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billskar23
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videomae-base-videoMAE

Video Classification
Transformers
TensorBoard
Safetensors
videomae
Generated from Trainer
Inference Endpoints
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  1. README.md +31 -31
  2. model.safetensors +1 -1
  3. runs/Sep30_15-29-20_hmudgx/events.out.tfevents.1727699364.hmudgx.644246.2 +2 -2
README.md CHANGED
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0000
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- - Accuracy: 1.0
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- - Precision: 1.0
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- - Recall: 1.0
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- - F1: 1.0
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  ## Model description
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@@ -51,37 +51,37 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - training_steps: 12850
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 0.5601 | 1.0 | 515 | 0.4608 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.6361 | 2.0 | 1030 | 2.9149 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 0.1473 | 3.0 | 1545 | 0.0617 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0012 | 4.0 | 2060 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.096 | 5.0 | 2575 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.1104 | 6.0 | 3090 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.2175 | 7.0 | 3605 | 2.6599 | 0.6667 | 1.0 | 0.6667 | 0.8000 |
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- | 0.0004 | 8.0 | 4120 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.1348 | 9.0 | 4635 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0001 | 10.0 | 5150 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.1291 | 11.0 | 5665 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.174 | 12.0 | 6180 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0002 | 13.0 | 6695 | 0.0019 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0001 | 14.0 | 7210 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 15.0 | 7725 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 16.0 | 8240 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 17.0 | 8755 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 18.0 | 9270 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 19.0 | 9785 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 20.0 | 10300 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 21.0 | 10815 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 22.0 | 11330 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 23.0 | 11845 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 24.0 | 12360 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 24.9515 | 12850 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.6956
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+ - Accuracy: 0.8571
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+ - Precision: 0.8929
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+ - Recall: 0.8571
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+ - F1: 0.8571
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - training_steps: 12825
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.4984 | 1.0 | 514 | 1.3741 | 0.4286 | 0.1837 | 0.4286 | 0.2571 |
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+ | 0.3492 | 2.0 | 1028 | 0.9819 | 0.4286 | 0.1837 | 0.4286 | 0.2571 |
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+ | 0.4071 | 3.0 | 1542 | 2.8074 | 0.4286 | 0.1837 | 0.4286 | 0.2571 |
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+ | 0.3151 | 4.0 | 2056 | 0.2223 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0053 | 5.0 | 2570 | 4.9944 | 0.4286 | 0.1837 | 0.4286 | 0.2571 |
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+ | 0.0017 | 6.0 | 3084 | 0.3940 | 0.8571 | 0.8857 | 0.8571 | 0.8508 |
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+ | 0.0092 | 7.0 | 3598 | 3.8118 | 0.4286 | 0.1837 | 0.4286 | 0.2571 |
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+ | 0.0003 | 8.0 | 4112 | 4.5879 | 0.4286 | 0.1837 | 0.4286 | 0.2571 |
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+ | 0.0973 | 9.0 | 4626 | 2.7723 | 0.4286 | 0.1837 | 0.4286 | 0.2571 |
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+ | 0.0011 | 10.0 | 5140 | 4.0052 | 0.4286 | 0.1837 | 0.4286 | 0.2571 |
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+ | 0.0 | 11.0 | 5654 | 1.5523 | 0.5714 | 0.7857 | 0.5714 | 0.5143 |
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+ | 0.0 | 12.0 | 6168 | 1.4310 | 0.7143 | 0.8286 | 0.7143 | 0.7024 |
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+ | 0.0 | 13.0 | 6682 | 1.4236 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 14.0 | 7196 | 1.4245 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 15.0 | 7710 | 1.4416 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 16.0 | 8224 | 1.4639 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 17.0 | 8738 | 1.4884 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 18.0 | 9252 | 1.5161 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 19.0 | 9766 | 1.5452 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 20.0 | 10280 | 1.5755 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 21.0 | 10794 | 1.6078 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 22.0 | 11308 | 1.6386 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 23.0 | 11822 | 1.6669 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 24.0 | 12336 | 1.6868 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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+ | 0.0 | 24.9514 | 12825 | 1.6956 | 0.8571 | 0.8929 | 0.8571 | 0.8571 |
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  ### Framework versions
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