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sashes_model

Image Classification
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Safetensors
vit
Generated from Trainer
Eval Results
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metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: sashes_model
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.875968992248062

sashes_model

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3784
  • Accuracy: 0.8760

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: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 112

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.9697 8 2.2973 0.1434
2.2994 1.9394 16 2.2717 0.1957
2.2791 2.9091 24 2.2377 0.2287
2.2378 4.0 33 2.1866 0.3178
2.1604 4.9697 41 2.1096 0.3934
2.1604 5.9394 49 2.0257 0.4322
2.0801 6.9091 57 1.9312 0.4264
1.9587 8.0 66 1.7939 0.4942
1.821 8.9697 74 1.6869 0.5465
1.6903 9.9394 82 1.6025 0.5736
1.5687 10.9091 90 1.4849 0.6202
1.5687 12.0 99 1.4674 0.5407
1.4183 12.9697 107 1.3539 0.6163
1.3907 13.9394 115 1.2365 0.6938
1.3058 14.9091 123 1.2258 0.6938
1.2181 16.0 132 1.1759 0.6822
1.1537 16.9697 140 1.1413 0.7074
1.1537 17.9394 148 1.0586 0.7248
1.0819 18.9091 156 1.0059 0.7558
0.9905 20.0 165 0.9575 0.7578
1.0055 20.9697 173 0.9807 0.7442
0.9484 21.9394 181 0.9553 0.7539
0.9484 22.9091 189 0.8213 0.8004
0.8974 24.0 198 0.8305 0.8043
0.8545 24.9697 206 0.8273 0.7849
0.8724 25.9394 214 0.8177 0.7519
0.8642 26.9091 222 0.7692 0.7926
0.7609 28.0 231 0.7293 0.8062
0.7609 28.9697 239 0.7001 0.8198
0.7418 29.9394 247 0.7899 0.7636
0.7552 30.9091 255 0.6595 0.8101
0.7291 32.0 264 0.6971 0.7907
0.693 32.9697 272 0.7215 0.7946
0.6891 33.9394 280 0.6980 0.8004
0.6891 34.9091 288 0.6200 0.8372
0.6936 36.0 297 0.7245 0.7733
0.6698 36.9697 305 0.6724 0.7984
0.6502 37.9394 313 0.6701 0.8023
0.6988 38.9091 321 0.6049 0.8236
0.6709 40.0 330 0.6397 0.7965
0.6709 40.9697 338 0.5654 0.8391
0.652 41.9394 346 0.6371 0.8101
0.64 42.9091 354 0.6341 0.8062
0.6368 44.0 363 0.5662 0.8527
0.595 44.9697 371 0.5744 0.8411
0.595 45.9394 379 0.5465 0.8430
0.5823 46.9091 387 0.6254 0.7984
0.5514 48.0 396 0.5368 0.8333
0.5693 48.9697 404 0.5705 0.8043
0.5244 49.9394 412 0.5685 0.8314
0.5495 50.9091 420 0.5811 0.8120
0.5495 52.0 429 0.5037 0.8469
0.5501 52.9697 437 0.5423 0.8372
0.5405 53.9394 445 0.5487 0.8178
0.534 54.9091 453 0.5607 0.8217
0.5502 56.0 462 0.5141 0.8198
0.4772 56.9697 470 0.4813 0.8605
0.4772 57.9394 478 0.5007 0.8566
0.4823 58.9091 486 0.4847 0.8624
0.5107 60.0 495 0.5273 0.8333
0.5205 60.9697 503 0.4981 0.8430
0.5171 61.9394 511 0.4819 0.8430
0.5171 62.9091 519 0.4415 0.8682
0.5498 64.0 528 0.4578 0.8566
0.4732 64.9697 536 0.4614 0.8450
0.4623 65.9394 544 0.4923 0.8488
0.4406 66.9091 552 0.4556 0.8547
0.4889 68.0 561 0.4727 0.8488
0.4889 68.9697 569 0.4746 0.8469
0.4532 69.9394 577 0.4496 0.8585
0.3988 70.9091 585 0.4260 0.8702
0.4608 72.0 594 0.4464 0.8547
0.4429 72.9697 602 0.3946 0.8818
0.4502 73.9394 610 0.4566 0.8527
0.4502 74.9091 618 0.4472 0.8663
0.4381 76.0 627 0.4701 0.8372
0.4437 76.9697 635 0.4351 0.8488
0.4223 77.9394 643 0.4011 0.8779
0.4121 78.9091 651 0.4328 0.8547
0.4164 80.0 660 0.3908 0.8857
0.4164 80.9697 668 0.3774 0.8876
0.418 81.9394 676 0.4397 0.8643
0.3961 82.9091 684 0.4500 0.8585
0.4035 84.0 693 0.3968 0.8624
0.4269 84.9697 701 0.4457 0.8566
0.4269 85.9394 709 0.3987 0.8740
0.3694 86.9091 717 0.4074 0.8760
0.3642 88.0 726 0.3781 0.9012
0.3985 88.9697 734 0.3575 0.8934
0.4237 89.9394 742 0.4313 0.8508
0.4156 90.9091 750 0.3504 0.8934
0.4156 92.0 759 0.4116 0.8566
0.389 92.9697 767 0.3739 0.8779
0.3934 93.9394 775 0.3990 0.8779
0.4231 94.9091 783 0.4164 0.8624
0.3792 96.0 792 0.3808 0.8721
0.3928 96.9697 800 0.3534 0.8915
0.3928 97.9394 808 0.3643 0.8798
0.4003 98.9091 816 0.4150 0.8624
0.3929 100.0 825 0.3477 0.9050
0.3992 100.9697 833 0.4037 0.8682
0.387 101.9394 841 0.3453 0.9050
0.387 102.9091 849 0.4012 0.8682
0.3942 104.0 858 0.3843 0.8915
0.3794 104.9697 866 0.3478 0.8798
0.3794 105.9394 874 0.3111 0.9167
0.396 106.9091 882 0.3588 0.8818
0.3767 108.0 891 0.3602 0.8837
0.3767 108.6061 896 0.3784 0.8760

Framework versions

  • Transformers 4.45.0.dev0
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1