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---
license: other
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-toolwear
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-segments-toolwear
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co./nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1009
- Mean Iou: 0.2182
- Mean Accuracy: 0.4365
- Overall Accuracy: 0.4365
- Accuracy Unlabeled: nan
- Accuracy Tool: nan
- Accuracy Wear: 0.4365
- Iou Unlabeled: 0.0
- Iou Tool: nan
- Iou Wear: 0.4365
## 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 Unlabeled | Accuracy Tool | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
| 0.8747 | 1.18 | 20 | 0.9764 | 0.1788 | 0.5363 | 0.5363 | nan | nan | 0.5363 | 0.0 | 0.0 | 0.5363 |
| 0.6206 | 2.35 | 40 | 0.6394 | 0.1860 | 0.3719 | 0.3719 | nan | nan | 0.3719 | 0.0 | nan | 0.3719 |
| 0.4963 | 3.53 | 60 | 0.4309 | 0.2230 | 0.4460 | 0.4460 | nan | nan | 0.4460 | 0.0 | nan | 0.4460 |
| 0.3978 | 4.71 | 80 | 0.3839 | 0.3231 | 0.6463 | 0.6463 | nan | nan | 0.6463 | 0.0 | nan | 0.6463 |
| 0.3171 | 5.88 | 100 | 0.3193 | 0.2653 | 0.5306 | 0.5306 | nan | nan | 0.5306 | 0.0 | nan | 0.5306 |
| 0.3046 | 7.06 | 120 | 0.2760 | 0.1372 | 0.2745 | 0.2745 | nan | nan | 0.2745 | 0.0 | nan | 0.2745 |
| 0.2558 | 8.24 | 140 | 0.2181 | 0.2549 | 0.5097 | 0.5097 | nan | nan | 0.5097 | 0.0 | nan | 0.5097 |
| 0.225 | 9.41 | 160 | 0.1933 | 0.2673 | 0.5345 | 0.5345 | nan | nan | 0.5345 | 0.0 | nan | 0.5345 |
| 0.1532 | 10.59 | 180 | 0.1735 | 0.2673 | 0.5346 | 0.5346 | nan | nan | 0.5346 | 0.0 | nan | 0.5346 |
| 0.1505 | 11.76 | 200 | 0.1660 | 0.1857 | 0.3715 | 0.3715 | nan | nan | 0.3715 | 0.0 | nan | 0.3715 |
| 0.1222 | 12.94 | 220 | 0.1641 | 0.1508 | 0.3016 | 0.3016 | nan | nan | 0.3016 | 0.0 | nan | 0.3016 |
| 0.0921 | 14.12 | 240 | 0.1363 | 0.2869 | 0.5738 | 0.5738 | nan | nan | 0.5738 | 0.0 | nan | 0.5738 |
| 0.0792 | 15.29 | 260 | 0.1300 | 0.2245 | 0.4491 | 0.4491 | nan | nan | 0.4491 | 0.0 | nan | 0.4491 |
| 0.0804 | 16.47 | 280 | 0.1338 | 0.1910 | 0.3820 | 0.3820 | nan | nan | 0.3820 | 0.0 | nan | 0.3820 |
| 0.0732 | 17.65 | 300 | 0.1118 | 0.2583 | 0.5166 | 0.5166 | nan | nan | 0.5166 | 0.0 | nan | 0.5166 |
| 0.062 | 18.82 | 320 | 0.1102 | 0.2432 | 0.4864 | 0.4864 | nan | nan | 0.4864 | 0.0 | nan | 0.4864 |
| 0.0582 | 20.0 | 340 | 0.1023 | 0.2547 | 0.5095 | 0.5095 | nan | nan | 0.5095 | 0.0 | nan | 0.5095 |
| 0.056 | 21.18 | 360 | 0.1151 | 0.2111 | 0.4222 | 0.4222 | nan | nan | 0.4222 | 0.0 | nan | 0.4222 |
| 0.0493 | 22.35 | 380 | 0.1126 | 0.2045 | 0.4089 | 0.4089 | nan | nan | 0.4089 | 0.0 | nan | 0.4089 |
| 0.0633 | 23.53 | 400 | 0.1065 | 0.2220 | 0.4440 | 0.4440 | nan | nan | 0.4440 | 0.0 | nan | 0.4440 |
| 0.0438 | 24.71 | 420 | 0.0987 | 0.2558 | 0.5116 | 0.5116 | nan | nan | 0.5116 | 0.0 | nan | 0.5116 |
| 0.0451 | 25.88 | 440 | 0.1060 | 0.2326 | 0.4652 | 0.4652 | nan | nan | 0.4652 | 0.0 | nan | 0.4652 |
| 0.0426 | 27.06 | 460 | 0.0981 | 0.2493 | 0.4986 | 0.4986 | nan | nan | 0.4986 | 0.0 | nan | 0.4986 |
| 0.0397 | 28.24 | 480 | 0.0955 | 0.2485 | 0.4970 | 0.4970 | nan | nan | 0.4970 | 0.0 | nan | 0.4970 |
| 0.0349 | 29.41 | 500 | 0.0991 | 0.2321 | 0.4641 | 0.4641 | nan | nan | 0.4641 | 0.0 | nan | 0.4641 |
| 0.0337 | 30.59 | 520 | 0.1048 | 0.2111 | 0.4222 | 0.4222 | nan | nan | 0.4222 | 0.0 | nan | 0.4222 |
| 0.0358 | 31.76 | 540 | 0.0870 | 0.2856 | 0.5712 | 0.5712 | nan | nan | 0.5712 | 0.0 | nan | 0.5712 |
| 0.0322 | 32.94 | 560 | 0.1061 | 0.2085 | 0.4170 | 0.4170 | nan | nan | 0.4170 | 0.0 | nan | 0.4170 |
| 0.028 | 34.12 | 580 | 0.0950 | 0.2399 | 0.4798 | 0.4798 | nan | nan | 0.4798 | 0.0 | nan | 0.4798 |
| 0.0282 | 35.29 | 600 | 0.0880 | 0.2667 | 0.5335 | 0.5335 | nan | nan | 0.5335 | 0.0 | nan | 0.5335 |
| 0.0266 | 36.47 | 620 | 0.0952 | 0.2457 | 0.4914 | 0.4914 | nan | nan | 0.4914 | 0.0 | nan | 0.4914 |
| 0.0276 | 37.65 | 640 | 0.0994 | 0.2329 | 0.4658 | 0.4658 | nan | nan | 0.4658 | 0.0 | nan | 0.4658 |
| 0.0306 | 38.82 | 660 | 0.0978 | 0.2314 | 0.4627 | 0.4627 | nan | nan | 0.4627 | 0.0 | nan | 0.4627 |
| 0.0337 | 40.0 | 680 | 0.0949 | 0.2404 | 0.4809 | 0.4809 | nan | nan | 0.4809 | 0.0 | nan | 0.4809 |
| 0.0243 | 41.18 | 700 | 0.0948 | 0.2382 | 0.4765 | 0.4765 | nan | nan | 0.4765 | 0.0 | nan | 0.4765 |
| 0.0278 | 42.35 | 720 | 0.0978 | 0.2328 | 0.4655 | 0.4655 | nan | nan | 0.4655 | 0.0 | nan | 0.4655 |
| 0.0317 | 43.53 | 740 | 0.0975 | 0.2337 | 0.4675 | 0.4675 | nan | nan | 0.4675 | 0.0 | nan | 0.4675 |
| 0.0321 | 44.71 | 760 | 0.0981 | 0.2331 | 0.4663 | 0.4663 | nan | nan | 0.4663 | 0.0 | nan | 0.4663 |
| 0.0318 | 45.88 | 780 | 0.0955 | 0.2374 | 0.4748 | 0.4748 | nan | nan | 0.4748 | 0.0 | nan | 0.4748 |
| 0.0268 | 47.06 | 800 | 0.0963 | 0.2358 | 0.4715 | 0.4715 | nan | nan | 0.4715 | 0.0 | nan | 0.4715 |
| 0.0268 | 48.24 | 820 | 0.1001 | 0.2229 | 0.4459 | 0.4459 | nan | nan | 0.4459 | 0.0 | nan | 0.4459 |
| 0.0314 | 49.41 | 840 | 0.1009 | 0.2182 | 0.4365 | 0.4365 | nan | nan | 0.4365 | 0.0 | nan | 0.4365 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.13.3
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