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