metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- masked-auto-encoding
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-pretraining-2024_03_10
results: []
vit-pretraining-2024_03_10
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4444
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: 4.6875e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 200.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.0002 | 1.0 | 2443 | 1.0000 |
| 0.9832 | 2.0 | 4886 | 0.9753 |
| 0.9246 | 3.0 | 7329 | 0.9304 |
| 0.8979 | 4.0 | 9772 | 0.8855 |
| 0.8307 | 5.0 | 12215 | 0.8077 |
| 0.7861 | 6.0 | 14658 | 0.7776 |
| 0.7665 | 7.0 | 17101 | 0.7557 |
| 0.7421 | 8.0 | 19544 | 0.7337 |
| 0.6841 | 9.0 | 21987 | 0.7133 |
| 0.6875 | 10.0 | 24430 | 0.7001 |
| 0.6991 | 11.0 | 26873 | 0.6887 |
| 0.6991 | 12.0 | 29316 | 0.6711 |
| 0.6584 | 13.0 | 31759 | 0.6674 |
| 0.6619 | 14.0 | 34202 | 0.6507 |
| 0.6389 | 15.0 | 36645 | 0.6462 |
| 0.6381 | 16.0 | 39088 | 0.6370 |
| 0.616 | 17.0 | 41531 | 0.6248 |
| 0.627 | 18.0 | 43974 | 0.6213 |
| 0.6179 | 19.0 | 46417 | 0.6150 |
| 0.6226 | 20.0 | 48860 | 0.6112 |
| 0.5876 | 21.0 | 51303 | 0.6062 |
| 0.613 | 22.0 | 53746 | 0.5990 |
| 0.5864 | 23.0 | 56189 | 0.5948 |
| 0.5741 | 24.0 | 58632 | 0.5940 |
| 0.5886 | 25.0 | 61075 | 0.5883 |
| 0.6028 | 26.0 | 63518 | 0.5890 |
| 0.578 | 27.0 | 65961 | 0.5841 |
| 0.5846 | 28.0 | 68404 | 0.5779 |
| 0.5725 | 29.0 | 70847 | 0.5766 |
| 0.5684 | 30.0 | 73290 | 0.5791 |
| 0.5689 | 31.0 | 75733 | 0.5726 |
| 0.5478 | 32.0 | 78176 | 0.5708 |
| 0.5739 | 33.0 | 80619 | 0.5697 |
| 0.5578 | 34.0 | 83062 | 0.5629 |
| 0.568 | 35.0 | 85505 | 0.5696 |
| 0.5819 | 36.0 | 87948 | 0.5649 |
| 0.5442 | 37.0 | 90391 | 0.5649 |
| 0.5616 | 38.0 | 92834 | 0.5626 |
| 0.5386 | 39.0 | 95277 | 0.5617 |
| 0.5725 | 40.0 | 97720 | 0.5552 |
| 0.549 | 41.0 | 100163 | 0.5621 |
| 0.5539 | 42.0 | 102606 | 0.5535 |
| 0.5513 | 43.0 | 105049 | 0.5514 |
| 0.5538 | 44.0 | 107492 | 0.5480 |
| 0.5423 | 45.0 | 109935 | 0.5488 |
| 0.5431 | 46.0 | 112378 | 0.5466 |
| 0.5495 | 47.0 | 114821 | 0.5442 |
| 0.5593 | 48.0 | 117264 | 0.5447 |
| 0.5488 | 49.0 | 119707 | 0.5431 |
| 0.5203 | 50.0 | 122150 | 0.5391 |
| 0.5386 | 51.0 | 124593 | 0.5384 |
| 0.5498 | 52.0 | 127036 | 0.5393 |
| 0.5391 | 53.0 | 129479 | 0.5372 |
| 0.5361 | 54.0 | 131922 | 0.5363 |
| 0.5295 | 55.0 | 134365 | 0.5343 |
| 0.5227 | 56.0 | 136808 | 0.5345 |
| 0.5182 | 57.0 | 139251 | 0.5287 |
| 0.5103 | 58.0 | 141694 | 0.5303 |
| 0.5411 | 59.0 | 144137 | 0.5278 |
| 0.5187 | 60.0 | 146580 | 0.5259 |
| 0.5272 | 61.0 | 149023 | 0.5254 |
| 0.5352 | 62.0 | 151466 | 0.5264 |
| 0.5243 | 63.0 | 153909 | 0.5214 |
| 0.5134 | 64.0 | 156352 | 0.5210 |
| 0.5305 | 65.0 | 158795 | 0.5238 |
| 0.5507 | 66.0 | 161238 | 0.5210 |
| 0.5179 | 67.0 | 163681 | 0.5217 |
| 0.5162 | 68.0 | 166124 | 0.5166 |
| 0.5192 | 69.0 | 168567 | 0.5201 |
| 0.5231 | 70.0 | 171010 | 0.5175 |
| 0.5095 | 71.0 | 173453 | 0.5138 |
| 0.5205 | 72.0 | 175896 | 0.5135 |
| 0.5299 | 73.0 | 178339 | 0.5147 |
| 0.4947 | 74.0 | 180782 | 0.5112 |
| 0.5133 | 75.0 | 183225 | 0.5115 |
| 0.4886 | 76.0 | 185668 | 0.5090 |
| 0.5288 | 77.0 | 188111 | 0.5105 |
| 0.514 | 78.0 | 190554 | 0.5072 |
| 0.4803 | 79.0 | 192997 | 0.5053 |
| 0.4882 | 80.0 | 195440 | 0.5075 |
| 0.5037 | 81.0 | 197883 | 0.5063 |
| 0.5314 | 82.0 | 200326 | 0.5027 |
| 0.5181 | 83.0 | 202769 | 0.5013 |
| 0.5191 | 84.0 | 205212 | 0.5009 |
| 0.503 | 85.0 | 207655 | 0.4980 |
| 0.4894 | 86.0 | 210098 | 0.4993 |
| 0.4801 | 87.0 | 212541 | 0.4964 |
| 0.5019 | 88.0 | 214984 | 0.4956 |
| 0.5036 | 89.0 | 217427 | 0.4927 |
| 0.4844 | 90.0 | 219870 | 0.4932 |
| 0.4656 | 91.0 | 222313 | 0.4890 |
| 0.4839 | 92.0 | 224756 | 0.4881 |
| 0.4955 | 93.0 | 227199 | 0.4880 |
| 0.4792 | 94.0 | 229642 | 0.4877 |
| 0.4655 | 95.0 | 232085 | 0.4833 |
| 0.4811 | 96.0 | 234528 | 0.4835 |
| 0.5118 | 97.0 | 236971 | 0.4842 |
| 0.479 | 98.0 | 239414 | 0.4830 |
| 0.4693 | 99.0 | 241857 | 0.4827 |
| 0.46 | 100.0 | 244300 | 0.4785 |
| 0.479 | 101.0 | 246743 | 0.4792 |
| 0.4702 | 102.0 | 249186 | 0.4793 |
| 0.4683 | 103.0 | 251629 | 0.4757 |
| 0.4682 | 104.0 | 254072 | 0.4750 |
| 0.4749 | 105.0 | 256515 | 0.4747 |
| 0.4915 | 106.0 | 258958 | 0.4719 |
| 0.4832 | 107.0 | 261401 | 0.4729 |
| 0.4371 | 108.0 | 263844 | 0.4720 |
| 0.4779 | 109.0 | 266287 | 0.4710 |
| 0.4796 | 110.0 | 268730 | 0.4693 |
| 0.463 | 111.0 | 271173 | 0.4696 |
| 0.4722 | 112.0 | 273616 | 0.4679 |
| 0.4689 | 113.0 | 276059 | 0.4693 |
| 0.4644 | 114.0 | 278502 | 0.4665 |
| 0.4688 | 115.0 | 280945 | 0.4674 |
| 0.4619 | 116.0 | 283388 | 0.4644 |
| 0.4533 | 117.0 | 285831 | 0.4663 |
| 0.4604 | 118.0 | 288274 | 0.4634 |
| 0.4722 | 119.0 | 290717 | 0.4637 |
| 0.4622 | 120.0 | 293160 | 0.4634 |
| 0.4575 | 121.0 | 295603 | 0.4628 |
| 0.4824 | 122.0 | 298046 | 0.4631 |
| 0.4757 | 123.0 | 300489 | 0.4620 |
| 0.4457 | 124.0 | 302932 | 0.4620 |
| 0.4471 | 125.0 | 305375 | 0.4599 |
| 0.444 | 126.0 | 307818 | 0.4575 |
| 0.4521 | 127.0 | 310261 | 0.4599 |
| 0.4441 | 128.0 | 312704 | 0.4588 |
| 0.4432 | 129.0 | 315147 | 0.4596 |
| 0.4518 | 130.0 | 317590 | 0.4550 |
| 0.4457 | 131.0 | 320033 | 0.4578 |
| 0.4529 | 132.0 | 322476 | 0.4543 |
| 0.4871 | 133.0 | 324919 | 0.4560 |
| 0.4482 | 134.0 | 327362 | 0.4546 |
| 0.4648 | 135.0 | 329805 | 0.4574 |
| 0.4372 | 136.0 | 332248 | 0.4546 |
| 0.4353 | 137.0 | 334691 | 0.4531 |
| 0.4446 | 138.0 | 337134 | 0.4539 |
| 0.4666 | 139.0 | 339577 | 0.4518 |
| 0.4734 | 140.0 | 342020 | 0.4528 |
| 0.4601 | 141.0 | 344463 | 0.4540 |
| 0.4415 | 142.0 | 346906 | 0.4528 |
| 0.459 | 143.0 | 349349 | 0.4505 |
| 0.454 | 144.0 | 351792 | 0.4514 |
| 0.4606 | 145.0 | 354235 | 0.4511 |
| 0.4315 | 146.0 | 356678 | 0.4514 |
| 0.4583 | 147.0 | 359121 | 0.4520 |
| 0.452 | 148.0 | 361564 | 0.4495 |
| 0.4449 | 149.0 | 364007 | 0.4508 |
| 0.4272 | 150.0 | 366450 | 0.4489 |
| 0.439 | 151.0 | 368893 | 0.4504 |
| 0.4586 | 152.0 | 371336 | 0.4503 |
| 0.4559 | 153.0 | 373779 | 0.4500 |
| 0.4527 | 154.0 | 376222 | 0.4492 |
| 0.4511 | 155.0 | 378665 | 0.4491 |
| 0.4405 | 156.0 | 381108 | 0.4488 |
| 0.4509 | 157.0 | 383551 | 0.4482 |
| 0.4713 | 158.0 | 385994 | 0.4480 |
| 0.4578 | 159.0 | 388437 | 0.4465 |
| 0.4154 | 160.0 | 390880 | 0.4464 |
| 0.4399 | 161.0 | 393323 | 0.4488 |
| 0.4547 | 162.0 | 395766 | 0.4476 |
| 0.4426 | 163.0 | 398209 | 0.4456 |
| 0.4517 | 164.0 | 400652 | 0.4484 |
| 0.4376 | 165.0 | 403095 | 0.4455 |
| 0.4463 | 166.0 | 405538 | 0.4463 |
| 0.4289 | 167.0 | 407981 | 0.4466 |
| 0.4291 | 168.0 | 410424 | 0.4469 |
| 0.4623 | 169.0 | 412867 | 0.4455 |
| 0.4673 | 170.0 | 415310 | 0.4455 |
| 0.4609 | 171.0 | 417753 | 0.4456 |
| 0.4478 | 172.0 | 420196 | 0.4468 |
| 0.4521 | 173.0 | 422639 | 0.4437 |
| 0.4378 | 174.0 | 425082 | 0.4460 |
| 0.4361 | 175.0 | 427525 | 0.4446 |
| 0.4321 | 176.0 | 429968 | 0.4451 |
| 0.4369 | 177.0 | 432411 | 0.4451 |
| 0.4381 | 178.0 | 434854 | 0.4443 |
| 0.4408 | 179.0 | 437297 | 0.4449 |
| 0.4414 | 180.0 | 439740 | 0.4448 |
| 0.4333 | 181.0 | 442183 | 0.4438 |
| 0.4468 | 182.0 | 444626 | 0.4452 |
| 0.4394 | 183.0 | 447069 | 0.4440 |
| 0.441 | 184.0 | 449512 | 0.4434 |
| 0.4546 | 185.0 | 451955 | 0.4462 |
| 0.4455 | 186.0 | 454398 | 0.4458 |
| 0.4431 | 187.0 | 456841 | 0.4426 |
| 0.4489 | 188.0 | 459284 | 0.4433 |
| 0.4485 | 189.0 | 461727 | 0.4435 |
| 0.4449 | 190.0 | 464170 | 0.4433 |
| 0.4482 | 191.0 | 466613 | 0.4449 |
| 0.4395 | 192.0 | 469056 | 0.4433 |
| 0.4557 | 193.0 | 471499 | 0.4436 |
| 0.4208 | 194.0 | 473942 | 0.4450 |
| 0.4274 | 195.0 | 476385 | 0.4429 |
| 0.4423 | 196.0 | 478828 | 0.4434 |
| 0.4331 | 197.0 | 481271 | 0.4453 |
| 0.43 | 198.0 | 483714 | 0.4448 |
| 0.4308 | 199.0 | 486157 | 0.4460 |
| 0.4373 | 200.0 | 488600 | 0.4430 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2