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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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- f1 |
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- accuracy |
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model-index: |
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- name: videomae-finetuned-nba-5-class-4-batch-8000-vid-multiclass-4 |
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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-finetuned-nba-5-class-4-batch-8000-vid-multiclass-4 |
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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: 0.7974 |
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- F1: 0.8701 |
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- Accuracy: 0.8701 |
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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: 1.5e-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: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 50000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:| |
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| 1.3802 | 0.04 | 2000 | 1.2381 | 0.52 | 0.52 | |
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| 1.0115 | 1.04 | 4000 | 1.0522 | 0.6684 | 0.6684 | |
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| 0.9749 | 2.04 | 6000 | 0.9298 | 0.7537 | 0.7537 | |
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| 0.9048 | 3.04 | 8000 | 0.8679 | 0.7863 | 0.7863 | |
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| 0.7977 | 4.04 | 10000 | 0.8846 | 0.7811 | 0.7811 | |
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| 0.9259 | 5.04 | 12000 | 0.8018 | 0.8263 | 0.8263 | |
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| 0.6077 | 6.04 | 14000 | 0.8212 | 0.8189 | 0.8189 | |
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| 0.7102 | 7.04 | 16000 | 0.7876 | 0.8242 | 0.8242 | |
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| 0.5726 | 8.04 | 18000 | 0.8805 | 0.8232 | 0.8232 | |
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| 0.7768 | 9.04 | 20000 | 0.7490 | 0.8589 | 0.8589 | |
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| 0.6793 | 10.04 | 22000 | 0.7730 | 0.8558 | 0.8558 | |
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| 0.5765 | 11.04 | 24000 | 0.7752 | 0.8368 | 0.8368 | |
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| 0.4789 | 12.04 | 26000 | 0.7902 | 0.8484 | 0.8484 | |
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| 0.7398 | 13.04 | 28000 | 0.7603 | 0.8568 | 0.8568 | |
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| 0.6807 | 14.04 | 30000 | 0.7531 | 0.8716 | 0.8716 | |
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| 0.3262 | 15.04 | 32000 | 0.7663 | 0.8768 | 0.8768 | |
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| 0.4387 | 16.04 | 34000 | 0.7549 | 0.88 | 0.88 | |
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| 0.5013 | 17.04 | 36000 | 0.7713 | 0.8737 | 0.8737 | |
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| 0.9572 | 18.04 | 38000 | 0.7613 | 0.8684 | 0.8684 | |
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| 1.0645 | 19.04 | 40000 | 0.7618 | 0.8811 | 0.8811 | |
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| 0.4949 | 20.04 | 42000 | 0.7882 | 0.8747 | 0.8747 | |
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| 0.6131 | 21.04 | 44000 | 0.7964 | 0.8705 | 0.8705 | |
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| 0.628 | 22.04 | 46000 | 0.8089 | 0.8747 | 0.8747 | |
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| 0.5693 | 23.04 | 48000 | 0.8010 | 0.8747 | 0.8747 | |
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| 0.4764 | 24.04 | 50000 | 0.8117 | 0.8789 | 0.8789 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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