Comments (5)
Hello, you may check 'Evaluation on Downstream Tasks' in README.md and find whether it's what you want?
Hi Ferry,
Thank you so much for your reply. Sorry that I didn't explain clearly about the task.
The image caption task is the task mentioned in your supplementary file, where the evaluation metrics are BLEU, METEOR, Cider and SPICE.
In addition, may I ask about the decoder experiments as I cannot see the setting of adding the optinal decoder.
Thanks for your kind help.
from meter.
Hello, you may check 'Evaluation on Downstream Tasks' in README.md and find whether it's what you want?
Hi Ferry, Thank you so much for your reply. Sorry that I didn't explain clearly about the task. The image caption task is the task mentioned in your supplementary file, where the evaluation metrics are BLEU, METEOR, Cider and SPICE. In addition, may I ask about the decoder experiments as I cannot see the setting of adding the optinal decoder. Thanks for your kind help.
I should be sorry that I actually mistake this as script for image caption task which is indeed for image/text retrieval. I'll delete my reply...Plus, I am also trying to apply an additional decoder (author seems to leave this for future work) as to applying model on image caption :)
from meter.
Hhaha, Never mind.
In the paper, the authors compare the performance with and without the decoder. Also, they verify their model on image caption task.
Therefore, I guess there is an implementation for the relevant parts and ask for the script and details, e.g., the implementation details of the decoder.
Maybe we can have more discussion later.
Thanks for your kind information.
from meter.
Hello, you can find the image captioning code in our new FIBER repo https://github.com/microsoft/FIBER/
from meter.
Hello, you can find the image captioning code in our new FIBER repo https://github.com/microsoft/FIBER/
Thanks for your reply. I will take a look. Again, thanks for sharing the code.
from meter.
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
- > 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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from meter.