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---
license: other
tags:
- vision
- image-segmentation
- 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 the HorcruxNo13/toolwear_complete_tool dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0236
- Mean Iou: 0.4952
- Mean Accuracy: 0.9903
- Overall Accuracy: 0.9903
- Accuracy Unlabeled: nan
- Accuracy Tool: 0.9903
- Iou Unlabeled: 0.0
- Iou Tool: 0.9903
## 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 | Iou Unlabeled | Iou Tool |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:|
| 0.1696 | 1.18 | 20 | 0.3490 | 0.4962 | 0.9924 | 0.9924 | nan | 0.9924 | 0.0 | 0.9924 |
| 0.1045 | 2.35 | 40 | 0.0977 | 0.4878 | 0.9755 | 0.9755 | nan | 0.9755 | 0.0 | 0.9755 |
| 0.0871 | 3.53 | 60 | 0.0650 | 0.4953 | 0.9905 | 0.9905 | nan | 0.9905 | 0.0 | 0.9905 |
| 0.0542 | 4.71 | 80 | 0.0652 | 0.4956 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 |
| 0.0507 | 5.88 | 100 | 0.0573 | 0.4952 | 0.9905 | 0.9905 | nan | 0.9905 | 0.0 | 0.9905 |
| 0.0702 | 7.06 | 120 | 0.0510 | 0.4942 | 0.9883 | 0.9883 | nan | 0.9883 | 0.0 | 0.9883 |
| 0.0455 | 8.24 | 140 | 0.0487 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 |
| 0.049 | 9.41 | 160 | 0.0430 | 0.4934 | 0.9867 | 0.9867 | nan | 0.9867 | 0.0 | 0.9867 |
| 0.048 | 10.59 | 180 | 0.0409 | 0.4940 | 0.9881 | 0.9881 | nan | 0.9881 | 0.0 | 0.9881 |
| 0.0476 | 11.76 | 200 | 0.0347 | 0.4965 | 0.9931 | 0.9931 | nan | 0.9931 | 0.0 | 0.9931 |
| 0.048 | 12.94 | 220 | 0.0366 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 |
| 0.0242 | 14.12 | 240 | 0.0341 | 0.4963 | 0.9926 | 0.9926 | nan | 0.9926 | 0.0 | 0.9926 |
| 0.0274 | 15.29 | 260 | 0.0305 | 0.4966 | 0.9933 | 0.9933 | nan | 0.9933 | 0.0 | 0.9933 |
| 0.0192 | 16.47 | 280 | 0.0318 | 0.4956 | 0.9913 | 0.9913 | nan | 0.9913 | 0.0 | 0.9913 |
| 0.0388 | 17.65 | 300 | 0.0280 | 0.4966 | 0.9932 | 0.9932 | nan | 0.9932 | 0.0 | 0.9932 |
| 0.0245 | 18.82 | 320 | 0.0280 | 0.4947 | 0.9894 | 0.9894 | nan | 0.9894 | 0.0 | 0.9894 |
| 0.0268 | 20.0 | 340 | 0.0268 | 0.4949 | 0.9899 | 0.9899 | nan | 0.9899 | 0.0 | 0.9899 |
| 0.0173 | 21.18 | 360 | 0.0278 | 0.4955 | 0.9910 | 0.9910 | nan | 0.9910 | 0.0 | 0.9910 |
| 0.0275 | 22.35 | 380 | 0.0270 | 0.4957 | 0.9914 | 0.9914 | nan | 0.9914 | 0.0 | 0.9914 |
| 0.0269 | 23.53 | 400 | 0.0271 | 0.4950 | 0.9899 | 0.9899 | nan | 0.9899 | 0.0 | 0.9899 |
| 0.0371 | 24.71 | 420 | 0.0252 | 0.4938 | 0.9876 | 0.9876 | nan | 0.9876 | 0.0 | 0.9876 |
| 0.0233 | 25.88 | 440 | 0.0264 | 0.4933 | 0.9867 | 0.9867 | nan | 0.9867 | 0.0 | 0.9867 |
| 0.0181 | 27.06 | 460 | 0.0257 | 0.4959 | 0.9918 | 0.9918 | nan | 0.9918 | 0.0 | 0.9918 |
| 0.0243 | 28.24 | 480 | 0.0255 | 0.4952 | 0.9904 | 0.9904 | nan | 0.9904 | 0.0 | 0.9904 |
| 0.0144 | 29.41 | 500 | 0.0244 | 0.4956 | 0.9912 | 0.9912 | nan | 0.9912 | 0.0 | 0.9912 |
| 0.0158 | 30.59 | 520 | 0.0251 | 0.4947 | 0.9894 | 0.9894 | nan | 0.9894 | 0.0 | 0.9894 |
| 0.017 | 31.76 | 540 | 0.0247 | 0.4955 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 |
| 0.0179 | 32.94 | 560 | 0.0237 | 0.4965 | 0.9930 | 0.9930 | nan | 0.9930 | 0.0 | 0.9930 |
| 0.0162 | 34.12 | 580 | 0.0238 | 0.4956 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 |
| 0.0191 | 35.29 | 600 | 0.0241 | 0.4950 | 0.9901 | 0.9901 | nan | 0.9901 | 0.0 | 0.9901 |
| 0.0133 | 36.47 | 620 | 0.0241 | 0.4956 | 0.9911 | 0.9911 | nan | 0.9911 | 0.0 | 0.9911 |
| 0.0118 | 37.65 | 640 | 0.0244 | 0.4948 | 0.9896 | 0.9896 | nan | 0.9896 | 0.0 | 0.9896 |
| 0.0133 | 38.82 | 660 | 0.0228 | 0.4960 | 0.9921 | 0.9921 | nan | 0.9921 | 0.0 | 0.9921 |
| 0.0197 | 40.0 | 680 | 0.0234 | 0.4957 | 0.9914 | 0.9914 | nan | 0.9914 | 0.0 | 0.9914 |
| 0.0168 | 41.18 | 700 | 0.0232 | 0.4961 | 0.9922 | 0.9922 | nan | 0.9922 | 0.0 | 0.9922 |
| 0.0119 | 42.35 | 720 | 0.0234 | 0.4957 | 0.9914 | 0.9914 | nan | 0.9914 | 0.0 | 0.9914 |
| 0.0155 | 43.53 | 740 | 0.0243 | 0.4950 | 0.9900 | 0.9900 | nan | 0.9900 | 0.0 | 0.9900 |
| 0.0126 | 44.71 | 760 | 0.0242 | 0.4949 | 0.9897 | 0.9897 | nan | 0.9897 | 0.0 | 0.9897 |
| 0.0129 | 45.88 | 780 | 0.0242 | 0.4955 | 0.9910 | 0.9910 | nan | 0.9910 | 0.0 | 0.9910 |
| 0.0116 | 47.06 | 800 | 0.0238 | 0.4953 | 0.9906 | 0.9906 | nan | 0.9906 | 0.0 | 0.9906 |
| 0.0122 | 48.24 | 820 | 0.0239 | 0.4954 | 0.9908 | 0.9908 | nan | 0.9908 | 0.0 | 0.9908 |
| 0.0164 | 49.41 | 840 | 0.0236 | 0.4952 | 0.9903 | 0.9903 | nan | 0.9903 | 0.0 | 0.9903 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.13.3
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