convnextv2-tiny-1k-224-finetuned-pattern-rgb

This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5797
  • Accuracy: 0.875

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 120

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.6954 0.9912 28 1.4482 0.58
1.015 1.9823 56 0.9088 0.7175
0.7953 2.9735 84 0.7266 0.7625
0.627 4.0 113 0.5872 0.8
0.4684 4.9912 141 0.5534 0.8175
0.4301 5.9823 169 0.5053 0.8275
0.3716 6.9735 197 0.4885 0.83
0.3798 8.0 226 0.4639 0.8525
0.3123 8.9912 254 0.5282 0.825
0.3148 9.9823 282 0.4569 0.8475
0.2427 10.9735 310 0.4206 0.865
0.2198 12.0 339 0.4832 0.84
0.1995 12.9912 367 0.4468 0.865
0.1738 13.9823 395 0.5668 0.8425
0.1683 14.9735 423 0.4454 0.8725
0.1426 16.0 452 0.5118 0.8525
0.133 16.9912 480 0.4713 0.865
0.1148 17.9823 508 0.5226 0.855
0.1147 18.9735 536 0.5333 0.8425
0.1284 20.0 565 0.4399 0.8575
0.1035 20.9912 593 0.5194 0.8525
0.1054 21.9823 621 0.5140 0.845
0.1056 22.9735 649 0.5183 0.87
0.1224 24.0 678 0.5293 0.85
0.0956 24.9912 706 0.4985 0.87
0.0717 25.9823 734 0.5267 0.8625
0.0858 26.9735 762 0.5525 0.8575
0.097 28.0 791 0.5340 0.855
0.0914 28.9912 819 0.4830 0.87
0.0699 29.9823 847 0.4883 0.8725
0.0932 30.9735 875 0.6106 0.8575
0.0967 32.0 904 0.5614 0.855
0.101 32.9912 932 0.5947 0.8525
0.0734 33.9823 960 0.5388 0.87
0.0742 34.9735 988 0.5110 0.8725
0.0698 36.0 1017 0.5384 0.8525
0.0785 36.9912 1045 0.5407 0.8475
0.0718 37.9823 1073 0.5420 0.86
0.061 38.9735 1101 0.5747 0.8675
0.0695 40.0 1130 0.5829 0.8575
0.0611 40.9912 1158 0.6212 0.8525
0.0734 41.9823 1186 0.5035 0.875
0.0643 42.9735 1214 0.5345 0.8775
0.0625 44.0 1243 0.5208 0.8625
0.047 44.9912 1271 0.5635 0.8675
0.0612 45.9823 1299 0.4721 0.8775
0.0582 46.9735 1327 0.5683 0.855
0.0516 48.0 1356 0.5883 0.8625
0.0427 48.9912 1384 0.5757 0.8575
0.0601 49.9823 1412 0.5368 0.8625
0.0645 50.9735 1440 0.5608 0.84
0.054 52.0 1469 0.5380 0.87
0.0647 52.9912 1497 0.5490 0.8625
0.0539 53.9823 1525 0.5686 0.8625
0.0485 54.9735 1553 0.5474 0.8725
0.0649 56.0 1582 0.5938 0.86
0.0486 56.9912 1610 0.5642 0.86
0.0385 57.9823 1638 0.5390 0.8675
0.0404 58.9735 1666 0.5735 0.8775
0.0543 60.0 1695 0.5117 0.875
0.0506 60.9912 1723 0.5422 0.8725
0.0398 61.9823 1751 0.5473 0.87
0.0494 62.9735 1779 0.5333 0.8675
0.0472 64.0 1808 0.5650 0.8825
0.0504 64.9912 1836 0.5771 0.8575
0.044 65.9823 1864 0.5220 0.86
0.061 66.9735 1892 0.5622 0.8725
0.0459 68.0 1921 0.5864 0.8625
0.0294 68.9912 1949 0.6341 0.8625
0.0428 69.9823 1977 0.5696 0.8675
0.0317 70.9735 2005 0.6313 0.845
0.0453 72.0 2034 0.5955 0.875
0.0592 72.9912 2062 0.5844 0.8675
0.0408 73.9823 2090 0.5868 0.86
0.0358 74.9735 2118 0.6115 0.85
0.0412 76.0 2147 0.5940 0.865
0.0323 76.9912 2175 0.5752 0.8625
0.0378 77.9823 2203 0.5515 0.8725
0.0359 78.9735 2231 0.5910 0.8775
0.028 80.0 2260 0.6060 0.8725
0.032 80.9912 2288 0.6054 0.8775
0.032 81.9823 2316 0.6312 0.8725
0.0228 82.9735 2344 0.6153 0.87
0.0457 84.0 2373 0.6443 0.86
0.0248 84.9912 2401 0.5726 0.875
0.0405 85.9823 2429 0.6042 0.875
0.0203 86.9735 2457 0.6107 0.87
0.0557 88.0 2486 0.5890 0.88
0.0302 88.9912 2514 0.5778 0.8625
0.0268 89.9823 2542 0.6039 0.8625
0.0313 90.9735 2570 0.5608 0.885
0.0227 92.0 2599 0.6019 0.8625
0.0277 92.9912 2627 0.5949 0.8675
0.0378 93.9823 2655 0.5785 0.875
0.0381 94.9735 2683 0.5646 0.8825
0.0435 96.0 2712 0.5513 0.88
0.0264 96.9912 2740 0.5257 0.875
0.0362 97.9823 2768 0.5332 0.8825
0.0209 98.9735 2796 0.5777 0.855
0.0348 100.0 2825 0.5674 0.8675
0.02 100.9912 2853 0.5744 0.8625
0.0092 101.9823 2881 0.5852 0.8675
0.0343 102.9735 2909 0.5856 0.8675
0.0185 104.0 2938 0.5670 0.88
0.0198 104.9912 2966 0.5612 0.8775
0.016 105.9823 2994 0.5701 0.88
0.0369 106.9735 3022 0.5791 0.8825
0.0357 108.0 3051 0.5730 0.8725
0.0361 108.9912 3079 0.5627 0.8725
0.0438 109.9823 3107 0.5812 0.875
0.0243 110.9735 3135 0.5922 0.8725
0.0241 112.0 3164 0.5913 0.8775
0.0256 112.9912 3192 0.5862 0.8675
0.0247 113.9823 3220 0.5813 0.8675
0.028 114.9735 3248 0.5752 0.87
0.0177 116.0 3277 0.5742 0.87
0.0255 116.9912 3305 0.5795 0.87
0.0174 117.9823 3333 0.5803 0.875
0.0225 118.9381 3360 0.5797 0.875

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1
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Evaluation results