metadata
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-Vsl-Lab-PC-V9
results: []
videomae-base-Vsl-Lab-PC-V9
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3401
- Accuracy: 0.8112
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: 10
- eval_batch_size: 10
- 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: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0001 | 0.02 | 81 | 1.0305 | 0.8455 |
| 0.0001 | 1.02 | 162 | 1.0570 | 0.8412 |
| 0.0 | 2.02 | 243 | 1.0822 | 0.8369 |
| 0.0 | 3.02 | 324 | 1.1003 | 0.8412 |
| 0.0 | 4.02 | 405 | 1.1198 | 0.8412 |
| 0.5974 | 5.02 | 486 | 1.9278 | 0.6953 |
| 0.2193 | 6.02 | 567 | 1.1442 | 0.7554 |
| 0.0261 | 7.02 | 648 | 0.9625 | 0.8026 |
| 0.0859 | 8.02 | 729 | 1.1657 | 0.8155 |
| 0.0044 | 9.02 | 810 | 1.2597 | 0.8197 |
| 0.0007 | 10.02 | 891 | 1.2663 | 0.8112 |
| 0.0001 | 11.02 | 972 | 1.2367 | 0.8240 |
| 0.0002 | 12.02 | 1053 | 1.1224 | 0.8326 |
| 0.0002 | 13.02 | 1134 | 1.1528 | 0.8326 |
| 0.0 | 14.02 | 1215 | 1.1598 | 0.8326 |
| 0.0 | 15.02 | 1296 | 1.1572 | 0.8369 |
| 0.0 | 16.02 | 1377 | 1.1560 | 0.8369 |
| 0.0 | 17.02 | 1458 | 1.1555 | 0.8369 |
| 0.0002 | 18.02 | 1539 | 1.3611 | 0.8112 |
| 0.0 | 19.02 | 1620 | 1.2183 | 0.8326 |
| 0.0 | 20.02 | 1701 | 1.2105 | 0.8283 |
| 0.0 | 21.02 | 1782 | 1.2063 | 0.8283 |
| 0.0 | 22.02 | 1863 | 1.2034 | 0.8283 |
| 0.0 | 23.02 | 1944 | 1.2020 | 0.8283 |
| 0.001 | 24.02 | 2025 | 1.1831 | 0.8412 |
| 0.0687 | 25.02 | 2106 | 1.2683 | 0.8240 |
| 0.0 | 26.02 | 2187 | 1.2521 | 0.8240 |
| 0.0 | 27.02 | 2268 | 1.2430 | 0.8326 |
| 0.0 | 28.02 | 2349 | 1.2394 | 0.8326 |
| 0.0001 | 29.02 | 2430 | 1.2711 | 0.8283 |
| 0.0 | 30.02 | 2511 | 1.2562 | 0.8283 |
| 0.0 | 31.02 | 2592 | 1.2484 | 0.8326 |
| 0.0 | 32.02 | 2673 | 1.2432 | 0.8326 |
| 0.0 | 33.02 | 2754 | 1.2387 | 0.8326 |
| 0.0 | 34.02 | 2835 | 1.2349 | 0.8326 |
| 0.0 | 35.02 | 2916 | 1.2319 | 0.8326 |
| 0.0 | 36.02 | 2997 | 1.2294 | 0.8326 |
| 0.0 | 37.02 | 3078 | 1.2276 | 0.8326 |
| 0.0 | 38.02 | 3159 | 1.2245 | 0.8326 |
| 0.0 | 39.02 | 3240 | 1.2232 | 0.8326 |
| 0.0 | 40.02 | 3321 | 1.2217 | 0.8326 |
| 0.0 | 41.02 | 3402 | 1.2204 | 0.8326 |
| 0.0 | 42.02 | 3483 | 1.3696 | 0.7983 |
| 0.0001 | 43.02 | 3564 | 1.3923 | 0.7940 |
| 0.0 | 44.02 | 3645 | 1.3421 | 0.8112 |
| 0.0 | 45.02 | 3726 | 1.3414 | 0.8112 |
| 0.0 | 46.02 | 3807 | 1.3407 | 0.8112 |
| 0.0 | 47.02 | 3888 | 1.3403 | 0.8112 |
| 0.0 | 48.02 | 3969 | 1.3401 | 0.8112 |
| 0.0 | 49.01 | 4000 | 1.3401 | 0.8112 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2