segformer-b0-finetuned-arabidopsis-roots-multi
This model is a fine-tuned version of nvidia/mit-b0 on the jacquelinegrimm/arabidopsis-roots-multi dataset. It achieves the following results on the evaluation set:
- Loss: 0.0675
- Mean Iou: 0.5272
- Mean Accuracy: 0.7048
- Overall Accuracy: 0.6874
- Accuracy Background: nan
- Accuracy Main root: 0.6670
- Accuracy Lateral root: 0.6181
- Accuracy Shoot: 0.7576
- Accuracy Botrytis: 0.7765
- Iou Background: 0.0
- Iou Main root: 0.6256
- Iou Lateral root: 0.5603
- Iou Shoot: 0.6767
- Iou Botrytis: 0.7734
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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Main root | Accuracy Lateral root | Accuracy Shoot | Accuracy Botrytis | Iou Background | Iou Main root | Iou Lateral root | Iou Shoot | Iou Botrytis |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1.1832 | 1.0 | 20 | 1.2347 | 0.3383 | 0.4952 | 0.4799 | nan | 0.1614 | 0.7764 | 0.1747 | 0.8683 | 0.0 | 0.1377 | 0.5355 | 0.1700 | 0.8484 |
0.901 | 2.0 | 40 | 0.7864 | 0.3051 | 0.4105 | 0.3438 | nan | 0.0062 | 0.3933 | 0.3166 | 0.9258 | 0.0 | 0.0061 | 0.3238 | 0.2853 | 0.9101 |
0.7161 | 3.0 | 60 | 0.6386 | 0.3059 | 0.4133 | 0.3430 | nan | 0.0271 | 0.3389 | 0.3349 | 0.9523 | 0.0 | 0.0270 | 0.2919 | 0.2847 | 0.9261 |
0.6358 | 4.0 | 80 | 0.6021 | 0.3155 | 0.4225 | 0.3502 | nan | 0.0390 | 0.3338 | 0.3713 | 0.9458 | 0.0 | 0.0387 | 0.2878 | 0.3241 | 0.9271 |
0.5111 | 5.0 | 100 | 0.4770 | 0.2871 | 0.3779 | 0.3175 | nan | 0.0567 | 0.2880 | 0.2798 | 0.8871 | 0.0 | 0.0562 | 0.2493 | 0.2571 | 0.8728 |
0.4842 | 6.0 | 120 | 0.4069 | 0.2422 | 0.3232 | 0.2792 | nan | 0.1142 | 0.2559 | 0.3734 | 0.5495 | 0.0 | 0.1112 | 0.2290 | 0.3240 | 0.5470 |
0.3631 | 7.0 | 140 | 0.3527 | 0.2692 | 0.3581 | 0.3537 | nan | 0.3382 | 0.3411 | 0.3353 | 0.4179 | 0.0 | 0.3168 | 0.3021 | 0.3093 | 0.4179 |
0.3316 | 8.0 | 160 | 0.2986 | 0.4239 | 0.5731 | 0.5496 | nan | 0.4712 | 0.5003 | 0.5306 | 0.7904 | 0.0 | 0.4372 | 0.4333 | 0.4719 | 0.7773 |
0.3074 | 9.0 | 180 | 0.2755 | 0.3755 | 0.5061 | 0.4932 | nan | 0.4440 | 0.4876 | 0.5202 | 0.5727 | 0.0 | 0.4129 | 0.4281 | 0.4679 | 0.5687 |
0.3216 | 10.0 | 200 | 0.2317 | 0.4262 | 0.5762 | 0.5625 | nan | 0.5676 | 0.4656 | 0.5853 | 0.6864 | 0.0 | 0.5153 | 0.4206 | 0.5142 | 0.6811 |
0.2426 | 11.0 | 220 | 0.2022 | 0.4659 | 0.6319 | 0.5981 | nan | 0.5068 | 0.5241 | 0.6682 | 0.8285 | 0.0 | 0.4766 | 0.4531 | 0.5856 | 0.8139 |
0.2063 | 12.0 | 240 | 0.1752 | 0.4274 | 0.5759 | 0.5627 | nan | 0.5756 | 0.4687 | 0.6275 | 0.6319 | 0.0 | 0.5259 | 0.4287 | 0.5547 | 0.6277 |
0.1811 | 13.0 | 260 | 0.1542 | 0.4608 | 0.6216 | 0.6022 | nan | 0.5509 | 0.5639 | 0.6604 | 0.7113 | 0.0 | 0.5166 | 0.4893 | 0.5899 | 0.7083 |
0.1738 | 14.0 | 280 | 0.1396 | 0.4393 | 0.5926 | 0.5781 | nan | 0.6143 | 0.4492 | 0.6755 | 0.6314 | 0.0 | 0.5544 | 0.4218 | 0.5914 | 0.6291 |
0.2094 | 15.0 | 300 | 0.1193 | 0.4422 | 0.5906 | 0.5683 | nan | 0.5321 | 0.4922 | 0.6475 | 0.6907 | 0.0 | 0.4953 | 0.4505 | 0.5760 | 0.6891 |
0.129 | 16.0 | 320 | 0.1200 | 0.4816 | 0.6520 | 0.6415 | nan | 0.6646 | 0.5644 | 0.7487 | 0.6303 | 0.0 | 0.6072 | 0.5158 | 0.6552 | 0.6298 |
0.1326 | 17.0 | 340 | 0.1109 | 0.4622 | 0.6200 | 0.6005 | nan | 0.5568 | 0.5628 | 0.7030 | 0.6576 | 0.0 | 0.5257 | 0.5029 | 0.6270 | 0.6555 |
0.0935 | 18.0 | 360 | 0.1043 | 0.5018 | 0.6743 | 0.6542 | nan | 0.5992 | 0.6207 | 0.7253 | 0.7519 | 0.0 | 0.5665 | 0.5412 | 0.6523 | 0.7490 |
0.0862 | 19.0 | 380 | 0.1044 | 0.4790 | 0.6435 | 0.6272 | nan | 0.5848 | 0.6080 | 0.7212 | 0.6600 | 0.0 | 0.5538 | 0.5333 | 0.6492 | 0.6586 |
0.122 | 20.0 | 400 | 0.0940 | 0.5244 | 0.7048 | 0.6846 | nan | 0.6428 | 0.6277 | 0.7498 | 0.7987 | 0.0 | 0.6000 | 0.5576 | 0.6708 | 0.7939 |
0.0886 | 21.0 | 420 | 0.0945 | 0.5390 | 0.7263 | 0.7016 | nan | 0.6253 | 0.6676 | 0.7686 | 0.8439 | 0.0 | 0.5912 | 0.5754 | 0.6902 | 0.8382 |
0.0742 | 22.0 | 440 | 0.0933 | 0.4919 | 0.6620 | 0.6407 | nan | 0.6001 | 0.5945 | 0.7732 | 0.6803 | 0.0 | 0.5677 | 0.5301 | 0.6829 | 0.6788 |
0.0727 | 23.0 | 460 | 0.0886 | 0.4711 | 0.6279 | 0.6139 | nan | 0.6072 | 0.5489 | 0.6892 | 0.6664 | 0.0 | 0.5695 | 0.5005 | 0.6199 | 0.6656 |
0.0708 | 24.0 | 480 | 0.0854 | 0.4966 | 0.6684 | 0.6574 | nan | 0.6409 | 0.6351 | 0.7520 | 0.6454 | 0.0 | 0.6040 | 0.5612 | 0.6737 | 0.6443 |
0.0614 | 25.0 | 500 | 0.0857 | 0.4882 | 0.6563 | 0.6405 | nan | 0.6005 | 0.6244 | 0.7464 | 0.6541 | 0.0 | 0.5691 | 0.5524 | 0.6658 | 0.6535 |
0.151 | 26.0 | 520 | 0.0834 | 0.5106 | 0.6852 | 0.6667 | nan | 0.6271 | 0.6253 | 0.7566 | 0.7320 | 0.0 | 0.5918 | 0.5566 | 0.6762 | 0.7284 |
0.0658 | 27.0 | 540 | 0.0823 | 0.5055 | 0.6768 | 0.6636 | nan | 0.6741 | 0.5807 | 0.7498 | 0.7027 | 0.0 | 0.6250 | 0.5332 | 0.6673 | 0.7017 |
0.0643 | 28.0 | 560 | 0.0797 | 0.4959 | 0.6636 | 0.6481 | nan | 0.6315 | 0.5930 | 0.7399 | 0.6901 | 0.0 | 0.5921 | 0.5366 | 0.6619 | 0.6889 |
0.059 | 29.0 | 580 | 0.0782 | 0.5115 | 0.6845 | 0.6682 | nan | 0.6490 | 0.6092 | 0.7531 | 0.7265 | 0.0 | 0.6100 | 0.5472 | 0.6769 | 0.7234 |
0.1726 | 30.0 | 600 | 0.0786 | 0.5235 | 0.7039 | 0.6846 | nan | 0.6190 | 0.6734 | 0.7552 | 0.7679 | 0.0 | 0.5905 | 0.5825 | 0.6807 | 0.7639 |
0.0546 | 31.0 | 620 | 0.0745 | 0.5116 | 0.6843 | 0.6654 | nan | 0.6337 | 0.6072 | 0.7468 | 0.7494 | 0.0 | 0.5956 | 0.5467 | 0.6701 | 0.7457 |
0.1096 | 32.0 | 640 | 0.0746 | 0.5093 | 0.6814 | 0.6590 | nan | 0.5992 | 0.6202 | 0.7427 | 0.7634 | 0.0 | 0.5666 | 0.5550 | 0.6656 | 0.7595 |
0.0552 | 33.0 | 660 | 0.0750 | 0.5358 | 0.7160 | 0.6944 | nan | 0.6520 | 0.6285 | 0.7572 | 0.8262 | 0.0 | 0.6147 | 0.5639 | 0.6783 | 0.8223 |
0.0557 | 34.0 | 680 | 0.0731 | 0.5123 | 0.6878 | 0.6709 | nan | 0.6271 | 0.6496 | 0.7669 | 0.7076 | 0.0 | 0.5955 | 0.5745 | 0.6853 | 0.7060 |
0.0516 | 35.0 | 700 | 0.0733 | 0.5307 | 0.7108 | 0.6929 | nan | 0.6696 | 0.6260 | 0.7665 | 0.7813 | 0.0 | 0.6276 | 0.5654 | 0.6825 | 0.7781 |
0.105 | 36.0 | 720 | 0.0717 | 0.5242 | 0.7020 | 0.6823 | nan | 0.6365 | 0.6397 | 0.7633 | 0.7684 | 0.0 | 0.6029 | 0.5682 | 0.6841 | 0.7660 |
0.1305 | 37.0 | 740 | 0.0713 | 0.5232 | 0.7002 | 0.6845 | nan | 0.6739 | 0.6137 | 0.7624 | 0.7510 | 0.0 | 0.6289 | 0.5585 | 0.6794 | 0.7490 |
0.0479 | 38.0 | 760 | 0.0707 | 0.5196 | 0.6944 | 0.6763 | nan | 0.6517 | 0.6116 | 0.7556 | 0.7588 | 0.0 | 0.6125 | 0.5532 | 0.6754 | 0.7567 |
0.0552 | 39.0 | 780 | 0.0705 | 0.5198 | 0.6977 | 0.6837 | nan | 0.6561 | 0.6543 | 0.7664 | 0.7139 | 0.0 | 0.6209 | 0.5803 | 0.6852 | 0.7124 |
0.0997 | 40.0 | 800 | 0.0699 | 0.5219 | 0.6968 | 0.6789 | nan | 0.6586 | 0.6057 | 0.7478 | 0.7752 | 0.0 | 0.6180 | 0.5504 | 0.6680 | 0.7729 |
0.0774 | 41.0 | 820 | 0.0708 | 0.5171 | 0.6900 | 0.6727 | nan | 0.6592 | 0.5927 | 0.7395 | 0.7686 | 0.0 | 0.6163 | 0.5402 | 0.6635 | 0.7656 |
0.0899 | 42.0 | 840 | 0.0692 | 0.5324 | 0.7125 | 0.6927 | nan | 0.6683 | 0.6169 | 0.7765 | 0.7883 | 0.0 | 0.6265 | 0.5601 | 0.6902 | 0.7851 |
0.0492 | 43.0 | 860 | 0.0682 | 0.5390 | 0.7216 | 0.7043 | nan | 0.6740 | 0.6497 | 0.7676 | 0.7950 | 0.0 | 0.6348 | 0.5793 | 0.6891 | 0.7918 |
0.0712 | 44.0 | 880 | 0.0690 | 0.5121 | 0.6844 | 0.6692 | nan | 0.6570 | 0.6071 | 0.7533 | 0.7204 | 0.0 | 0.6153 | 0.5524 | 0.6743 | 0.7186 |
0.1034 | 45.0 | 900 | 0.0685 | 0.5503 | 0.7379 | 0.7191 | nan | 0.6822 | 0.6645 | 0.7832 | 0.8215 | 0.0 | 0.6439 | 0.5905 | 0.6994 | 0.8175 |
0.0478 | 46.0 | 920 | 0.0681 | 0.5365 | 0.7179 | 0.6998 | nan | 0.6726 | 0.6369 | 0.7719 | 0.7902 | 0.0 | 0.6326 | 0.5728 | 0.6901 | 0.7869 |
0.0452 | 47.0 | 940 | 0.0682 | 0.5341 | 0.7157 | 0.6993 | nan | 0.6723 | 0.6495 | 0.7765 | 0.7647 | 0.0 | 0.6346 | 0.5803 | 0.6935 | 0.7621 |
0.0542 | 48.0 | 960 | 0.0675 | 0.5382 | 0.7206 | 0.7021 | nan | 0.6695 | 0.6444 | 0.7726 | 0.7961 | 0.0 | 0.6313 | 0.5772 | 0.6899 | 0.7928 |
0.0738 | 49.0 | 980 | 0.0680 | 0.5360 | 0.7168 | 0.6977 | nan | 0.6714 | 0.6254 | 0.7666 | 0.8040 | 0.0 | 0.6302 | 0.5658 | 0.6838 | 0.8004 |
0.1169 | 50.0 | 1000 | 0.0675 | 0.5272 | 0.7048 | 0.6874 | nan | 0.6670 | 0.6181 | 0.7576 | 0.7765 | 0.0 | 0.6256 | 0.5603 | 0.6767 | 0.7734 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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