Comments (2)
Thanks! It is normal as for N test instances, ranking requires computing features for NxN image-text pairs. One potential way to increase the inference speed is caching the image and text features at the bottom layers, which I left as a TODO here https://github.com/zdou0830/METER/blob/main/meter/modules/objectives.py#L315.
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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
- training steps HOT 1
- Resume pre-training using "resume_from_checkpoint" in pytorch lightning HOT 2
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