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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: 0.2168
- Mean Iou: 0.3007
- Mean Accuracy: 0.6014
- Overall Accuracy: 0.6014
- Accuracy Unlabeled: nan
- Accuracy Tool: nan
- Accuracy Wear: 0.6014
- Iou Unlabeled: 0.0
- Iou Tool: nan
- Iou Wear: 0.6014
## 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: 25
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
| 0.9147 | 1.82 | 20 | 0.9308 | 0.3010 | 0.9029 | 0.9029 | nan | nan | 0.9029 | 0.0 | 0.0 | 0.9029 |
| 0.6788 | 3.64 | 40 | 0.6521 | 0.2589 | 0.7768 | 0.7768 | nan | nan | 0.7768 | 0.0 | 0.0 | 0.7768 |
| 0.4559 | 5.45 | 60 | 0.4600 | 0.2663 | 0.7989 | 0.7989 | nan | nan | 0.7989 | 0.0 | 0.0 | 0.7989 |
| 0.3799 | 7.27 | 80 | 0.3767 | 0.2061 | 0.6182 | 0.6182 | nan | nan | 0.6182 | 0.0 | 0.0 | 0.6182 |
| 0.4438 | 9.09 | 100 | 0.3259 | 0.3479 | 0.6958 | 0.6958 | nan | nan | 0.6958 | 0.0 | nan | 0.6958 |
| 0.3534 | 10.91 | 120 | 0.3008 | 0.3057 | 0.6114 | 0.6114 | nan | nan | 0.6114 | 0.0 | nan | 0.6114 |
| 0.3332 | 12.73 | 140 | 0.2805 | 0.3631 | 0.7261 | 0.7261 | nan | nan | 0.7261 | 0.0 | nan | 0.7261 |
| 0.2543 | 14.55 | 160 | 0.2659 | 0.2927 | 0.5853 | 0.5853 | nan | nan | 0.5853 | 0.0 | nan | 0.5853 |
| 0.2746 | 16.36 | 180 | 0.2324 | 0.2724 | 0.5449 | 0.5449 | nan | nan | 0.5449 | 0.0 | nan | 0.5449 |
| 0.2532 | 18.18 | 200 | 0.2409 | 0.3597 | 0.7194 | 0.7194 | nan | nan | 0.7194 | 0.0 | nan | 0.7194 |
| 0.2353 | 20.0 | 220 | 0.2369 | 0.3070 | 0.6139 | 0.6139 | nan | nan | 0.6139 | 0.0 | nan | 0.6139 |
| 0.2192 | 21.82 | 240 | 0.2210 | 0.3041 | 0.6083 | 0.6083 | nan | nan | 0.6083 | 0.0 | nan | 0.6083 |
| 0.2469 | 23.64 | 260 | 0.2168 | 0.3007 | 0.6014 | 0.6014 | nan | nan | 0.6014 | 0.0 | nan | 0.6014 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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