Comments (13)
Is there any news on checkpoints of k400? Looking forward it as well.
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@June01 The weight in "Kinetics400-pretrained models" are self-supervised trained on K400 only.
Hmm, it seems your retrieval result here "1NN=0.5062" is better than what I got with the same model last year. But anyway in our paper, we only report NN-retrieval with UCF101-pretrained weights.
@thematrixduo filename - sorry, they are the same file, I changed the name. What accuracy did you get when reproducing? If UCF101-RGB finetune is about 86% - 88% I think it's acceptable. Our 90+ result is obtained by fusing two-stream predictions.
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Maybe later this week or next week, I am still doing some final check.
Will let you know in this issue if it's uploaded.
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Maybe later this week or next week, I am still doing some final check.
Will let you know in this issue if it's uploaded.
Thanks! Look forward to your update!
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Also waiting here, as I cannot reproduce the results for K400 training.
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Same here.
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Sorry for the long delay... they have been uploaded now.
https://github.com/TengdaHan/CoCLR#pretrained-weights
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Thanks for uploading this. Strangely I cannot reproduce this result using your given instructions. I noticed that in your infoNCE training you run 'main_infonce.py' and 'teco_fb_main.py' instead of 'main_nce.py'. Are they the same files?
In fact, I cannot reproduce the result for ucf101 pretraining either. If someone else succeeded in reproducing the result using latest pytorch package please let me know.
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@TengdaHan Hi Tengda, thanks for uploading it! I am a little bit confused, is it just the weights of training on k400 only? Or a joint training of k400 firstly and then ucf101? Here is the retrieval performance I got on UCF101 without any training further:
1NN acc = 0.5062
5NN acc = 0.6845
10NN acc = 0.7638
20NN acc = 0.8371
50NN acc = 0.9082
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@
@June01 The weight in "Kinetics400-pretrained models" are self-supervised trained on K400 only.
Hmm, it seems your retrieval result here "1NN=0.5062" is better than what I got with the same model last year. But anyway in our paper, we only report NN-retrieval with UCF101-pretrained weights.@thematrixduo filename - sorry, they are the same file, I changed the name. What accuracy did you get when reproducing? If UCF101-RGB finetune is about 86% - 88% I think it's acceptable. Our 90+ result is obtained by fusing two-stream predictions.
Thanks for the reply. I can only get 82% for K400-Pretraining, and only 78% for UCF101-Pretraining (2-Cycles). For UCF101-pretraining I used your uploaded lmdb data, and strictly followed the instructions given here.
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Did you pretrain any other architectures like R(2+1)D-18 on Kinetics 400.
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@fmthoker No, we didn't. We only used S3D backbone in our experiment.
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@thematrixduo I updated the code that fixed an issue that might reduce the training efficiency of the co-training stage. Maybe it's related to UCF101 reproduction: #43
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Related Issues (20)
- 2-Stream evaluation on UCF101 HOT 1
- Possible to share S3D model trained on UCF101 -RGB only end to end fine tuned (reported accuracy 81.4) HOT 1
- Making Kinetics-400 RGB lmdb dataset. HOT 2
- Command for Linear Probe and Finetuning HOT 1
- How to generate corresponding 'video_source.json'? HOT 1
- AttributeError: 'str' object has no attribute 'decode' HOT 2
- information about requirements and dateset preparations HOT 1
- lmdb_dataset.py txn.get(self.get_video_id[name].encode('ascii'))) HOT 6
- Issue about DistributedDataParallel(DDP) HOT 3
- Reproducing CoCLR RGB Results??? HOT 1
- fusing two-stream predictions HOT 1
- Question about the code in eval/main_classifier.py HOT 2
- question about ten crop
- Can we design a downstream task for action detection? HOT 5
- Two-stream feature
- TypeError: order must be str, not int HOT 2
- main_classifier.py: error: unrecognized arguments: --final_bn
- I can't download the pretrained model HOT 1
- A question about convert_video_to_lmdb.py HOT 2
- Download Link of Checkpoint HOT 2
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