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vit-base-lcdoctypev1_session2

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metadata
license: apache-2.0
base_model: nsugianto/vit-base-lcdoctypev1
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-lcdoctypev1_session2
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: doctype_v1
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9752066115702479

vit-base-lcdoctypev1_session2

This model is a fine-tuned version of nsugianto/vit-base-lcdoctypev1 on the doctype_v1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1231
  • Accuracy: 0.9752

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: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.08 5 0.3652 0.9008
0.2494 0.17 10 0.4354 0.8926
0.2494 0.25 15 0.6027 0.8264
0.4714 0.33 20 0.6016 0.8182
0.4714 0.42 25 0.6495 0.8017
0.6257 0.5 30 0.6561 0.7769
0.6257 0.58 35 0.4359 0.8264
0.3534 0.67 40 0.4082 0.8760
0.3534 0.75 45 0.3042 0.9174
0.2059 0.83 50 0.3919 0.8843
0.2059 0.92 55 0.9259 0.7521
0.3693 1.0 60 0.5527 0.8430
0.3693 1.08 65 0.3412 0.8595
0.3436 1.17 70 0.3949 0.8595
0.3436 1.25 75 0.4402 0.8760
0.361 1.33 80 0.8712 0.7355
0.361 1.42 85 0.4137 0.8430
0.4531 1.5 90 0.2484 0.9256
0.4531 1.58 95 0.3072 0.9008
0.223 1.67 100 0.3936 0.9008
0.223 1.75 105 0.4798 0.8595
0.3396 1.83 110 0.3731 0.8760
0.3396 1.92 115 0.3138 0.9174
0.1516 2.0 120 0.3140 0.9008
0.1516 2.08 125 0.3938 0.8926
0.1709 2.17 130 0.3311 0.9174
0.1709 2.25 135 0.3652 0.9091
0.1193 2.33 140 0.4033 0.9008
0.1193 2.42 145 0.3220 0.8926
0.2134 2.5 150 0.4836 0.8595
0.2134 2.58 155 0.2969 0.9091
0.1314 2.67 160 0.3032 0.9174
0.1314 2.75 165 0.3466 0.8843
0.2197 2.83 170 0.2749 0.8843
0.2197 2.92 175 0.2071 0.9256
0.1748 3.0 180 0.2477 0.9256
0.1748 3.08 185 0.2279 0.9174
0.0949 3.17 190 0.2859 0.9091
0.0949 3.25 195 0.2731 0.9008
0.1901 3.33 200 0.2329 0.9339
0.1901 3.42 205 0.4313 0.9008
0.1897 3.5 210 0.3919 0.9008
0.1897 3.58 215 0.2083 0.9504
0.1213 3.67 220 0.2194 0.9421
0.1213 3.75 225 0.2504 0.9421
0.1225 3.83 230 0.2727 0.9339
0.1225 3.92 235 0.5615 0.8595
0.148 4.0 240 0.3485 0.9091
0.148 4.08 245 0.2324 0.9008
0.1498 4.17 250 0.2689 0.9256
0.1498 4.25 255 0.1947 0.9504
0.0504 4.33 260 0.2275 0.9339
0.0504 4.42 265 0.2790 0.9339
0.0465 4.5 270 0.2727 0.9256
0.0465 4.58 275 0.2949 0.9256
0.0744 4.67 280 0.2705 0.9421
0.0744 4.75 285 0.2787 0.9339
0.1504 4.83 290 0.2361 0.9421
0.1504 4.92 295 0.2886 0.9256
0.1192 5.0 300 0.2602 0.9256
0.1192 5.08 305 0.2183 0.9256
0.0749 5.17 310 0.2774 0.9174
0.0749 5.25 315 0.2491 0.9091
0.0719 5.33 320 0.1902 0.9504
0.0719 5.42 325 0.1915 0.9504
0.061 5.5 330 0.1713 0.9587
0.061 5.58 335 0.2117 0.9504
0.0381 5.67 340 0.2145 0.9587
0.0381 5.75 345 0.2108 0.9504
0.0494 5.83 350 0.2006 0.9504
0.0494 5.92 355 0.1935 0.9504
0.0341 6.0 360 0.1891 0.9669
0.0341 6.08 365 0.1675 0.9669
0.0186 6.17 370 0.1582 0.9669
0.0186 6.25 375 0.1470 0.9669
0.0444 6.33 380 0.1401 0.9752
0.0444 6.42 385 0.1611 0.9669
0.031 6.5 390 0.1923 0.9669
0.031 6.58 395 0.2467 0.9339
0.0415 6.67 400 0.1997 0.9587
0.0415 6.75 405 0.1741 0.9669
0.0396 6.83 410 0.1882 0.9669
0.0396 6.92 415 0.2266 0.9587
0.1419 7.0 420 0.2240 0.9587
0.1419 7.08 425 0.1832 0.9669
0.0401 7.17 430 0.1767 0.9587
0.0401 7.25 435 0.1653 0.9587
0.0435 7.33 440 0.1873 0.9421
0.0435 7.42 445 0.2051 0.9504
0.0368 7.5 450 0.2075 0.9504
0.0368 7.58 455 0.1932 0.9504
0.0144 7.67 460 0.1878 0.9504
0.0144 7.75 465 0.1855 0.9504
0.0245 7.83 470 0.1826 0.9587
0.0245 7.92 475 0.1652 0.9669
0.049 8.0 480 0.1540 0.9669
0.049 8.08 485 0.1566 0.9587
0.0205 8.17 490 0.1390 0.9587
0.0205 8.25 495 0.1231 0.9752
0.0582 8.33 500 0.1489 0.9669
0.0582 8.42 505 0.1636 0.9669
0.0233 8.5 510 0.1738 0.9587
0.0233 8.58 515 0.1830 0.9587
0.026 8.67 520 0.1868 0.9587
0.026 8.75 525 0.1848 0.9587
0.0193 8.83 530 0.1805 0.9669
0.0193 8.92 535 0.1774 0.9669
0.0174 9.0 540 0.1709 0.9669
0.0174 9.08 545 0.1626 0.9587
0.036 9.17 550 0.1482 0.9669
0.036 9.25 555 0.1522 0.9669
0.0248 9.33 560 0.1524 0.9669
0.0248 9.42 565 0.1530 0.9669
0.0097 9.5 570 0.1537 0.9669
0.0097 9.58 575 0.1546 0.9669
0.0154 9.67 580 0.1550 0.9669
0.0154 9.75 585 0.1554 0.9669
0.0208 9.83 590 0.1549 0.9669
0.0208 9.92 595 0.1541 0.9669
0.0167 10.0 600 0.1542 0.9669

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2