vit-base-cocoa
This model is a fine-tuned version of google/vit-base-patch16-224 on the SemilleroCV/Cocoa-dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.2061
- Accuracy: 0.9278
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100.0
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.3733 | 1.0 | 196 | 0.9025 | 0.3558 |
| 0.3727 | 2.0 | 392 | 0.8989 | 0.4098 |
| 0.3901 | 3.0 | 588 | 0.8989 | 0.2668 |
| 0.3421 | 4.0 | 784 | 0.9170 | 0.2612 |
| 0.2703 | 5.0 | 980 | 0.9278 | 0.2061 |
| 0.1734 | 6.0 | 1176 | 0.9278 | 0.2568 |
| 0.1385 | 7.0 | 1372 | 0.9206 | 0.3242 |
| 0.3237 | 8.0 | 1568 | 0.9386 | 0.2922 |
| 0.236 | 9.0 | 1764 | 0.9386 | 0.3044 |
| 0.2124 | 10.0 | 1960 | 0.9061 | 0.3848 |
| 0.0454 | 11.0 | 2156 | 0.9350 | 0.3527 |
| 0.0756 | 12.0 | 2352 | 0.9350 | 0.2844 |
| 0.0605 | 13.0 | 2548 | 0.9314 | 0.3077 |
| 0.0214 | 14.0 | 2744 | 0.9025 | 0.6295 |
| 0.1816 | 15.0 | 2940 | 0.9386 | 0.2996 |
| 0.0338 | 16.0 | 3136 | 0.9278 | 0.3597 |
| 0.2136 | 17.0 | 3332 | 0.9314 | 0.4070 |
| 0.188 | 18.0 | 3528 | 0.9458 | 0.3532 |
| 0.0539 | 19.0 | 3724 | 0.9386 | 0.3843 |
| 0.0992 | 20.0 | 3920 | 0.9422 | 0.3904 |
| 0.0019 | 21.0 | 4116 | 0.9458 | 0.3732 |
| 0.0348 | 22.0 | 4312 | 0.9386 | 0.4021 |
| 0.0823 | 23.0 | 4508 | 0.9350 | 0.4217 |
| 0.1125 | 24.0 | 4704 | 0.9097 | 0.4704 |
| 0.0173 | 25.0 | 4900 | 0.9350 | 0.3700 |
| 0.0442 | 26.0 | 5096 | 0.9314 | 0.3725 |
| 0.0009 | 27.0 | 5292 | 0.9278 | 0.4819 |
| 0.0087 | 28.0 | 5488 | 0.9170 | 0.6492 |
| 0.0021 | 29.0 | 5684 | 0.9242 | 0.5297 |
| 0.2552 | 30.0 | 5880 | 0.9314 | 0.4482 |
| 0.0154 | 31.0 | 6076 | 0.9242 | 0.6075 |
| 0.0009 | 32.0 | 6272 | 0.9350 | 0.4101 |
| 0.1626 | 33.0 | 6468 | 0.9350 | 0.4653 |
| 0.0276 | 34.0 | 6664 | 0.9386 | 0.4174 |
| 0.0139 | 35.0 | 6860 | 0.9422 | 0.3992 |
| 0.0023 | 36.0 | 7056 | 0.9170 | 0.6972 |
| 0.1264 | 37.0 | 7252 | 0.9314 | 0.4980 |
| 0.0113 | 38.0 | 7448 | 0.9170 | 0.7154 |
| 0.0694 | 39.0 | 7644 | 0.9242 | 0.5443 |
| 0.0976 | 40.0 | 7840 | 0.9350 | 0.3852 |
| 0.1191 | 41.0 | 8036 | 0.9242 | 0.5398 |
| 0.1249 | 42.0 | 8232 | 0.9170 | 0.6197 |
| 0.0002 | 43.0 | 8428 | 0.9134 | 0.6967 |
| 0.1163 | 44.0 | 8624 | 0.9242 | 0.5697 |
| 0.0201 | 45.0 | 8820 | 0.9134 | 0.7221 |
| 0.0003 | 46.0 | 9016 | 0.9314 | 0.5253 |
| 0.0224 | 47.0 | 9212 | 0.9495 | 0.3817 |
| 0.0183 | 48.0 | 9408 | 0.9242 | 0.4966 |
| 0.0077 | 49.0 | 9604 | 0.9458 | 0.4349 |
| 0.0083 | 50.0 | 9800 | 0.9242 | 0.5191 |
| 0.0571 | 51.0 | 9996 | 0.9206 | 0.5826 |
| 0.0583 | 52.0 | 10192 | 0.9170 | 0.5335 |
| 0.0019 | 53.0 | 10388 | 0.9206 | 0.5843 |
| 0.0044 | 54.0 | 10584 | 0.9206 | 0.5895 |
| 0.0065 | 55.0 | 10780 | 0.9350 | 0.4487 |
| 0.0126 | 56.0 | 10976 | 0.9314 | 0.6221 |
| 0.0093 | 57.0 | 11172 | 0.9314 | 0.5138 |
| 0.0004 | 58.0 | 11368 | 0.9314 | 0.5162 |
| 0.0002 | 59.0 | 11564 | 0.9350 | 0.4514 |
| 0.1463 | 60.0 | 11760 | 0.9386 | 0.4744 |
| 0.0001 | 61.0 | 11956 | 0.9314 | 0.5338 |
| 0.0006 | 62.0 | 12152 | 0.9278 | 0.5788 |
| 0.0269 | 63.0 | 12348 | 0.9278 | 0.5500 |
| 0.1 | 64.0 | 12544 | 0.9206 | 0.6467 |
| 0.0004 | 65.0 | 12740 | 0.9242 | 0.5828 |
| 0.0001 | 66.0 | 12936 | 0.9314 | 0.5283 |
| 0.0001 | 67.0 | 13132 | 0.9206 | 0.6212 |
| 0.0002 | 68.0 | 13328 | 0.9242 | 0.4973 |
| 0.0058 | 69.0 | 13524 | 0.9278 | 0.5021 |
| 0.0605 | 70.0 | 13720 | 0.9170 | 0.6982 |
| 0.0006 | 71.0 | 13916 | 0.9350 | 0.4602 |
| 0.0021 | 72.0 | 14112 | 0.9314 | 0.5595 |
| 0.0004 | 73.0 | 14308 | 0.9386 | 0.4366 |
| 0.0124 | 74.0 | 14504 | 0.9134 | 0.7612 |
| 0.0284 | 75.0 | 14700 | 0.9206 | 0.6054 |
| 0.0001 | 76.0 | 14896 | 0.9242 | 0.5922 |
| 0.0119 | 77.0 | 15092 | 0.9242 | 0.5496 |
| 0.0006 | 78.0 | 15288 | 0.9206 | 0.6327 |
| 0.0711 | 79.0 | 15484 | 0.9386 | 0.5177 |
| 0.0001 | 80.0 | 15680 | 0.9134 | 0.7391 |
| 0.0985 | 81.0 | 15876 | 0.9242 | 0.5683 |
| 0.0001 | 82.0 | 16072 | 0.9206 | 0.6106 |
| 0.0 | 83.0 | 16268 | 0.9242 | 0.6235 |
| 0.0006 | 84.0 | 16464 | 0.9061 | 0.7914 |
| 0.0001 | 85.0 | 16660 | 0.9314 | 0.5649 |
| 0.0 | 86.0 | 16856 | 0.9350 | 0.5512 |
| 0.066 | 87.0 | 17052 | 0.9350 | 0.5473 |
| 0.0189 | 88.0 | 17248 | 0.9386 | 0.4866 |
| 0.0 | 89.0 | 17444 | 0.9386 | 0.5136 |
| 0.0001 | 90.0 | 17640 | 0.9350 | 0.5246 |
| 0.0001 | 91.0 | 17836 | 0.9314 | 0.5626 |
| 0.0037 | 92.0 | 18032 | 0.9350 | 0.5335 |
| 0.0999 | 93.0 | 18228 | 0.9242 | 0.6357 |
| 0.1124 | 94.0 | 18424 | 0.9278 | 0.5905 |
| 0.0175 | 95.0 | 18620 | 0.9206 | 0.6618 |
| 0.0001 | 96.0 | 18816 | 0.9386 | 0.5588 |
| 0.0259 | 97.0 | 19012 | 0.9350 | 0.5549 |
| 0.0001 | 98.0 | 19208 | 0.9350 | 0.5599 |
| 0.0285 | 99.0 | 19404 | 0.9350 | 0.5517 |
| 0.003 | 100.0 | 19600 | 0.9350 | 0.5570 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0