by MoCo.
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#### Pascal VOC 2007 + 2012
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Please refer to [faster_rcnn_r50_c4_mstrain_24k.py](../../benchmarks/mmdetection/voc0712/faster_rcnn_r50_c4_mstrain_24k.py) for details of config.
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| Model | Config | mAP | AP50 |
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| --------- | ---------------------------------------------------------------------------- | --- | ---- |
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| [model]() | [resnet50_8xb16-steplr-70e](rotation-pred_resnet50_8xb16-steplr-70e_in1k.py) | | |
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#### COCO2017
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Please refer to [mask_rcnn_r50_fpn_mstrain_1x.py](../../benchmarks/mmdetection/coco/mask_rcnn_r50_fpn_mstrain_1x.py) for details of config.
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| Model | Config | mAP(Box) | AP50(Box) | AP75(Box) | mAP(Mask) | AP50(Mask) | AP75(Mask) |
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| --------- | ---------------------------------------------------------------------------- | -------- | --------- | --------- | --------- | ---------- | ---------- |
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| [model]() | [resnet50_8xb16-steplr-70e](rotation-pred_resnet50_8xb16-steplr-70e_in1k.py) | | | | | | |
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### Segmentation
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The segmentation benchmarks includes 2 downstream task datasets, **Cityscapes** and **Pascal VOC 2012 + Aug**. It follows the evluation protocols set up by MMSegmentation.
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#### Pascal VOC 2012 + Aug
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Please refer to [file]() for details of config.
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| Model | Config | mIOU |
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| --------- | ---------------------------------------------------------------------------- | ---- |
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| [model]() | [resnet50_8xb16-steplr-70e](rotation-pred_resnet50_8xb16-steplr-70e_in1k.py) | |
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#### Cityscapes
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Please refer to [file]() for details of config.
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| Model | Config | mIOU |
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| --------- | ---------------------------------------------------------------------------- | ---- |
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| [model]() | [resnet50_8xb16-steplr-70e](rotation-pred_resnet50_8xb16-steplr-70e_in1k.py) | |
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