Comments (1)
Hi, we actually also train clip32 for 100k steps to get the results in the main paper. we train clip32 for 50k steps for the ablations on the pretraining datasets in the appendix because there are fewer data in that setting.
In practice, we found that training models for 50k steps can achieve pretty decent performance.
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Related Issues (20)
- About license HOT 2
- How much is the per gpu batch size? HOT 5
- The results of COCO IR/TR HOT 3
- question about the pre-trained weights HOT 2
- Pre-trained models for the Merged Attention Model? HOT 2
- ValueError and AttributeError HOT 4
- questions about vqa HOT 2
- Why the test results are different using same data? HOT 1
- The model meter_clip16_288_roberta_flickr.ckpt is inconsistent with the network weight parameter dimension HOT 4
- The last checkpoint or the best one on the Val split? HOT 3
- The training set of using different pretraining datasets. HOT 2
- Pretrained weights of CLIP-ViT-224/32
- Inference with Fine-tuned SNLI Model HOT 4
- Unable to train models faster with more gpus HOT 3
- pretraining task HOT 4
- Image Caption Script HOT 5
- > Hello, you may check 'Evaluation on Downstream Tasks' in README.md and find whether it's what you want? ![image](https://user-images.githubusercontent.com/71176040/201471719-fc32a022-201d-4c9d-acee-03d39451da56.png)
- Is P100 okay? HOT 1
- Resume pre-training using "resume_from_checkpoint" in pytorch lightning HOT 2
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