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---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-morphpadver1-hgo-coord-v9_mix_resample_40epochs
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. -->
# segformer-b0-finetuned-morphpadver1-hgo-coord-v9_mix_resample_40epochs
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the NICOPOI-9/morphpad_coord_hgo_512_4class_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8517
- Mean Iou: 0.5269
- Mean Accuracy: 0.6857
- Overall Accuracy: 0.6922
- Accuracy 0-0: 0.5774
- Accuracy 0-90: 0.7340
- Accuracy 90-0: 0.7692
- Accuracy 90-90: 0.6625
- Iou 0-0: 0.4919
- Iou 0-90: 0.5434
- Iou 90-0: 0.5346
- Iou 90-90: 0.5380
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy 0-0 | Accuracy 0-90 | Accuracy 90-0 | Accuracy 90-90 | Iou 0-0 | Iou 0-90 | Iou 90-0 | Iou 90-90 |
|:-------------:|:-------:|:------:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:-------------:|:-------------:|:--------------:|:-------:|:--------:|:--------:|:---------:|
| 1.4178 | 1.3638 | 4000 | 1.4017 | 0.0915 | 0.2543 | 0.2763 | 0.0012 | 0.1224 | 0.8886 | 0.0052 | 0.0012 | 0.0995 | 0.2601 | 0.0052 |
| 1.1726 | 2.7276 | 8000 | 1.3327 | 0.2022 | 0.3465 | 0.3633 | 0.1781 | 0.4945 | 0.5489 | 0.1646 | 0.1323 | 0.2709 | 0.2783 | 0.1273 |
| 1.3111 | 4.0914 | 12000 | 1.3034 | 0.2235 | 0.3684 | 0.3811 | 0.2368 | 0.5790 | 0.4089 | 0.2489 | 0.1621 | 0.3039 | 0.2584 | 0.1694 |
| 1.033 | 5.4552 | 16000 | 1.2837 | 0.2340 | 0.3853 | 0.4006 | 0.1897 | 0.5159 | 0.5735 | 0.2619 | 0.1526 | 0.2965 | 0.3101 | 0.1769 |
| 1.3103 | 6.8190 | 20000 | 1.2502 | 0.2593 | 0.4171 | 0.4339 | 0.2446 | 0.6314 | 0.5467 | 0.2456 | 0.1839 | 0.3469 | 0.3212 | 0.1851 |
| 0.6831 | 8.1827 | 24000 | 1.2405 | 0.2655 | 0.4238 | 0.4336 | 0.2449 | 0.3989 | 0.6621 | 0.3893 | 0.1849 | 0.2957 | 0.3333 | 0.2482 |
| 1.1638 | 9.5465 | 28000 | 1.1866 | 0.2955 | 0.4566 | 0.4696 | 0.3300 | 0.6108 | 0.5695 | 0.3160 | 0.2396 | 0.3484 | 0.3479 | 0.2463 |
| 1.2145 | 10.9103 | 32000 | 1.1129 | 0.3356 | 0.5008 | 0.5092 | 0.4052 | 0.5818 | 0.5926 | 0.4236 | 0.2913 | 0.3764 | 0.3705 | 0.3042 |
| 0.767 | 12.2741 | 36000 | 1.1059 | 0.3423 | 0.5078 | 0.5144 | 0.4463 | 0.5576 | 0.5978 | 0.4295 | 0.3098 | 0.3613 | 0.3732 | 0.3250 |
| 1.0089 | 13.6379 | 40000 | 1.0832 | 0.3500 | 0.5157 | 0.5252 | 0.4129 | 0.6431 | 0.5790 | 0.4280 | 0.3054 | 0.3812 | 0.3870 | 0.3263 |
| 1.0757 | 15.0017 | 44000 | 1.0207 | 0.3866 | 0.5553 | 0.5626 | 0.4802 | 0.6133 | 0.6502 | 0.4776 | 0.3529 | 0.4112 | 0.4246 | 0.3577 |
| 0.8842 | 16.3655 | 48000 | 1.0716 | 0.3737 | 0.5417 | 0.5529 | 0.4372 | 0.6738 | 0.6390 | 0.4169 | 0.3371 | 0.4152 | 0.4191 | 0.3234 |
| 0.8464 | 17.7293 | 52000 | 1.0188 | 0.4101 | 0.5795 | 0.5884 | 0.4262 | 0.6296 | 0.7147 | 0.5474 | 0.3467 | 0.4325 | 0.4543 | 0.4069 |
| 0.8371 | 19.0931 | 56000 | 0.9905 | 0.4260 | 0.5942 | 0.6027 | 0.4614 | 0.6846 | 0.6765 | 0.5542 | 0.3766 | 0.4455 | 0.4655 | 0.4166 |
| 0.7882 | 20.4569 | 60000 | 0.9542 | 0.4454 | 0.6126 | 0.6216 | 0.4838 | 0.6737 | 0.7397 | 0.5530 | 0.3925 | 0.4665 | 0.4815 | 0.4411 |
| 2.4763 | 21.8207 | 64000 | 0.9188 | 0.4671 | 0.6330 | 0.6402 | 0.5338 | 0.6708 | 0.7484 | 0.5788 | 0.4359 | 0.4820 | 0.4932 | 0.4572 |
| 0.3528 | 23.1845 | 68000 | 0.8817 | 0.4725 | 0.6381 | 0.6450 | 0.5270 | 0.6813 | 0.7379 | 0.6063 | 0.4314 | 0.4937 | 0.4905 | 0.4745 |
| 0.8088 | 24.5482 | 72000 | 0.9115 | 0.4800 | 0.6458 | 0.6500 | 0.5807 | 0.6461 | 0.7349 | 0.6217 | 0.4507 | 0.4844 | 0.4938 | 0.4912 |
| 0.8153 | 25.9120 | 76000 | 0.9558 | 0.4531 | 0.6215 | 0.6342 | 0.3715 | 0.7059 | 0.7951 | 0.6135 | 0.3382 | 0.5023 | 0.4889 | 0.4829 |
| 0.9085 | 27.2758 | 80000 | 0.9089 | 0.4777 | 0.6415 | 0.6542 | 0.4936 | 0.7556 | 0.7928 | 0.5238 | 0.4312 | 0.5149 | 0.5148 | 0.4500 |
| 0.3666 | 28.6396 | 84000 | 1.0426 | 0.4467 | 0.6141 | 0.6270 | 0.3862 | 0.6873 | 0.8064 | 0.5767 | 0.3460 | 0.5000 | 0.4754 | 0.4654 |
| 0.6065 | 30.0034 | 88000 | 0.9086 | 0.4850 | 0.6497 | 0.6557 | 0.5433 | 0.6885 | 0.7346 | 0.6323 | 0.4404 | 0.5002 | 0.5009 | 0.4985 |
| 0.1385 | 31.3672 | 92000 | 0.9247 | 0.4688 | 0.6343 | 0.6469 | 0.4228 | 0.7420 | 0.7832 | 0.5892 | 0.3792 | 0.5132 | 0.4999 | 0.4829 |
| 0.4116 | 32.7310 | 96000 | 0.8724 | 0.5014 | 0.6628 | 0.6707 | 0.5288 | 0.6729 | 0.8213 | 0.6281 | 0.4585 | 0.5268 | 0.5094 | 0.5112 |
| 0.4991 | 34.0948 | 100000 | 0.8752 | 0.5078 | 0.6693 | 0.6766 | 0.5435 | 0.7342 | 0.7515 | 0.6480 | 0.4584 | 0.5274 | 0.5232 | 0.5225 |
| 0.5235 | 35.4586 | 104000 | 0.8312 | 0.5135 | 0.6736 | 0.6814 | 0.6179 | 0.7514 | 0.7598 | 0.5651 | 0.5060 | 0.5362 | 0.5256 | 0.4861 |
| 0.6378 | 36.8224 | 108000 | 0.8729 | 0.5102 | 0.6705 | 0.6784 | 0.5636 | 0.7161 | 0.7926 | 0.6097 | 0.4781 | 0.5335 | 0.5216 | 0.5076 |
| 0.6895 | 38.1862 | 112000 | 0.9258 | 0.4833 | 0.6466 | 0.6600 | 0.4375 | 0.7335 | 0.8392 | 0.5761 | 0.3990 | 0.5343 | 0.5097 | 0.4903 |
| 0.5259 | 39.5499 | 116000 | 0.8517 | 0.5269 | 0.6857 | 0.6922 | 0.5774 | 0.7340 | 0.7692 | 0.6625 | 0.4919 | 0.5434 | 0.5346 | 0.5380 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.1.0
- Datasets 3.2.0
- Tokenizers 0.21.0
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