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dit-base-finetuned-rvlcdip-finetuned-data200

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metadata
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: dit-base-finetuned-rvlcdip-finetuned-data200
    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: 0.5698924731182796

dit-base-finetuned-rvlcdip-finetuned-data200

This model is a fine-tuned version of microsoft/dit-base-finetuned-rvlcdip on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 3.0080
  • Accuracy: 0.5699

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: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 200

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.1142 1.0 46 2.0131 0.3441
1.9953 2.0 92 1.9577 0.4086
1.9558 3.0 138 1.9231 0.4301
1.9251 4.0 184 1.8015 0.4946
1.6485 5.0 230 1.7045 0.5269
1.5973 6.0 276 1.5806 0.5054
1.4755 7.0 322 1.4849 0.5054
1.4537 8.0 368 1.4356 0.5161
1.416 9.0 414 1.4512 0.5269
1.3645 10.0 460 1.3857 0.5591
1.3017 11.0 506 1.3108 0.5484
1.2794 12.0 552 1.3027 0.5376
1.1553 13.0 598 1.2883 0.5484
1.1526 14.0 644 1.3554 0.5054
1.1116 15.0 690 1.3235 0.5914
1.1925 16.0 736 1.2401 0.5806
1.1297 17.0 782 1.3425 0.5914
0.9717 18.0 828 1.3538 0.5484
0.8404 19.0 874 1.2648 0.5699
0.7008 20.0 920 1.4971 0.5376
1.1454 21.0 966 1.4137 0.4839
0.6849 22.0 1012 1.2801 0.5591
0.8566 23.0 1058 1.2380 0.5699
0.8956 24.0 1104 1.2903 0.6129
0.8004 25.0 1150 1.4372 0.5591
0.818 26.0 1196 1.1640 0.6344
0.6387 27.0 1242 1.3120 0.6452
0.7282 28.0 1288 1.4678 0.5161
0.7426 29.0 1334 1.4815 0.5269
0.735 30.0 1380 1.2714 0.6129
0.6769 31.0 1426 1.2262 0.5699
0.5562 32.0 1472 1.3348 0.6344
0.6671 33.0 1518 1.4159 0.6129
0.3708 34.0 1564 1.6416 0.5484
0.3967 35.0 1610 1.3298 0.5699
0.4692 36.0 1656 1.3559 0.5699
0.632 37.0 1702 1.3349 0.5699
0.3719 38.0 1748 1.4697 0.5914
0.4238 39.0 1794 1.5207 0.6022
0.3608 40.0 1840 1.5557 0.5591
0.6252 41.0 1886 1.6247 0.5269
0.4183 42.0 1932 1.5885 0.5914
0.3922 43.0 1978 1.6593 0.5699
0.5715 44.0 2024 1.5270 0.5699
0.3656 45.0 2070 1.8899 0.5054
0.3656 46.0 2116 2.0936 0.4624
0.4003 47.0 2162 1.5610 0.5054
0.446 48.0 2208 1.7388 0.5376
0.5219 49.0 2254 1.4976 0.6129
0.3488 50.0 2300 1.5744 0.5914
0.323 51.0 2346 1.6312 0.6022
0.3713 52.0 2392 1.6975 0.5591
0.2981 53.0 2438 1.6229 0.5699
0.3422 54.0 2484 2.0909 0.4624
0.2538 55.0 2530 2.0966 0.5161
0.3868 56.0 2576 1.5614 0.6344
0.4662 57.0 2622 1.8929 0.5269
0.4277 58.0 2668 1.9573 0.5376
0.5301 59.0 2714 1.7999 0.5699
0.3867 60.0 2760 2.3481 0.4624
0.2334 61.0 2806 1.9924 0.5376
0.2921 62.0 2852 2.0454 0.5591
0.4386 63.0 2898 1.7798 0.5376
0.3299 64.0 2944 1.9370 0.5914
0.5982 65.0 2990 2.0527 0.5591
0.4433 66.0 3036 1.6222 0.6237
0.3717 67.0 3082 1.7977 0.5914
0.3642 68.0 3128 1.6988 0.5914
0.4541 69.0 3174 1.7567 0.6022
0.3464 70.0 3220 1.9029 0.5699
0.2764 71.0 3266 1.9611 0.6022
0.2138 72.0 3312 1.9333 0.5591
0.3928 73.0 3358 1.7701 0.5806
0.1811 74.0 3404 1.8330 0.5806
0.2076 75.0 3450 1.6676 0.6559
0.3326 76.0 3496 2.0036 0.6022
0.1343 77.0 3542 1.6937 0.6344
0.3031 78.0 3588 1.9223 0.6237
0.2743 79.0 3634 2.1681 0.5699
0.3392 80.0 3680 2.0505 0.6129
0.1346 81.0 3726 2.0190 0.5699
0.0652 82.0 3772 2.2910 0.5699
0.4219 83.0 3818 1.8858 0.5914
0.1386 84.0 3864 1.7976 0.6237
0.2155 85.0 3910 2.4278 0.5161
0.4901 86.0 3956 1.9239 0.6237
0.3141 87.0 4002 2.0954 0.6559
0.2328 88.0 4048 2.2602 0.5806
0.2768 89.0 4094 2.1083 0.5914
0.3476 90.0 4140 2.4922 0.5269
0.2029 91.0 4186 2.2094 0.5591
0.2421 92.0 4232 2.2407 0.5376
0.2034 93.0 4278 2.1488 0.5591
0.2461 94.0 4324 2.1332 0.5806
0.1462 95.0 4370 2.2702 0.5591
0.5213 96.0 4416 2.2134 0.5699
0.3634 97.0 4462 2.1066 0.5699
0.1698 98.0 4508 2.2736 0.6237
0.1685 99.0 4554 2.3919 0.5806
0.1971 100.0 4600 2.0664 0.6237
0.1496 101.0 4646 2.5661 0.5806
0.283 102.0 4692 2.0714 0.5699
0.185 103.0 4738 2.1369 0.6022
0.1489 104.0 4784 2.1653 0.6129
0.1231 105.0 4830 2.0890 0.6452
0.3224 106.0 4876 2.3771 0.5376
0.3452 107.0 4922 2.2537 0.6344
0.4404 108.0 4968 2.0253 0.6129
0.3408 109.0 5014 2.1653 0.5699
0.2406 110.0 5060 2.0196 0.6237
0.3051 111.0 5106 2.1980 0.6129
0.1515 112.0 5152 2.4104 0.5699
0.3836 113.0 5198 2.2342 0.6344
0.3572 114.0 5244 2.2321 0.6022
0.3006 115.0 5290 2.3555 0.5806
0.0965 116.0 5336 2.7237 0.4516
0.2023 117.0 5382 2.3798 0.6237
0.1272 118.0 5428 2.5357 0.5591
0.4318 119.0 5474 2.4913 0.5699
0.0414 120.0 5520 2.3760 0.6022
0.1785 121.0 5566 2.3920 0.6129
0.0142 122.0 5612 2.4256 0.6022
0.1262 123.0 5658 2.7212 0.5806
0.2219 124.0 5704 2.3683 0.5699
0.1629 125.0 5750 2.4280 0.5484
0.149 126.0 5796 3.0708 0.4839
0.2394 127.0 5842 2.2192 0.6022
0.2165 128.0 5888 2.4015 0.5806
0.0729 129.0 5934 2.2241 0.6022
0.2585 130.0 5980 2.9483 0.5054
0.1401 131.0 6026 2.3180 0.6129
0.4162 132.0 6072 3.0147 0.4946
0.1188 133.0 6118 2.3128 0.6237
0.0939 134.0 6164 2.5300 0.6022
0.1039 135.0 6210 2.5740 0.5699
0.3678 136.0 6256 2.5887 0.5914
0.3998 137.0 6302 2.5664 0.5376
0.1952 138.0 6348 2.1861 0.6774
0.2616 139.0 6394 2.7036 0.5806
0.2523 140.0 6440 2.5953 0.5806
0.2772 141.0 6486 2.4114 0.6129
0.2399 142.0 6532 2.3203 0.6237
0.3769 143.0 6578 2.7200 0.5591
0.0094 144.0 6624 2.7315 0.5591
0.1818 145.0 6670 2.5223 0.6129
0.3063 146.0 6716 2.3310 0.6237
0.222 147.0 6762 2.6180 0.5806
0.2505 148.0 6808 2.2976 0.6344
0.2705 149.0 6854 2.4091 0.5914
0.1624 150.0 6900 2.8030 0.5269
0.1322 151.0 6946 2.6379 0.5591
0.0876 152.0 6992 2.5781 0.5484
0.1332 153.0 7038 2.8476 0.5591
0.2727 154.0 7084 2.6779 0.5699
0.195 155.0 7130 3.0504 0.4839
0.152 156.0 7176 2.6103 0.5806
0.2811 157.0 7222 2.5947 0.6129
0.0742 158.0 7268 2.4666 0.6559
0.2052 159.0 7314 2.5116 0.5484
0.2598 160.0 7360 3.0400 0.5269
0.2846 161.0 7406 2.2042 0.6667
0.2653 162.0 7452 3.0598 0.5484
0.358 163.0 7498 2.7669 0.5806
0.0355 164.0 7544 2.4568 0.6237
0.1817 165.0 7590 2.9532 0.5806
0.0955 166.0 7636 2.4798 0.6237
0.1941 167.0 7682 2.7027 0.5699
0.1787 168.0 7728 2.4225 0.6237
0.0998 169.0 7774 2.5104 0.5914
0.0392 170.0 7820 2.6235 0.5806
0.2689 171.0 7866 2.9215 0.5806
0.0595 172.0 7912 2.8108 0.5699
0.148 173.0 7958 2.9213 0.5806
0.2159 174.0 8004 2.6172 0.6129
0.1221 175.0 8050 2.4386 0.6237
0.0691 176.0 8096 2.8642 0.5269
0.2014 177.0 8142 2.7364 0.6022
0.0379 178.0 8188 2.4859 0.6022
0.2202 179.0 8234 3.0665 0.5484
0.2078 180.0 8280 2.3521 0.6237
0.1051 181.0 8326 2.4827 0.6237
0.2257 182.0 8372 2.8155 0.5914
0.1339 183.0 8418 2.6274 0.6237
0.1414 184.0 8464 2.7645 0.5806
0.0993 185.0 8510 2.8886 0.5591
0.1769 186.0 8556 2.5164 0.6129
0.1575 187.0 8602 2.9346 0.5376
0.0251 188.0 8648 2.6099 0.5376
0.0536 189.0 8694 2.9630 0.5376
0.1748 190.0 8740 2.8360 0.5699
0.0151 191.0 8786 2.7525 0.6022
0.2198 192.0 8832 2.6656 0.5376
0.267 193.0 8878 3.0118 0.5591
0.1043 194.0 8924 3.0214 0.5699
0.0035 195.0 8970 2.7925 0.5806
0.0707 196.0 9016 2.7839 0.5806
0.0656 197.0 9062 3.0370 0.5376
0.1155 198.0 9108 2.6510 0.5914
0.1118 199.0 9154 2.7058 0.5699
0.3086 200.0 9200 3.0080 0.5699

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.0
  • Tokenizers 0.13.2