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[Feature]: Add docs and docker
2021-12-15 19:06:36 +08:00
# ODC
## Online Deep Clustering for Unsupervised Representation Learning
<!-- [ABSTRACT] -->
Joint clustering and feature learning methods have shown remarkable performance in unsupervised representation learning. However, the training schedule alternating between feature clustering and network parameters update leads to unstable learning of visual representations. To overcome this challenge, we propose Online Deep Clustering (ODC) that performs clustering and network update simultaneously rather than alternatingly. Our key insight is that the cluster centroids should evolve steadily in keeping the classifier stably updated. Specifically, we design and maintain two dynamic memory modules, i.e., samples memory to store samples’ labels and features, and centroids memory for centroids evolution. We break down the abrupt global clustering into steady memory update and batch-wise label re-assignment. The process is integrated into network update iterations. In this way, labels and the network evolve shoulder-to-shoulder rather than alternatingly. Extensive experiments demonstrate that ODC stabilizes the training process and boosts the performance effectively.
<!-- [IMAGE] -->
<div align="center">
<img />
</div>
## Citation
<!-- [ALGORITHM] -->
```bibtex
@inproceedings{zhan2020online,
title={Online deep clustering for unsupervised representation learning},
author={Zhan, Xiaohang and Xie, Jiahao and Liu, Ziwei and Ong, Yew-Soon and Loy, Chen Change},
booktitle={CVPR},
year={2020}
}
```
## Models and Benchmarks
[Back to model_zoo.md](../../../docs/model_zoo.md)
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.
### VOC SVM / Low-shot SVM
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).
Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
| Model | Config | Best Layer | SVM | k=1 | k=2 | k=4 | k=8 | k=16 | k=32 | k=64 | k=96 |
| --------- | -------------------------------------------------------------------- | ---------- | --- | --- | --- | --- | --- | ---- | ---- | ---- | ---- |
| [model]() | [resnet50_8xb64-steplr-440e](odc_resnet50_8xb64-steplr-440e_in1k.py) | | | | | | | | | | |
### ImageNet Linear Evaluation
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.
The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to [file name]() for details of config.
| Model | Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
| --------- | -------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |
| [model]() | [resnet50_8xb64-steplr-440e](odc_resnet50_8xb64-steplr-440e_in1k.py) | | | | | | |
### iNaturalist2018 Linear Evaluation
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.
| Model | Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
| --------- | -------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |
| [model]() | [resnet50_8xb64-steplr-440e](odc_resnet50_8xb64-steplr-440e_in1k.py) | | | | | | |
### Places205 Linear Evaluation
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.
| Model | Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
| --------- | -------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |
| [model]() | [resnet50_8xb64-steplr-440e](odc_resnet50_8xb64-steplr-440e_in1k.py) | | | | | | |
#### Semi-Supervised Classification
- 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.
- 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`.
- Please use --deterministic in this benchmark.
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.
| Model | Pretrain Config | Fine-tuned Config | Top-1 (%) | Top-5 (%) |
| --------- | -------------------------------------------------------------------- | ----------------- | --------- | --------- |
| [model]() | [resnet50_8xb64-steplr-440e](odc_resnet50_8xb64-steplr-440e_in1k.py) | | | |
### Detection
The detection benchmarks includes 2 downstream task datasets, **Pascal VOC 2007 + 2012** and **COCO2017**. This benchmark follows the evluation protocols set up by MoCo.
#### Pascal VOC 2007 + 2012
Please refer to [faster_rcnn_r50_c4_mstrain_24k.py](../../benchmarks/mmdetection/voc0712/faster_rcnn_r50_c4_mstrain_24k.py) for details of config.
| Model | Config | mAP | AP50 |
| --------- | -------------------------------------------------------------------- | --- | ---- |
| [model]() | [resnet50_8xb64-steplr-440e](odc_resnet50_8xb64-steplr-440e_in1k.py) | | |
#### COCO2017
Please refer to [mask_rcnn_r50_fpn_mstrain_1x.py](../../benchmarks/mmdetection/coco/mask_rcnn_r50_fpn_mstrain_1x.py) for details of config.
| Model | Config | mAP(Box) | AP50(Box) | AP75(Box) | mAP(Mask) | AP50(Mask) | AP75(Mask) |
| --------- | -------------------------------------------------------------------- | -------- | --------- | --------- | --------- | ---------- | ---------- |
| [model]() | [resnet50_8xb64-steplr-440e](odc_resnet50_8xb64-steplr-440e_in1k.py) | | | | | | |
### Segmentation
The segmentation benchmarks includes 2 downstream task datasets, **Cityscapes** and **Pascal VOC 2012 + Aug**. It follows the evluation protocols set up by MMSegmentation.
#### Pascal VOC 2012 + Aug
Please refer to [file]() for details of config.
| Model | Config | mIOU |
| --------- | -------------------------------------------------------------------- | ---- |
| [model]() | [resnet50_8xb64-steplr-440e](odc_resnet50_8xb64-steplr-440e_in1k.py) | |
#### Cityscapes
Please refer to [file]() for details of config.
| Model | Config | mIOU |
| --------- | -------------------------------------------------------------------- | ---- |
| [model]() | [resnet50_8xb64-steplr-440e](odc_resnet50_8xb64-steplr-440e_in1k.py) | |