segformer-b0-finetuned-arabidopsis-roots-v02
This model is a fine-tuned version of nvidia/mit-b0 on the jacquelinegrimm/arabidopsis-roots-v02 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0500
- Mean Iou: 0.4449
- Mean Accuracy: 0.8899
- Overall Accuracy: 0.8899
- Accuracy Background: nan
- Accuracy Seedling: 0.8899
- Iou Background: 0.0
- Iou Seedling: 0.8899
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: 4
- eval_batch_size: 4
- 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 Seedling | Iou Background | Iou Seedling |
---|---|---|---|---|---|---|---|---|---|---|
0.4792 | 1.0 | 20 | 0.5681 | 0.4482 | 0.8964 | 0.8964 | nan | 0.8964 | 0.0 | 0.8964 |
0.3118 | 2.0 | 40 | 0.3829 | 0.4641 | 0.9282 | 0.9282 | nan | 0.9282 | 0.0 | 0.9282 |
0.2481 | 3.0 | 60 | 0.2563 | 0.4722 | 0.9445 | 0.9445 | nan | 0.9445 | 0.0 | 0.9445 |
0.2159 | 4.0 | 80 | 0.2402 | 0.4695 | 0.9389 | 0.9389 | nan | 0.9389 | 0.0 | 0.9389 |
0.1721 | 5.0 | 100 | 0.1644 | 0.4627 | 0.9254 | 0.9254 | nan | 0.9254 | 0.0 | 0.9254 |
0.1494 | 6.0 | 120 | 0.1407 | 0.4595 | 0.9189 | 0.9189 | nan | 0.9189 | 0.0 | 0.9189 |
0.1286 | 7.0 | 140 | 0.1213 | 0.4616 | 0.9233 | 0.9233 | nan | 0.9233 | 0.0 | 0.9233 |
0.1233 | 8.0 | 160 | 0.1097 | 0.4469 | 0.8939 | 0.8939 | nan | 0.8939 | 0.0 | 0.8939 |
0.108 | 9.0 | 180 | 0.0963 | 0.4419 | 0.8837 | 0.8837 | nan | 0.8837 | 0.0 | 0.8837 |
0.0968 | 10.0 | 200 | 0.0998 | 0.4491 | 0.8983 | 0.8983 | nan | 0.8983 | 0.0 | 0.8983 |
0.1041 | 11.0 | 220 | 0.0849 | 0.4354 | 0.8708 | 0.8708 | nan | 0.8708 | 0.0 | 0.8708 |
0.0842 | 12.0 | 240 | 0.0795 | 0.4444 | 0.8888 | 0.8888 | nan | 0.8888 | 0.0 | 0.8888 |
0.0896 | 13.0 | 260 | 0.0756 | 0.4402 | 0.8803 | 0.8803 | nan | 0.8803 | 0.0 | 0.8803 |
0.077 | 14.0 | 280 | 0.0797 | 0.4484 | 0.8968 | 0.8968 | nan | 0.8968 | 0.0 | 0.8968 |
0.0767 | 15.0 | 300 | 0.0690 | 0.4389 | 0.8779 | 0.8779 | nan | 0.8779 | 0.0 | 0.8779 |
0.0776 | 16.0 | 320 | 0.0665 | 0.4355 | 0.8710 | 0.8710 | nan | 0.8710 | 0.0 | 0.8710 |
0.0789 | 17.0 | 340 | 0.0680 | 0.4450 | 0.8901 | 0.8901 | nan | 0.8901 | 0.0 | 0.8901 |
0.071 | 18.0 | 360 | 0.0660 | 0.4424 | 0.8848 | 0.8848 | nan | 0.8848 | 0.0 | 0.8848 |
0.0661 | 19.0 | 380 | 0.0677 | 0.4371 | 0.8742 | 0.8742 | nan | 0.8742 | 0.0 | 0.8742 |
0.0784 | 20.0 | 400 | 0.0639 | 0.4402 | 0.8805 | 0.8805 | nan | 0.8805 | 0.0 | 0.8805 |
0.0789 | 21.0 | 420 | 0.0602 | 0.4369 | 0.8737 | 0.8737 | nan | 0.8737 | 0.0 | 0.8737 |
0.0756 | 22.0 | 440 | 0.0595 | 0.4313 | 0.8627 | 0.8627 | nan | 0.8627 | 0.0 | 0.8627 |
0.0563 | 23.0 | 460 | 0.0581 | 0.4420 | 0.8839 | 0.8839 | nan | 0.8839 | 0.0 | 0.8839 |
0.0713 | 24.0 | 480 | 0.0570 | 0.4438 | 0.8875 | 0.8875 | nan | 0.8875 | 0.0 | 0.8875 |
0.0594 | 25.0 | 500 | 0.0579 | 0.4478 | 0.8955 | 0.8955 | nan | 0.8955 | 0.0 | 0.8955 |
0.0636 | 26.0 | 520 | 0.0582 | 0.4523 | 0.9046 | 0.9046 | nan | 0.9046 | 0.0 | 0.9046 |
0.053 | 27.0 | 540 | 0.0558 | 0.4431 | 0.8863 | 0.8863 | nan | 0.8863 | 0.0 | 0.8863 |
0.0688 | 28.0 | 560 | 0.0561 | 0.4470 | 0.8939 | 0.8939 | nan | 0.8939 | 0.0 | 0.8939 |
0.0564 | 29.0 | 580 | 0.0542 | 0.4442 | 0.8885 | 0.8885 | nan | 0.8885 | 0.0 | 0.8885 |
0.0671 | 30.0 | 600 | 0.0537 | 0.4445 | 0.8891 | 0.8891 | nan | 0.8891 | 0.0 | 0.8891 |
0.0699 | 31.0 | 620 | 0.0530 | 0.4397 | 0.8794 | 0.8794 | nan | 0.8794 | 0.0 | 0.8794 |
0.0648 | 32.0 | 640 | 0.0535 | 0.4423 | 0.8845 | 0.8845 | nan | 0.8845 | 0.0 | 0.8845 |
0.058 | 33.0 | 660 | 0.0535 | 0.4467 | 0.8934 | 0.8934 | nan | 0.8934 | 0.0 | 0.8934 |
0.0582 | 34.0 | 680 | 0.0536 | 0.4513 | 0.9025 | 0.9025 | nan | 0.9025 | 0.0 | 0.9025 |
0.0561 | 35.0 | 700 | 0.0525 | 0.4444 | 0.8888 | 0.8888 | nan | 0.8888 | 0.0 | 0.8888 |
0.0731 | 36.0 | 720 | 0.0520 | 0.4439 | 0.8878 | 0.8878 | nan | 0.8878 | 0.0 | 0.8878 |
0.0503 | 37.0 | 740 | 0.0528 | 0.4484 | 0.8968 | 0.8968 | nan | 0.8968 | 0.0 | 0.8968 |
0.0895 | 38.0 | 760 | 0.0505 | 0.4388 | 0.8777 | 0.8777 | nan | 0.8777 | 0.0 | 0.8777 |
0.064 | 39.0 | 780 | 0.0508 | 0.4418 | 0.8836 | 0.8836 | nan | 0.8836 | 0.0 | 0.8836 |
0.0481 | 40.0 | 800 | 0.0512 | 0.4474 | 0.8949 | 0.8949 | nan | 0.8949 | 0.0 | 0.8949 |
0.0497 | 41.0 | 820 | 0.0533 | 0.4503 | 0.9006 | 0.9006 | nan | 0.9006 | 0.0 | 0.9006 |
0.06 | 42.0 | 840 | 0.0511 | 0.4447 | 0.8893 | 0.8893 | nan | 0.8893 | 0.0 | 0.8893 |
0.0543 | 43.0 | 860 | 0.0504 | 0.4441 | 0.8882 | 0.8882 | nan | 0.8882 | 0.0 | 0.8882 |
0.0539 | 44.0 | 880 | 0.0515 | 0.4421 | 0.8843 | 0.8843 | nan | 0.8843 | 0.0 | 0.8843 |
0.0461 | 45.0 | 900 | 0.0506 | 0.4461 | 0.8921 | 0.8921 | nan | 0.8921 | 0.0 | 0.8921 |
0.0554 | 46.0 | 920 | 0.0501 | 0.4453 | 0.8907 | 0.8907 | nan | 0.8907 | 0.0 | 0.8907 |
0.0475 | 47.0 | 940 | 0.0503 | 0.4471 | 0.8942 | 0.8942 | nan | 0.8942 | 0.0 | 0.8942 |
0.0645 | 48.0 | 960 | 0.0506 | 0.4463 | 0.8926 | 0.8926 | nan | 0.8926 | 0.0 | 0.8926 |
0.045 | 49.0 | 980 | 0.0510 | 0.4471 | 0.8942 | 0.8942 | nan | 0.8942 | 0.0 | 0.8942 |
0.0576 | 50.0 | 1000 | 0.0500 | 0.4449 | 0.8899 | 0.8899 | nan | 0.8899 | 0.0 | 0.8899 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
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