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| # dataset settings | ||
| data_source = 'ImageNet' | ||
| train_dataset_type = 'MultiViewDataset' | ||
| extract_dataset_type = 'SingleViewDataset' | ||
| img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | ||
| train_pipeline = [ | ||
| dict(type='RandomResizedCrop', size=224, scale=(0.2, 1.)), | ||
| dict( | ||
| type='RandomAppliedTrans', | ||
| transforms=[ | ||
| dict( | ||
| type='ColorJitter', | ||
| brightness=0.4, | ||
| contrast=0.4, | ||
| saturation=0.4, | ||
| hue=0.1) | ||
| ], | ||
| p=0.8), | ||
| dict(type='RandomGrayscale', p=0.2), | ||
| dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=0.5), | ||
| dict(type='RandomHorizontalFlip'), | ||
| ] | ||
| extract_pipeline = [ | ||
| dict(type='Resize', size=256), | ||
| dict(type='CenterCrop', size=224), | ||
| ] | ||
|
|
||
| # prefetch | ||
| prefetch = False | ||
| if not prefetch: | ||
| train_pipeline.extend( | ||
| [dict(type='ToTensor'), | ||
| dict(type='Normalize', **img_norm_cfg)]) | ||
| extract_pipeline.extend( | ||
| [dict(type='ToTensor'), | ||
| dict(type='Normalize', **img_norm_cfg)]) | ||
|
|
||
| # dataset summary | ||
| data = dict( | ||
| samples_per_gpu=32, # total 32*8=256 | ||
| replace=True, | ||
| workers_per_gpu=4, | ||
| drop_last=True, | ||
| train=dict( | ||
| type=train_dataset_type, | ||
| data_source=dict( | ||
| type=data_source, | ||
| data_prefix='data/imagenet/train', | ||
| ann_file='data/imagenet/meta/train.txt', | ||
| ), | ||
| num_views=[2], | ||
| pipelines=[train_pipeline], | ||
| prefetch=prefetch)) | ||
|
|
||
| # additional hooks | ||
| num_classes = 10000 | ||
| custom_hooks = [ | ||
| dict( | ||
| type='InterCLRHook', | ||
| extractor=dict( | ||
| samples_per_gpu=256, | ||
| workers_per_gpu=8, | ||
| dataset=dict( | ||
| type=extract_dataset_type, | ||
| data_source=dict( | ||
| type=data_source, | ||
| data_prefix='data/imagenet/train', | ||
| ann_file='data/imagenet/meta/train.txt', | ||
| ), | ||
| pipeline=extract_pipeline, | ||
| prefetch=prefetch), | ||
| prefetch=prefetch, | ||
| img_norm_cfg=img_norm_cfg), | ||
| clustering=dict(type='Kmeans', k=num_classes, pca_dim=-1), # no pca | ||
| centroids_update_interval=10, # iter | ||
| deal_with_small_clusters_interval=1, | ||
| evaluate_interval=50, | ||
| warmup_epochs=0, | ||
| init_memory=True, | ||
| initial=True, # call initially | ||
| online_labels=True, | ||
| interval=10) # same as the checkpoint interval | ||
| ] |
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| # model settings | ||
| model = dict( | ||
| type='InterCLRMoCo', | ||
| queue_len=65536, | ||
| feat_dim=128, | ||
| momentum=0.999, | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=50, | ||
| in_channels=3, | ||
| out_indices=[4], # 0: conv-1, x: stage-x | ||
| norm_cfg=dict(type='BN')), | ||
| neck=dict( | ||
| type='MoCoV2Neck', | ||
| in_channels=2048, | ||
| hid_channels=2048, | ||
| out_channels=128, | ||
| with_avg_pool=True), | ||
| head=dict(type='ContrastiveHead', temperature=0.2), | ||
| memory_bank=dict( | ||
| type='InterCLRMemory', | ||
| length=1281167, | ||
| feat_dim=128, | ||
| momentum=1., | ||
| num_classes=10000, | ||
| min_cluster=20, | ||
| debug=False), | ||
| online_labels=True, | ||
| neg_num=16384, | ||
| neg_sampling='semihard', # 'hard', 'semihard', 'random', 'semieasy' | ||
| semihard_neg_pool_num=128000, | ||
| semieasy_neg_pool_num=128000, | ||
| intra_cos_marign_loss=False, | ||
| intra_cos_margin=0, | ||
| inter_cos_marign_loss=True, | ||
| inter_cos_margin=-0.5, | ||
| intra_loss_weight=0.75, | ||
| inter_loss_weight=0.25, | ||
| share_neck=True, | ||
| num_classes=10000) |
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| # InterCLR | ||
|
|
||
| > [Delving into Inter-Image Invariance for Unsupervised Visual Representations](https://arxiv.org/abs/2008.11702) | ||
| <!-- [ALGORITHM] --> | ||
|
|
||
| ## Abstract | ||
|
|
||
| Contrastive learning has recently shown immense | ||
| potential in unsupervised visual representation learning. Existing studies in this track mainly focus on intra-image invariance learning. The learning typically uses rich intraimage transformations to construct positive pairs and then | ||
| maximizes agreement using a contrastive loss. The merits | ||
| of inter-image invariance, conversely, remain much less explored. One major obstacle to exploit inter-image invariance | ||
| is that it is unclear how to reliably construct inter-image | ||
| positive pairs, and further derive effective supervision from | ||
| them since no pair annotations are available. In this work, | ||
| we present a comprehensive empirical study to better understand the role of inter-image invariance learning from three main constituting components: pseudo-label maintenance, | ||
| sampling strategy, and decision boundary design. To facilitate the study, we introduce a unified and generic framework that supports the integration of unsupervised intra- and | ||
| inter-image invariance learning. Through carefully-designed | ||
| comparisons and analysis, multiple valuable observations | ||
| are revealed: 1) online labels converge faster and perform | ||
| better than offline labels; 2) semi-hard negative samples are more reliable and unbiased than hard negative samples; 3) a | ||
| less stringent decision boundary is more favorable for interimage invariance learning. With all the obtained recipes, our final model, namely InterCLR, shows consistent improvements over state-of-the-art intra-image invariance learning methods on multiple standard benchmarks. We hope this | ||
| work will provide useful experience for devising effective unsupervised inter-image invariance learning. | ||
|
|
||
| <div align="center"> | ||
| <img src="https://user-images.githubusercontent.com/52497952/205854109-2385b765-e12b-4e22-b7b8-45db6292895b.png" width="800" /> | ||
| </div> | ||
|
|
||
| ## Results and Models | ||
|
|
||
| In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models are pre-trained on ImageNet-1k dataset. Here, we use MoCov2-InterCLR as an example. More models and results are coming soon. | ||
|
|
||
| ### Classification | ||
|
|
||
| #### 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. | ||
|
|
||
| | Self-Supervised Config | Best Layer | SVM | k=1 | k=2 | k=4 | k=8 | k=16 | k=32 | k=64 | k=96 | | ||
| | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ---- | ----- | | ||
| | [interclr-moco_resnet50_8xb32-coslr-200e](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/interclr/interclr-moco_resnet50_8xb32-coslr-200e_in1k.py) | feature5 | 85.24 | 45.08 | 59.25 | 65.99 | 74.31 | 77.95 | 80.68 | 82.7 | 83.49 | | ||
|
|
||
| #### 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_linear-8xb32-steplr-90e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/resnet50_mhead_linear-8xb32-steplr-90e_in1k.py) for details of config. | ||
|
|
||
| | Self-Supervised Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | | ||
| | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | | ||
| | [interclr-moco_resnet50_8xb32-coslr-200e](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/interclr/interclr-moco_resnet50_8xb32-coslr-200e_in1k.py) | 15.59 | 35.10 | 47.36 | 62.86 | 68.04 | | ||
|
|
||
| ## Citation | ||
|
|
||
| ```bibtex | ||
| @article{xie2022delving, | ||
| title={Delving into inter-image invariance for unsupervised visual representations}, | ||
| author={Xie, Jiahao and Zhan, Xiaohang and Liu, Ziwei and Ong, Yew-Soon and Loy, Chen Change}, | ||
| journal={International Journal of Computer Vision}, | ||
| year={2022} | ||
| } | ||
| ``` |
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configs/selfsup/interclr/interclr-moco_resnet50_8xb32-coslr-200e_in1k.py
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| _base_ = [ | ||
| '../_base_/models/interclr-moco.py', | ||
| '../_base_/datasets/imagenet_interclr-moco.py', | ||
| '../_base_/schedules/sgd_coslr-200e_in1k.py', | ||
| '../_base_/default_runtime.py', | ||
| ] | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| memory_bank=dict(num_classes={{_base_.num_classes}}), | ||
| num_classes={{_base_.num_classes}}, | ||
| ) | ||
|
|
||
| # runtime settings | ||
| # the max_keep_ckpts controls the max number of ckpt file in your work_dirs | ||
| # if it is 3, when CheckpointHook (in mmcv) saves the 4th ckpt | ||
| # it will remove the oldest one to keep the number of total ckpts as 3 | ||
| checkpoint_config = dict(interval=10, max_keep_ckpts=3) |
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