|
--- |
|
license: cc-by-nc-4.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: videomae-base-finetuned-ucf101-subset |
|
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-ucf101-subset |
|
|
|
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4171 |
|
- Accuracy: 0.9079 |
|
|
|
## 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: 4 |
|
- eval_batch_size: 4 |
|
- 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: 3750 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 2.3212 | 0.02 | 75 | 2.2295 | 0.2581 | |
|
| 1.7775 | 1.02 | 150 | 1.7800 | 0.3871 | |
|
| 1.0633 | 2.02 | 225 | 0.9655 | 0.5484 | |
|
| 0.3783 | 3.02 | 300 | 0.5901 | 0.7419 | |
|
| 0.756 | 4.02 | 375 | 0.9142 | 0.6774 | |
|
| 0.5186 | 5.02 | 450 | 0.7384 | 0.7742 | |
|
| 0.3714 | 6.02 | 525 | 1.1662 | 0.7742 | |
|
| 0.0263 | 7.02 | 600 | 0.9102 | 0.8065 | |
|
| 0.0848 | 8.02 | 675 | 0.1210 | 0.9355 | |
|
| 0.0028 | 9.02 | 750 | 0.2374 | 0.9355 | |
|
| 0.0863 | 10.02 | 825 | 0.1728 | 0.9677 | |
|
| 0.0018 | 11.02 | 900 | 0.3043 | 0.9355 | |
|
| 0.1662 | 12.02 | 975 | 0.4244 | 0.9032 | |
|
| 0.0132 | 13.02 | 1050 | 0.6966 | 0.8710 | |
|
| 0.0019 | 14.02 | 1125 | 0.5602 | 0.8710 | |
|
| 0.0012 | 15.02 | 1200 | 0.2649 | 0.8710 | |
|
| 0.0061 | 16.02 | 1275 | 0.7361 | 0.8710 | |
|
| 0.0012 | 17.02 | 1350 | 0.2821 | 0.9355 | |
|
| 0.001 | 18.02 | 1425 | 0.3117 | 0.9355 | |
|
| 0.001 | 19.02 | 1500 | 0.1193 | 0.9677 | |
|
| 0.0021 | 20.02 | 1575 | 0.2413 | 0.9355 | |
|
| 0.0009 | 21.02 | 1650 | 0.1641 | 0.9677 | |
|
| 0.0016 | 22.02 | 1725 | 0.1333 | 0.9677 | |
|
| 0.0007 | 23.02 | 1800 | 0.1060 | 0.9677 | |
|
| 0.0008 | 24.02 | 1875 | 0.1112 | 0.9677 | |
|
| 0.0006 | 25.02 | 1950 | 0.0160 | 1.0 | |
|
| 0.0006 | 26.02 | 2025 | 0.0291 | 0.9677 | |
|
| 0.0006 | 27.02 | 2100 | 0.0860 | 0.9677 | |
|
| 0.0005 | 28.02 | 2175 | 0.1080 | 0.9677 | |
|
| 0.0006 | 29.02 | 2250 | 0.1120 | 0.9677 | |
|
| 0.0006 | 30.02 | 2325 | 0.0593 | 0.9677 | |
|
| 0.0005 | 31.02 | 2400 | 0.1660 | 0.9677 | |
|
| 0.0005 | 32.02 | 2475 | 0.0454 | 0.9677 | |
|
| 0.0007 | 33.02 | 2550 | 0.1015 | 0.9677 | |
|
| 0.0005 | 34.02 | 2625 | 0.1712 | 0.9677 | |
|
| 0.0005 | 35.02 | 2700 | 0.1600 | 0.9677 | |
|
| 0.0004 | 36.02 | 2775 | 0.1618 | 0.9677 | |
|
| 0.0004 | 37.02 | 2850 | 0.1468 | 0.9677 | |
|
| 0.0004 | 38.02 | 2925 | 0.1167 | 0.9677 | |
|
| 0.0004 | 39.02 | 3000 | 0.1278 | 0.9677 | |
|
| 0.0004 | 40.02 | 3075 | 0.1200 | 0.9677 | |
|
| 0.0004 | 41.02 | 3150 | 0.1200 | 0.9677 | |
|
| 0.0004 | 42.02 | 3225 | 0.1230 | 0.9677 | |
|
| 0.0004 | 43.02 | 3300 | 0.1323 | 0.9677 | |
|
| 0.0004 | 44.02 | 3375 | 0.1283 | 0.9677 | |
|
| 0.0004 | 45.02 | 3450 | 0.1330 | 0.9677 | |
|
| 0.0004 | 46.02 | 3525 | 0.1341 | 0.9677 | |
|
| 0.0004 | 47.02 | 3600 | 0.1305 | 0.9677 | |
|
| 0.0004 | 48.02 | 3675 | 0.1309 | 0.9677 | |
|
| 0.0004 | 49.02 | 3750 | 0.1304 | 0.9677 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.24.0 |
|
- Pytorch 1.8.0+cu111 |
|
- Datasets 2.7.1 |
|
- Tokenizers 0.13.2 |
|
|
