Comments (4)
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Please read the CoCoOp carefully. CoCoOp uses ViT as the backbone, while CoOp uses ResNet. To make fair comparison with CoOp and CoCoOp, we select ResNet for all experiment setting.
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16 shots is strictly not few-shot setting (conventional few-shot uses 1 or 5), thus we choose 4.
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Dear Ma Chengcheng
If you are interested in this work. We can add a WeChat friend for further discussion. My account is: Thu_E_E
Best wishes.
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Another difference I forget to mention is that in CoCoOp paper, all experiments are run only for 10 epochs due the training inefficiency of CoCoOp. But according to my training log, 10 epochs is insufficient for model convergence which makes unfair comparison between difference models. Thus I adopt CoOp setting to the base-to-new experiments, ie, 50 epochs for 1 shot, 100 epochs for 2 and 4 shots, 200 epochs for 8 and 16 shots.
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Much appreciated~
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Related Issues (10)
- Imagenet 16-shot weights
- AttributeError: 'ProGrad' object has no attribute 'prograd_backward_and_update' HOT 2
- How is Grad-CAM picture drawed? HOT 5
- Very nice job! Congratulations! HOT 1
- How about ProGrad helping Fitness HOT 1
- 数值化结果 HOT 1
- self.prograd_backward_and_update HOT 1
- How to use Grad-CAM on ProGrad HOT 1
- ProGrad++ implementation
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