segformer-b0-finetuned-segments-graffiti
This model is a fine-tuned version of nvidia/mit-b0 on the Adriatogi/graffiti dataset. It achieves the following results on the evaluation set:
- Loss: 0.3250
- Mean Iou: 0.8048
- Mean Accuracy: 0.8943
- Overall Accuracy: 0.8929
- Accuracy Not Graf: 0.8830
- Accuracy Graf: 0.9056
- Iou Not Graf: 0.8227
- Iou Graf: 0.7870
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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Not Graf | Accuracy Graf | Iou Not Graf | Iou Graf |
---|---|---|---|---|---|---|---|---|---|---|
0.5235 | 0.21 | 20 | 0.6135 | 0.6499 | 0.8016 | 0.7879 | 0.6926 | 0.9105 | 0.6476 | 0.6523 |
0.5744 | 0.42 | 40 | 0.4091 | 0.7237 | 0.8496 | 0.8398 | 0.7714 | 0.9279 | 0.7305 | 0.7169 |
0.3705 | 0.62 | 60 | 0.3959 | 0.7389 | 0.8592 | 0.8500 | 0.7864 | 0.9320 | 0.7469 | 0.7309 |
0.1897 | 0.83 | 80 | 0.3006 | 0.7748 | 0.8666 | 0.8774 | 0.9525 | 0.7807 | 0.8139 | 0.7357 |
0.1662 | 1.04 | 100 | 0.2900 | 0.7817 | 0.8723 | 0.8809 | 0.9407 | 0.8040 | 0.8164 | 0.7469 |
0.4537 | 1.25 | 120 | 0.2751 | 0.7956 | 0.8830 | 0.8886 | 0.9276 | 0.8384 | 0.8242 | 0.7669 |
0.1249 | 1.46 | 140 | 0.2719 | 0.7944 | 0.8841 | 0.8873 | 0.9094 | 0.8588 | 0.8196 | 0.7691 |
0.4985 | 1.67 | 160 | 0.3441 | 0.7463 | 0.8630 | 0.8550 | 0.7995 | 0.9264 | 0.7563 | 0.7363 |
0.4279 | 1.88 | 180 | 0.2911 | 0.7819 | 0.8764 | 0.8796 | 0.9016 | 0.8512 | 0.8082 | 0.7555 |
0.1776 | 2.08 | 200 | 0.2808 | 0.7928 | 0.8831 | 0.8864 | 0.9093 | 0.8569 | 0.8184 | 0.7673 |
0.209 | 2.29 | 220 | 0.2815 | 0.7857 | 0.8752 | 0.8832 | 0.9393 | 0.8111 | 0.8191 | 0.7522 |
0.152 | 2.5 | 240 | 0.2833 | 0.7921 | 0.8846 | 0.8854 | 0.8916 | 0.8775 | 0.8142 | 0.7700 |
0.5696 | 2.71 | 260 | 0.2698 | 0.8035 | 0.8921 | 0.8923 | 0.8941 | 0.8901 | 0.8238 | 0.7832 |
0.1003 | 2.92 | 280 | 0.3147 | 0.7739 | 0.8796 | 0.8729 | 0.8263 | 0.9329 | 0.7854 | 0.7624 |
0.1349 | 3.12 | 300 | 0.2961 | 0.7980 | 0.8906 | 0.8886 | 0.8747 | 0.9064 | 0.8154 | 0.7805 |
0.2552 | 3.33 | 320 | 0.2701 | 0.8001 | 0.8914 | 0.8900 | 0.8800 | 0.9028 | 0.8183 | 0.7820 |
0.1138 | 3.54 | 340 | 0.2808 | 0.7890 | 0.8854 | 0.8830 | 0.8664 | 0.9044 | 0.8065 | 0.7716 |
0.1602 | 3.75 | 360 | 0.2815 | 0.7956 | 0.8875 | 0.8875 | 0.8874 | 0.8875 | 0.8161 | 0.7751 |
0.0823 | 3.96 | 380 | 0.3195 | 0.7753 | 0.8799 | 0.8739 | 0.8325 | 0.9272 | 0.7879 | 0.7627 |
0.331 | 4.17 | 400 | 0.3339 | 0.7782 | 0.8821 | 0.8757 | 0.8312 | 0.9330 | 0.7901 | 0.7664 |
0.205 | 4.38 | 420 | 0.3083 | 0.7923 | 0.8885 | 0.8849 | 0.8595 | 0.9175 | 0.8077 | 0.7769 |
0.1659 | 4.58 | 440 | 0.3035 | 0.7887 | 0.8862 | 0.8826 | 0.8569 | 0.9156 | 0.8042 | 0.7731 |
0.1186 | 4.79 | 460 | 0.2856 | 0.8004 | 0.8839 | 0.8923 | 0.9500 | 0.8179 | 0.8323 | 0.7684 |
0.2964 | 5.0 | 480 | 0.3583 | 0.7592 | 0.8723 | 0.8633 | 0.8004 | 0.9442 | 0.7672 | 0.7512 |
0.0742 | 5.21 | 500 | 0.3269 | 0.7804 | 0.8820 | 0.8772 | 0.8444 | 0.9196 | 0.7947 | 0.7660 |
0.1355 | 5.42 | 520 | 0.3504 | 0.7784 | 0.8819 | 0.8759 | 0.8338 | 0.9301 | 0.7908 | 0.7661 |
0.0757 | 5.62 | 540 | 0.2771 | 0.8062 | 0.8927 | 0.8942 | 0.9050 | 0.8804 | 0.8280 | 0.7844 |
0.2015 | 5.83 | 560 | 0.3324 | 0.7851 | 0.8850 | 0.8802 | 0.8469 | 0.9232 | 0.7992 | 0.7711 |
0.1187 | 6.04 | 580 | 0.2853 | 0.8077 | 0.8943 | 0.8949 | 0.8995 | 0.8891 | 0.8282 | 0.7872 |
0.1243 | 6.25 | 600 | 0.3166 | 0.7968 | 0.8915 | 0.8875 | 0.8599 | 0.9232 | 0.8115 | 0.7820 |
0.0484 | 6.46 | 620 | 0.2876 | 0.8134 | 0.8968 | 0.8986 | 0.9110 | 0.8826 | 0.8349 | 0.7919 |
0.0772 | 6.67 | 640 | 0.2985 | 0.8085 | 0.8964 | 0.8951 | 0.8863 | 0.9064 | 0.8263 | 0.7907 |
0.2296 | 6.88 | 660 | 0.3134 | 0.8080 | 0.8951 | 0.8950 | 0.8940 | 0.8962 | 0.8274 | 0.7886 |
0.0544 | 7.08 | 680 | 0.3300 | 0.8014 | 0.8925 | 0.8907 | 0.8780 | 0.9070 | 0.8189 | 0.7839 |
0.0942 | 7.29 | 700 | 0.3133 | 0.8070 | 0.8936 | 0.8946 | 0.9013 | 0.8860 | 0.8280 | 0.7860 |
0.2432 | 7.5 | 720 | 0.3376 | 0.8014 | 0.8938 | 0.8905 | 0.8675 | 0.9201 | 0.8168 | 0.7860 |
0.0637 | 7.71 | 740 | 0.3021 | 0.8108 | 0.8968 | 0.8967 | 0.8965 | 0.8970 | 0.8301 | 0.7915 |
0.0946 | 7.92 | 760 | 0.3242 | 0.8048 | 0.8943 | 0.8929 | 0.8831 | 0.9054 | 0.8227 | 0.7870 |
0.1291 | 8.12 | 780 | 0.3315 | 0.8011 | 0.8934 | 0.8903 | 0.8689 | 0.9179 | 0.8169 | 0.7853 |
0.1077 | 8.33 | 800 | 0.3095 | 0.8117 | 0.8944 | 0.8979 | 0.9221 | 0.8667 | 0.8356 | 0.7877 |
0.177 | 8.54 | 820 | 0.3174 | 0.8117 | 0.8951 | 0.8977 | 0.9162 | 0.8740 | 0.8345 | 0.7888 |
0.057 | 8.75 | 840 | 0.3106 | 0.8111 | 0.8973 | 0.8968 | 0.8930 | 0.9016 | 0.8297 | 0.7925 |
0.2007 | 8.96 | 860 | 0.3645 | 0.7953 | 0.8909 | 0.8866 | 0.8571 | 0.9247 | 0.8097 | 0.7809 |
0.1281 | 9.17 | 880 | 0.3561 | 0.8008 | 0.8932 | 0.8902 | 0.8688 | 0.9176 | 0.8166 | 0.7850 |
0.0639 | 9.38 | 900 | 0.3120 | 0.8109 | 0.8969 | 0.8968 | 0.8962 | 0.8975 | 0.8301 | 0.7917 |
0.0766 | 9.58 | 920 | 0.3306 | 0.8057 | 0.8947 | 0.8934 | 0.8843 | 0.9051 | 0.8236 | 0.7877 |
0.1766 | 9.79 | 940 | 0.3321 | 0.8042 | 0.8941 | 0.8925 | 0.8813 | 0.9068 | 0.8219 | 0.7866 |
0.0842 | 10.0 | 960 | 0.3250 | 0.8048 | 0.8943 | 0.8929 | 0.8830 | 0.9056 | 0.8227 | 0.7870 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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