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billskar23
/
videomae-base-videoMAE

Video Classification
Transformers
TensorBoard
Safetensors
videomae
Generated from Trainer
Inference Endpoints
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Training in progress, epoch 19

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  1. README.md +30 -30
  2. model.safetensors +1 -1
  3. runs/Sep26_16-54-02_hmudgx/events.out.tfevents.1727358844.hmudgx.917888.4 +2 -2
  4. runs/Sep26_16-56-41_hmudgx/events.out.tfevents.1727359003.hmudgx.918034.4 +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: 3.0328
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- - Accuracy: 0.6154
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- - Precision: 0.3787
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- - Recall: 0.6154
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- - F1: 0.4689
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  ## Model description
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@@ -45,38 +45,38 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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- - train_batch_size: 4
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- - eval_batch_size: 4
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  - seed: 42
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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: 10240
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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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- | 0.3985 | 1.0 | 512 | 0.2857 | 0.9231 | 0.9316 | 0.9231 | 0.9211 |
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- | 0.4718 | 2.0 | 1024 | 1.4444 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.181 | 3.0 | 1536 | 3.1151 | 0.3846 | 0.3077 | 0.3846 | 0.3419 |
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- | 0.113 | 4.0 | 2048 | 1.9200 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0771 | 5.0 | 2560 | 1.4445 | 0.5385 | 0.4974 | 0.5385 | 0.5064 |
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- | 0.0003 | 6.0 | 3072 | 1.6911 | 0.7692 | 0.7839 | 0.7692 | 0.7720 |
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- | 0.0002 | 7.0 | 3584 | 2.2123 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0002 | 8.0 | 4096 | 3.1463 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0001 | 9.0 | 4608 | 1.7846 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 10.0 | 5120 | 2.4646 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 11.0 | 5632 | 2.4743 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 12.0 | 6144 | 2.5687 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 13.0 | 6656 | 2.6551 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 14.0 | 7168 | 2.7365 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 15.0 | 7680 | 2.8134 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 16.0 | 8192 | 2.8798 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 17.0 | 8704 | 2.9415 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 18.0 | 9216 | 2.9896 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 19.0 | 9728 | 3.0225 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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- | 0.0 | 20.0 | 10240 | 3.0328 | 0.6154 | 0.3787 | 0.6154 | 0.4689 |
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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: 4.7834
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+ - Accuracy: 0.5333
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+ - Precision: 0.2844
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+ - Recall: 0.5333
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+ - F1: 0.3710
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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  - seed: 42
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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: 5100
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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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+ | 0.515 | 1.0 | 256 | 0.8388 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.4899 | 2.0 | 512 | 1.6684 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.3165 | 3.0 | 768 | 1.9704 | 0.6 | 0.7714 | 0.6 | 0.5045 |
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+ | 0.053 | 4.0 | 1024 | 3.4786 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0772 | 5.0 | 1280 | 3.9716 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.1053 | 6.0 | 1536 | 2.9126 | 0.6 | 0.7714 | 0.6 | 0.5045 |
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+ | 0.0867 | 7.0 | 1792 | 3.8445 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0001 | 8.0 | 2048 | 4.1078 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0001 | 9.0 | 2304 | 4.2770 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 10.0 | 2560 | 4.3905 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 11.0 | 2816 | 4.4740 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 12.0 | 3072 | 4.5399 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 13.0 | 3328 | 4.5972 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 14.0 | 3584 | 4.6437 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 15.0 | 3840 | 4.6843 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 16.0 | 4096 | 4.7171 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 17.0 | 4352 | 4.7472 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 18.0 | 4608 | 4.7671 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 19.0 | 4864 | 4.7798 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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+ | 0.0 | 19.9219 | 5100 | 4.7834 | 0.5333 | 0.2844 | 0.5333 | 0.3710 |
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  ### Framework versions
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