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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_segmentsai dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2940
- Mean Iou: 0.4104
- Mean Accuracy: 0.8207
- Overall Accuracy: 0.8207
- Accuracy Unlabeled: nan
- Accuracy Tool: nan
- Accuracy Wear: 0.8207
- Iou Unlabeled: 0.0
- Iou Tool: nan
- Iou Wear: 0.8207
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
| 0.843 | 1.82 | 20 | 0.8832 | 0.4637 | 0.9274 | 0.9274 | nan | nan | 0.9274 | 0.0 | nan | 0.9274 |
| 0.6849 | 3.64 | 40 | 0.5914 | 0.4361 | 0.8722 | 0.8722 | nan | nan | 0.8722 | 0.0 | nan | 0.8722 |
| 0.5085 | 5.45 | 60 | 0.5178 | 0.4628 | 0.9256 | 0.9256 | nan | nan | 0.9256 | 0.0 | nan | 0.9256 |
| 0.4027 | 7.27 | 80 | 0.5099 | 0.4598 | 0.9195 | 0.9195 | nan | nan | 0.9195 | 0.0 | nan | 0.9195 |
| 0.3536 | 9.09 | 100 | 0.4262 | 0.4365 | 0.8730 | 0.8730 | nan | nan | 0.8730 | 0.0 | nan | 0.8730 |
| 0.3657 | 10.91 | 120 | 0.3891 | 0.4228 | 0.8457 | 0.8457 | nan | nan | 0.8457 | 0.0 | nan | 0.8457 |
| 0.3234 | 12.73 | 140 | 0.4221 | 0.4377 | 0.8754 | 0.8754 | nan | nan | 0.8754 | 0.0 | nan | 0.8754 |
| 0.2874 | 14.55 | 160 | 0.3355 | 0.4098 | 0.8197 | 0.8197 | nan | nan | 0.8197 | 0.0 | nan | 0.8197 |
| 0.2335 | 16.36 | 180 | 0.3570 | 0.4266 | 0.8531 | 0.8531 | nan | nan | 0.8531 | 0.0 | nan | 0.8531 |
| 0.2167 | 18.18 | 200 | 0.3238 | 0.4404 | 0.8808 | 0.8808 | nan | nan | 0.8808 | 0.0 | nan | 0.8808 |
| 0.2201 | 20.0 | 220 | 0.3103 | 0.4185 | 0.8370 | 0.8370 | nan | nan | 0.8370 | 0.0 | nan | 0.8370 |
| 0.205 | 21.82 | 240 | 0.2881 | 0.4115 | 0.8230 | 0.8230 | nan | nan | 0.8230 | 0.0 | nan | 0.8230 |
| 0.241 | 23.64 | 260 | 0.2940 | 0.4104 | 0.8207 | 0.8207 | nan | nan | 0.8207 | 0.0 | nan | 0.8207 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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