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---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-kinetics-finetuned-nba-binary-data-2-batch-50-epochs-new-database
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-kinetics-finetuned-nba-binary-data-2-batch-50-epochs-new-database
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.
It achieves the following results on the evaluation set:
- Loss: 0.1744
- Accuracy: 0.965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6618 | 0.02 | 200 | 0.6293 | 0.6875 |
| 0.5781 | 1.02 | 400 | 1.4660 | 0.6042 |
| 0.8554 | 2.02 | 600 | 0.8740 | 0.6667 |
| 0.4445 | 3.02 | 800 | 1.0660 | 0.6667 |
| 0.3265 | 4.02 | 1000 | 0.6635 | 0.7708 |
| 0.5417 | 5.02 | 1200 | 0.4705 | 0.8542 |
| 0.5912 | 6.02 | 1400 | 1.0082 | 0.7708 |
| 0.5918 | 7.02 | 1600 | 2.6292 | 0.5625 |
| 0.8992 | 8.02 | 1800 | 0.8514 | 0.7708 |
| 0.172 | 9.02 | 2000 | 0.4568 | 0.875 |
| 0.493 | 10.02 | 2200 | 0.7354 | 0.7917 |
| 0.3622 | 11.02 | 2400 | 1.0386 | 0.7708 |
| 0.4966 | 12.02 | 2600 | 0.8979 | 0.7917 |
| 0.3541 | 13.02 | 2800 | 0.8220 | 0.7708 |
| 0.5386 | 14.02 | 3000 | 1.0256 | 0.7708 |
| 0.4615 | 15.02 | 3200 | 1.0447 | 0.7917 |
| 0.1624 | 16.02 | 3400 | 0.6448 | 0.8542 |
| 1.0388 | 17.02 | 3600 | 0.9992 | 0.7708 |
| 0.0442 | 18.02 | 3800 | 1.1626 | 0.7708 |
| 0.2449 | 19.02 | 4000 | 0.8174 | 0.8542 |
| 0.3024 | 20.02 | 4200 | 0.8500 | 0.7917 |
| 0.4879 | 21.02 | 4400 | 1.2219 | 0.7292 |
| 0.4035 | 22.02 | 4600 | 0.6436 | 0.8333 |
| 0.0334 | 23.02 | 4800 | 0.7433 | 0.8333 |
| 0.4849 | 24.02 | 5000 | 0.9911 | 0.8125 |
| 0.6075 | 25.02 | 5200 | 1.2249 | 0.7083 |
| 0.3441 | 26.02 | 5400 | 0.8563 | 0.8333 |
| 0.5653 | 27.02 | 5600 | 0.4557 | 0.8958 |
| 0.196 | 28.02 | 5800 | 0.4156 | 0.8542 |
| 0.0038 | 29.02 | 6000 | 0.4562 | 0.8542 |
| 0.2696 | 30.02 | 6200 | 0.8153 | 0.7917 |
| 0.0015 | 31.02 | 6400 | 0.5923 | 0.8958 |
| 0.0036 | 32.02 | 6600 | 0.7343 | 0.875 |
| 0.3623 | 33.02 | 6800 | 0.3089 | 0.9375 |
| 0.2142 | 34.02 | 7000 | 0.6142 | 0.8958 |
| 0.0008 | 35.02 | 7200 | 0.6010 | 0.875 |
| 0.0005 | 36.02 | 7400 | 0.6238 | 0.875 |
| 0.0002 | 37.02 | 7600 | 0.5966 | 0.875 |
| 0.5 | 38.02 | 7800 | 0.6371 | 0.8542 |
| 0.0004 | 39.02 | 8000 | 0.8515 | 0.8542 |
| 0.0001 | 40.02 | 8200 | 0.5120 | 0.875 |
| 0.0069 | 41.02 | 8400 | 0.8686 | 0.8542 |
| 0.0002 | 42.02 | 8600 | 0.8801 | 0.8542 |
| 0.0001 | 43.02 | 8800 | 0.8996 | 0.8542 |
| 0.0067 | 44.02 | 9000 | 0.7670 | 0.8542 |
| 0.0001 | 45.02 | 9200 | 0.9936 | 0.8333 |
| 0.0638 | 46.02 | 9400 | 0.6616 | 0.875 |
| 0.0001 | 47.02 | 9600 | 0.7978 | 0.8542 |
| 0.0001 | 48.02 | 9800 | 0.6737 | 0.8542 |
| 0.0001 | 49.02 | 10000 | 0.5887 | 0.875 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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