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---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: mit-b0_corm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mit-b0_corm
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0433
- Mean Iou: 0.9210
- Mean Accuracy: 0.9571
- Overall Accuracy: 0.9853
- Accuracy Background: 0.9977
- Accuracy Corm: 0.9360
- Accuracy Damage: 0.9377
- Iou Background: 0.9944
- Iou Corm: 0.8762
- Iou Damage: 0.8923
## 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: 8
- eval_batch_size: 8
- 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: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Corm | Accuracy Damage | Iou Background | Iou Corm | Iou Damage |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:---------------:|:--------------:|:--------:|:----------:|
| 0.933 | 0.6061 | 20 | 1.0299 | 0.3591 | 0.6054 | 0.6910 | 0.7236 | 0.1098 | 0.9827 | 0.7236 | 0.0867 | 0.2671 |
| 0.6505 | 1.2121 | 40 | 0.6909 | 0.6522 | 0.8240 | 0.9013 | 0.9328 | 0.5651 | 0.9740 | 0.9328 | 0.4509 | 0.5728 |
| 0.4133 | 1.8182 | 60 | 0.4184 | 0.7567 | 0.8872 | 0.9394 | 0.9609 | 0.7307 | 0.9701 | 0.9607 | 0.6218 | 0.6875 |
| 0.3299 | 2.4242 | 80 | 0.3451 | 0.8351 | 0.9306 | 0.9617 | 0.9751 | 0.8924 | 0.9243 | 0.9748 | 0.7569 | 0.7735 |
| 0.2594 | 3.0303 | 100 | 0.2506 | 0.8703 | 0.9412 | 0.9727 | 0.9862 | 0.8989 | 0.9384 | 0.9852 | 0.8019 | 0.8237 |
| 0.2253 | 3.6364 | 120 | 0.2006 | 0.8851 | 0.9403 | 0.9779 | 0.9939 | 0.8672 | 0.9599 | 0.9915 | 0.8207 | 0.8430 |
| 0.2222 | 4.2424 | 140 | 0.1654 | 0.8990 | 0.9490 | 0.9805 | 0.9946 | 0.9446 | 0.9079 | 0.9920 | 0.8438 | 0.8612 |
| 0.1347 | 4.8485 | 160 | 0.1413 | 0.9048 | 0.9508 | 0.9819 | 0.9956 | 0.9334 | 0.9234 | 0.9928 | 0.8526 | 0.8689 |
| 0.1366 | 5.4545 | 180 | 0.1155 | 0.9094 | 0.9516 | 0.9829 | 0.9966 | 0.9258 | 0.9325 | 0.9933 | 0.8583 | 0.8765 |
| 0.1121 | 6.0606 | 200 | 0.1086 | 0.8938 | 0.9447 | 0.9801 | 0.9961 | 0.9628 | 0.8753 | 0.9933 | 0.8392 | 0.8487 |
| 0.0982 | 6.6667 | 220 | 0.0963 | 0.9115 | 0.9524 | 0.9835 | 0.9972 | 0.9374 | 0.9227 | 0.9938 | 0.8626 | 0.8780 |
| 0.0993 | 7.2727 | 240 | 0.0892 | 0.9094 | 0.9513 | 0.9832 | 0.9968 | 0.9001 | 0.9571 | 0.9940 | 0.8570 | 0.8773 |
| 0.0813 | 7.8788 | 260 | 0.0842 | 0.9127 | 0.9543 | 0.9837 | 0.9966 | 0.9380 | 0.9281 | 0.9939 | 0.8643 | 0.8798 |
| 0.1059 | 8.4848 | 280 | 0.0774 | 0.9152 | 0.9541 | 0.9842 | 0.9973 | 0.9258 | 0.9391 | 0.9940 | 0.8673 | 0.8843 |
| 0.082 | 9.0909 | 300 | 0.0729 | 0.9159 | 0.9541 | 0.9843 | 0.9975 | 0.9294 | 0.9355 | 0.9940 | 0.8681 | 0.8854 |
| 0.0725 | 9.6970 | 320 | 0.0692 | 0.9162 | 0.9544 | 0.9844 | 0.9975 | 0.9247 | 0.9411 | 0.9941 | 0.8686 | 0.8861 |
| 0.0814 | 10.3030 | 340 | 0.0687 | 0.9161 | 0.9541 | 0.9844 | 0.9975 | 0.9155 | 0.9492 | 0.9942 | 0.8675 | 0.8865 |
| 0.076 | 10.9091 | 360 | 0.0640 | 0.9157 | 0.9555 | 0.9843 | 0.9968 | 0.9219 | 0.9479 | 0.9941 | 0.8680 | 0.8849 |
| 0.07 | 11.5152 | 380 | 0.0633 | 0.9166 | 0.9553 | 0.9845 | 0.9973 | 0.9375 | 0.9310 | 0.9941 | 0.8698 | 0.8859 |
| 0.0674 | 12.1212 | 400 | 0.0611 | 0.9176 | 0.9549 | 0.9847 | 0.9977 | 0.9217 | 0.9453 | 0.9943 | 0.8704 | 0.8881 |
| 0.0638 | 12.7273 | 420 | 0.0601 | 0.9116 | 0.9522 | 0.9836 | 0.9977 | 0.9529 | 0.9059 | 0.9941 | 0.8641 | 0.8768 |
| 0.0566 | 13.3333 | 440 | 0.0582 | 0.9176 | 0.9561 | 0.9847 | 0.9972 | 0.9322 | 0.9387 | 0.9943 | 0.8714 | 0.8872 |
| 0.0582 | 13.9394 | 460 | 0.0614 | 0.9077 | 0.9502 | 0.9829 | 0.9976 | 0.9583 | 0.8948 | 0.9941 | 0.8588 | 0.8700 |
| 0.0555 | 14.5455 | 480 | 0.0561 | 0.9146 | 0.9534 | 0.9841 | 0.9978 | 0.9481 | 0.9142 | 0.9941 | 0.8679 | 0.8817 |
| 0.053 | 15.1515 | 500 | 0.0540 | 0.9182 | 0.9551 | 0.9848 | 0.9977 | 0.9185 | 0.9492 | 0.9943 | 0.8707 | 0.8895 |
| 0.059 | 15.7576 | 520 | 0.0549 | 0.9180 | 0.9565 | 0.9848 | 0.9970 | 0.9248 | 0.9478 | 0.9943 | 0.8711 | 0.8887 |
| 0.0484 | 16.3636 | 540 | 0.0529 | 0.9177 | 0.9563 | 0.9847 | 0.9973 | 0.9405 | 0.9311 | 0.9943 | 0.8721 | 0.8866 |
| 0.0559 | 16.9697 | 560 | 0.0510 | 0.9192 | 0.9565 | 0.9850 | 0.9974 | 0.9268 | 0.9453 | 0.9943 | 0.8729 | 0.8904 |
| 0.0542 | 17.5758 | 580 | 0.0512 | 0.9190 | 0.9569 | 0.9850 | 0.9973 | 0.9351 | 0.9382 | 0.9944 | 0.8733 | 0.8894 |
| 0.0451 | 18.1818 | 600 | 0.0505 | 0.9184 | 0.9557 | 0.9848 | 0.9977 | 0.9428 | 0.9265 | 0.9943 | 0.8729 | 0.8880 |
| 0.05 | 18.7879 | 620 | 0.0499 | 0.9178 | 0.9542 | 0.9848 | 0.9979 | 0.9098 | 0.9549 | 0.9943 | 0.8691 | 0.8899 |
| 0.063 | 19.3939 | 640 | 0.0491 | 0.9190 | 0.9560 | 0.9850 | 0.9975 | 0.9221 | 0.9483 | 0.9943 | 0.8723 | 0.8904 |
| 0.0484 | 20.0 | 660 | 0.0501 | 0.9185 | 0.9569 | 0.9849 | 0.9972 | 0.9427 | 0.9308 | 0.9944 | 0.8732 | 0.8880 |
| 0.0527 | 20.6061 | 680 | 0.0492 | 0.9186 | 0.9561 | 0.9849 | 0.9976 | 0.9430 | 0.9276 | 0.9943 | 0.8732 | 0.8884 |
| 0.0583 | 21.2121 | 700 | 0.0476 | 0.9195 | 0.9563 | 0.9851 | 0.9976 | 0.9208 | 0.9506 | 0.9944 | 0.8730 | 0.8911 |
| 0.0557 | 21.8182 | 720 | 0.0488 | 0.9188 | 0.9565 | 0.9850 | 0.9973 | 0.9191 | 0.9531 | 0.9945 | 0.8723 | 0.8896 |
| 0.0458 | 22.4242 | 740 | 0.0481 | 0.9194 | 0.9568 | 0.9851 | 0.9973 | 0.9242 | 0.9489 | 0.9944 | 0.8729 | 0.8909 |
| 0.042 | 23.0303 | 760 | 0.0472 | 0.9202 | 0.9570 | 0.9852 | 0.9975 | 0.9326 | 0.9409 | 0.9944 | 0.8749 | 0.8911 |
| 0.0459 | 23.6364 | 780 | 0.0468 | 0.9191 | 0.9565 | 0.9850 | 0.9976 | 0.9423 | 0.9295 | 0.9944 | 0.8740 | 0.8889 |
| 0.0491 | 24.2424 | 800 | 0.0464 | 0.9204 | 0.9568 | 0.9852 | 0.9977 | 0.9361 | 0.9366 | 0.9944 | 0.8753 | 0.8914 |
| 0.0548 | 24.8485 | 820 | 0.0454 | 0.9201 | 0.9565 | 0.9852 | 0.9976 | 0.9244 | 0.9475 | 0.9944 | 0.8740 | 0.8917 |
| 0.0447 | 25.4545 | 840 | 0.0473 | 0.9176 | 0.9558 | 0.9847 | 0.9976 | 0.9477 | 0.9222 | 0.9944 | 0.8723 | 0.8863 |
| 0.0457 | 26.0606 | 860 | 0.0468 | 0.9203 | 0.9567 | 0.9852 | 0.9976 | 0.9270 | 0.9456 | 0.9944 | 0.8745 | 0.8922 |
| 0.0468 | 26.6667 | 880 | 0.0454 | 0.9201 | 0.9572 | 0.9852 | 0.9974 | 0.9403 | 0.9341 | 0.9944 | 0.8753 | 0.8905 |
| 0.0433 | 27.2727 | 900 | 0.0452 | 0.9208 | 0.9563 | 0.9853 | 0.9980 | 0.9339 | 0.9371 | 0.9943 | 0.8759 | 0.8923 |
| 0.0438 | 27.8788 | 920 | 0.0452 | 0.9208 | 0.9574 | 0.9853 | 0.9975 | 0.9352 | 0.9396 | 0.9944 | 0.8760 | 0.8920 |
| 0.0446 | 28.4848 | 940 | 0.0447 | 0.9210 | 0.9568 | 0.9853 | 0.9978 | 0.9349 | 0.9377 | 0.9943 | 0.8760 | 0.8926 |
| 0.0492 | 29.0909 | 960 | 0.0452 | 0.9211 | 0.9568 | 0.9853 | 0.9978 | 0.9352 | 0.9374 | 0.9943 | 0.8762 | 0.8928 |
| 0.0481 | 29.6970 | 980 | 0.0456 | 0.9195 | 0.9567 | 0.9851 | 0.9976 | 0.9443 | 0.9283 | 0.9944 | 0.8747 | 0.8893 |
| 0.0405 | 30.3030 | 1000 | 0.0447 | 0.9206 | 0.9574 | 0.9853 | 0.9975 | 0.9391 | 0.9355 | 0.9944 | 0.8758 | 0.8916 |
| 0.0505 | 30.9091 | 1020 | 0.0443 | 0.9210 | 0.9570 | 0.9853 | 0.9978 | 0.9370 | 0.9364 | 0.9944 | 0.8763 | 0.8923 |
| 0.047 | 31.5152 | 1040 | 0.0450 | 0.9204 | 0.9568 | 0.9853 | 0.9976 | 0.9223 | 0.9505 | 0.9945 | 0.8744 | 0.8923 |
| 0.0548 | 32.1212 | 1060 | 0.0452 | 0.9192 | 0.9561 | 0.9850 | 0.9978 | 0.9442 | 0.9261 | 0.9944 | 0.8744 | 0.8889 |
| 0.0445 | 32.7273 | 1080 | 0.0442 | 0.9208 | 0.9573 | 0.9853 | 0.9975 | 0.9320 | 0.9426 | 0.9944 | 0.8758 | 0.8921 |
| 0.0539 | 33.3333 | 1100 | 0.0435 | 0.9208 | 0.9571 | 0.9853 | 0.9976 | 0.9359 | 0.9379 | 0.9944 | 0.8758 | 0.8921 |
| 0.0383 | 33.9394 | 1120 | 0.0459 | 0.9171 | 0.9549 | 0.9846 | 0.9979 | 0.9493 | 0.9175 | 0.9943 | 0.8716 | 0.8853 |
| 0.0478 | 34.5455 | 1140 | 0.0443 | 0.9203 | 0.9572 | 0.9852 | 0.9974 | 0.9246 | 0.9496 | 0.9945 | 0.8748 | 0.8916 |
| 0.0432 | 35.1515 | 1160 | 0.0442 | 0.9210 | 0.9571 | 0.9853 | 0.9977 | 0.9349 | 0.9388 | 0.9944 | 0.8762 | 0.8924 |
| 0.0468 | 35.7576 | 1180 | 0.0439 | 0.9208 | 0.9572 | 0.9853 | 0.9976 | 0.9371 | 0.9368 | 0.9944 | 0.8761 | 0.8919 |
| 0.0475 | 36.3636 | 1200 | 0.0443 | 0.9209 | 0.9571 | 0.9853 | 0.9977 | 0.9371 | 0.9364 | 0.9944 | 0.8762 | 0.8921 |
| 0.0388 | 36.9697 | 1220 | 0.0436 | 0.9208 | 0.9573 | 0.9853 | 0.9976 | 0.9371 | 0.9373 | 0.9944 | 0.8761 | 0.8919 |
| 0.0468 | 37.5758 | 1240 | 0.0431 | 0.9208 | 0.9574 | 0.9853 | 0.9975 | 0.9343 | 0.9405 | 0.9944 | 0.8760 | 0.8921 |
| 0.0426 | 38.1818 | 1260 | 0.0445 | 0.9205 | 0.9570 | 0.9852 | 0.9977 | 0.9415 | 0.9318 | 0.9944 | 0.8758 | 0.8912 |
| 0.0549 | 38.7879 | 1280 | 0.0436 | 0.9209 | 0.9571 | 0.9853 | 0.9977 | 0.9373 | 0.9362 | 0.9944 | 0.8761 | 0.8921 |
| 0.045 | 39.3939 | 1300 | 0.0438 | 0.9208 | 0.9573 | 0.9853 | 0.9976 | 0.9381 | 0.9362 | 0.9944 | 0.8760 | 0.8919 |
| 0.0287 | 40.0 | 1320 | 0.0433 | 0.9210 | 0.9571 | 0.9853 | 0.9977 | 0.9360 | 0.9377 | 0.9944 | 0.8762 | 0.8923 |
### Framework versions
- Transformers 4.44.1
- Pytorch 2.6.0+cpu
- Datasets 2.21.0
- Tokenizers 0.19.1
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