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
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