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🦾 Main experiments
Pretrain on Imagenet -> finetune on COCO or PASCAL:
1. Pretrain on Imagenet in a self-supervised regime using the proposed DetCon approach.
2. Use the self-supervised pretraining of the backbone to initialize Mask-RCNN and fine-tune it with GT labels for 12 epochs on COCO or 45 epochs on PASCAL (Semantic Segmentation).
3. Achieve SOTA results while using 5x fewer pretraining epochs than SimCLR.
Pretrain on COCO -> finetune on PASCAL for Semantic Segmentation task:
1. Pretrain on COCO in self-supervised regime using the proposed DetCon approach.
2. Use the self-supervised pretraining of the backbone to initialize Mask-RCNN and fine-tune it with GT labels for 45 epochs on PASCAL (Semantic Segmentation).
3. Achieve SOTA results while using 4x fewer pretraining epochs than SimCLR.
5. The first time a self-supervised pretrained ResNet-50 backbone outperforms supervised pretraining on COCO.
📝 Paper: Efficient Visual Pretraining with Contrastive Detection
Pretrain on Imagenet -> finetune on COCO or PASCAL:
1. Pretrain on Imagenet in a self-supervised regime using the proposed DetCon approach.
2. Use the self-supervised pretraining of the backbone to initialize Mask-RCNN and fine-tune it with GT labels for 12 epochs on COCO or 45 epochs on PASCAL (Semantic Segmentation).
3. Achieve SOTA results while using 5x fewer pretraining epochs than SimCLR.
Pretrain on COCO -> finetune on PASCAL for Semantic Segmentation task:
1. Pretrain on COCO in self-supervised regime using the proposed DetCon approach.
2. Use the self-supervised pretraining of the backbone to initialize Mask-RCNN and fine-tune it with GT labels for 45 epochs on PASCAL (Semantic Segmentation).
3. Achieve SOTA results while using 4x fewer pretraining epochs than SimCLR.
5. The first time a self-supervised pretrained ResNet-50 backbone outperforms supervised pretraining on COCO.
📝 Paper: Efficient Visual Pretraining with Contrastive Detection
🦾 Main experiments
Pretrain on Imagenet -> finetune on COCO or PASCAL:
1. Pretrain on Imagenet in a self-supervised regime using the proposed DetCon approach.
2. Use the self-supervised pretraining of the backbone to initialize Mask-RCNN and fine-tune it with GT labels for 12 epochs on COCO or 45 epochs on PASCAL (Semantic Segmentation).
3. Achieve SOTA results while using 5x fewer pretraining epochs than SimCLR.
Pretrain on COCO -> finetune on PASCAL for Semantic Segmentation task:
1. Pretrain on COCO in self-supervised regime using the proposed DetCon approach.
2. Use the self-supervised pretraining of the backbone to initialize Mask-RCNN and fine-tune it with GT labels for 45 epochs on PASCAL (Semantic Segmentation).
3. Achieve SOTA results while using 4x fewer pretraining epochs than SimCLR.
5. The first time a self-supervised pretrained ResNet-50 backbone outperforms supervised pretraining on COCO.
📝 Paper: Efficient Visual Pretraining with Contrastive Detection
Pretrain on Imagenet -> finetune on COCO or PASCAL:
1. Pretrain on Imagenet in a self-supervised regime using the proposed DetCon approach.
2. Use the self-supervised pretraining of the backbone to initialize Mask-RCNN and fine-tune it with GT labels for 12 epochs on COCO or 45 epochs on PASCAL (Semantic Segmentation).
3. Achieve SOTA results while using 5x fewer pretraining epochs than SimCLR.
Pretrain on COCO -> finetune on PASCAL for Semantic Segmentation task:
1. Pretrain on COCO in self-supervised regime using the proposed DetCon approach.
2. Use the self-supervised pretraining of the backbone to initialize Mask-RCNN and fine-tune it with GT labels for 45 epochs on PASCAL (Semantic Segmentation).
3. Achieve SOTA results while using 4x fewer pretraining epochs than SimCLR.
5. The first time a self-supervised pretrained ResNet-50 backbone outperforms supervised pretraining on COCO.
📝 Paper: Efficient Visual Pretraining with Contrastive Detection
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