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--- |
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license: cc-by-nc-4.0 |
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base_model: MCG-NJU/videomae-base-finetuned-kinetics |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: videomae-base-finetuned-kinetics-finetuned-nba-binary-data-2-batch-50-epochs-new-database |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# videomae-base-finetuned-kinetics-finetuned-nba-binary-data-2-batch-50-epochs-new-database |
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This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co./MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1744 |
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- Accuracy: 0.965 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 2 |
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- eval_batch_size: 2 |
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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: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.6618 | 0.02 | 200 | 0.6293 | 0.6875 | |
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| 0.5781 | 1.02 | 400 | 1.4660 | 0.6042 | |
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| 0.8554 | 2.02 | 600 | 0.8740 | 0.6667 | |
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| 0.4445 | 3.02 | 800 | 1.0660 | 0.6667 | |
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| 0.3265 | 4.02 | 1000 | 0.6635 | 0.7708 | |
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| 0.5417 | 5.02 | 1200 | 0.4705 | 0.8542 | |
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| 0.5912 | 6.02 | 1400 | 1.0082 | 0.7708 | |
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| 0.5918 | 7.02 | 1600 | 2.6292 | 0.5625 | |
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| 0.8992 | 8.02 | 1800 | 0.8514 | 0.7708 | |
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| 0.172 | 9.02 | 2000 | 0.4568 | 0.875 | |
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| 0.493 | 10.02 | 2200 | 0.7354 | 0.7917 | |
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| 0.3622 | 11.02 | 2400 | 1.0386 | 0.7708 | |
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| 0.4966 | 12.02 | 2600 | 0.8979 | 0.7917 | |
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| 0.3541 | 13.02 | 2800 | 0.8220 | 0.7708 | |
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| 0.5386 | 14.02 | 3000 | 1.0256 | 0.7708 | |
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| 0.4615 | 15.02 | 3200 | 1.0447 | 0.7917 | |
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| 0.1624 | 16.02 | 3400 | 0.6448 | 0.8542 | |
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| 1.0388 | 17.02 | 3600 | 0.9992 | 0.7708 | |
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| 0.0442 | 18.02 | 3800 | 1.1626 | 0.7708 | |
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| 0.2449 | 19.02 | 4000 | 0.8174 | 0.8542 | |
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| 0.3024 | 20.02 | 4200 | 0.8500 | 0.7917 | |
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| 0.4879 | 21.02 | 4400 | 1.2219 | 0.7292 | |
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| 0.4035 | 22.02 | 4600 | 0.6436 | 0.8333 | |
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| 0.0334 | 23.02 | 4800 | 0.7433 | 0.8333 | |
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| 0.4849 | 24.02 | 5000 | 0.9911 | 0.8125 | |
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| 0.6075 | 25.02 | 5200 | 1.2249 | 0.7083 | |
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| 0.3441 | 26.02 | 5400 | 0.8563 | 0.8333 | |
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| 0.5653 | 27.02 | 5600 | 0.4557 | 0.8958 | |
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| 0.196 | 28.02 | 5800 | 0.4156 | 0.8542 | |
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| 0.0038 | 29.02 | 6000 | 0.4562 | 0.8542 | |
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| 0.2696 | 30.02 | 6200 | 0.8153 | 0.7917 | |
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| 0.0015 | 31.02 | 6400 | 0.5923 | 0.8958 | |
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| 0.0036 | 32.02 | 6600 | 0.7343 | 0.875 | |
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| 0.3623 | 33.02 | 6800 | 0.3089 | 0.9375 | |
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| 0.2142 | 34.02 | 7000 | 0.6142 | 0.8958 | |
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| 0.0008 | 35.02 | 7200 | 0.6010 | 0.875 | |
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| 0.0005 | 36.02 | 7400 | 0.6238 | 0.875 | |
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| 0.0002 | 37.02 | 7600 | 0.5966 | 0.875 | |
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| 0.5 | 38.02 | 7800 | 0.6371 | 0.8542 | |
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| 0.0004 | 39.02 | 8000 | 0.8515 | 0.8542 | |
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| 0.0001 | 40.02 | 8200 | 0.5120 | 0.875 | |
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| 0.0069 | 41.02 | 8400 | 0.8686 | 0.8542 | |
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| 0.0002 | 42.02 | 8600 | 0.8801 | 0.8542 | |
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| 0.0001 | 43.02 | 8800 | 0.8996 | 0.8542 | |
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| 0.0067 | 44.02 | 9000 | 0.7670 | 0.8542 | |
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| 0.0001 | 45.02 | 9200 | 0.9936 | 0.8333 | |
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| 0.0638 | 46.02 | 9400 | 0.6616 | 0.875 | |
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| 0.0001 | 47.02 | 9600 | 0.7978 | 0.8542 | |
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| 0.0001 | 48.02 | 9800 | 0.6737 | 0.8542 | |
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| 0.0001 | 49.02 | 10000 | 0.5887 | 0.875 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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