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--- |
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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-Vsl-Lab-PC-V7 |
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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-Vsl-Lab-PC-V7 |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1302 |
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- Accuracy: 0.8326 |
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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: 10 |
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- eval_batch_size: 10 |
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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: 4000 |
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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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| 0.2342 | 0.02 | 81 | 1.2321 | 0.7854 | |
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| 0.134 | 1.02 | 162 | 1.1259 | 0.8112 | |
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| 0.0524 | 2.02 | 243 | 1.2486 | 0.7768 | |
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| 0.0214 | 3.02 | 324 | 1.2101 | 0.8155 | |
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| 0.0607 | 4.02 | 405 | 1.3376 | 0.7897 | |
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| 0.0624 | 5.02 | 486 | 1.4538 | 0.7811 | |
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| 0.013 | 6.02 | 567 | 1.4118 | 0.7854 | |
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| 0.0545 | 7.02 | 648 | 1.8297 | 0.7511 | |
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| 0.0834 | 8.02 | 729 | 1.2903 | 0.7897 | |
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| 0.0261 | 9.02 | 810 | 1.3051 | 0.7983 | |
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| 0.1063 | 10.02 | 891 | 1.3785 | 0.7725 | |
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| 0.0011 | 11.02 | 972 | 1.3544 | 0.7940 | |
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| 0.0002 | 12.02 | 1053 | 1.1987 | 0.8326 | |
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| 0.0001 | 13.02 | 1134 | 1.1977 | 0.8283 | |
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| 0.0001 | 14.02 | 1215 | 1.1963 | 0.8326 | |
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| 0.0001 | 15.02 | 1296 | 1.1962 | 0.8326 | |
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| 0.0001 | 16.02 | 1377 | 1.1868 | 0.8369 | |
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| 0.0001 | 17.02 | 1458 | 1.0947 | 0.8326 | |
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| 0.0001 | 18.02 | 1539 | 1.1421 | 0.8283 | |
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| 0.007 | 19.02 | 1620 | 1.3070 | 0.7854 | |
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| 0.0001 | 20.02 | 1701 | 1.1657 | 0.8155 | |
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| 0.0001 | 21.02 | 1782 | 1.1627 | 0.8112 | |
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| 0.0001 | 22.02 | 1863 | 1.1432 | 0.8197 | |
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| 0.0001 | 23.02 | 1944 | 1.1271 | 0.8155 | |
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| 0.0001 | 24.02 | 2025 | 1.1188 | 0.8283 | |
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| 0.0957 | 25.02 | 2106 | 1.2623 | 0.8155 | |
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| 0.0001 | 26.02 | 2187 | 1.0093 | 0.8498 | |
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| 0.0001 | 27.02 | 2268 | 1.0397 | 0.8498 | |
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| 0.0001 | 28.02 | 2349 | 1.0425 | 0.8498 | |
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| 0.0001 | 29.02 | 2430 | 1.0113 | 0.8369 | |
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| 0.0001 | 30.02 | 2511 | 1.0144 | 0.8369 | |
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| 0.0001 | 31.02 | 2592 | 1.0132 | 0.8369 | |
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| 0.0001 | 32.02 | 2673 | 1.0325 | 0.8326 | |
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| 0.0321 | 33.02 | 2754 | 1.1152 | 0.8283 | |
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| 0.0001 | 34.02 | 2835 | 1.1767 | 0.8069 | |
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| 0.0001 | 35.02 | 2916 | 1.1709 | 0.8112 | |
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| 0.0001 | 36.02 | 2997 | 1.1632 | 0.8155 | |
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| 0.0001 | 37.02 | 3078 | 1.1567 | 0.8197 | |
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| 0.0001 | 38.02 | 3159 | 1.1511 | 0.8197 | |
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| 0.0001 | 39.02 | 3240 | 1.1263 | 0.8283 | |
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| 0.0001 | 40.02 | 3321 | 1.1233 | 0.8283 | |
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| 0.0001 | 41.02 | 3402 | 1.1222 | 0.8326 | |
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| 0.0001 | 42.02 | 3483 | 1.1212 | 0.8283 | |
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| 0.0001 | 43.02 | 3564 | 1.1206 | 0.8283 | |
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| 0.0001 | 44.02 | 3645 | 1.1200 | 0.8283 | |
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| 0.0001 | 45.02 | 3726 | 1.1197 | 0.8283 | |
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| 0.0001 | 46.02 | 3807 | 1.1243 | 0.8283 | |
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| 0.0001 | 47.02 | 3888 | 1.1303 | 0.8326 | |
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| 0.0001 | 48.02 | 3969 | 1.1302 | 0.8326 | |
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| 0.0001 | 49.01 | 4000 | 1.1302 | 0.8326 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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