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---
license: other
base_model: nvidia/mit-b0
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: 0.0491
- Mean Iou: 0.3531
- Mean Accuracy: 0.7062
- Overall Accuracy: 0.7062
- Accuracy Unlabeled: nan
- Accuracy Mass: 0.7062
- Iou Unlabeled: 0.0
- Iou Mass: 0.7062
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 45
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Mass | Iou Unlabeled | Iou Mass |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:|
| 0.3512 | 1.25 | 20 | 0.3893 | 0.0773 | 0.1545 | 0.1545 | nan | 0.1545 | 0.0 | 0.1545 |
| 0.2286 | 2.5 | 40 | 0.2047 | 0.1937 | 0.3874 | 0.3874 | nan | 0.3874 | 0.0 | 0.3874 |
| 0.1657 | 3.75 | 60 | 0.1423 | 0.2491 | 0.4982 | 0.4982 | nan | 0.4982 | 0.0 | 0.4982 |
| 0.1581 | 5.0 | 80 | 0.1117 | 0.2649 | 0.5299 | 0.5299 | nan | 0.5299 | 0.0 | 0.5299 |
| 0.1063 | 6.25 | 100 | 0.0943 | 0.3327 | 0.6653 | 0.6653 | nan | 0.6653 | 0.0 | 0.6653 |
| 0.0829 | 7.5 | 120 | 0.0782 | 0.2983 | 0.5966 | 0.5966 | nan | 0.5966 | 0.0 | 0.5966 |
| 0.0808 | 8.75 | 140 | 0.0740 | 0.3257 | 0.6515 | 0.6515 | nan | 0.6515 | 0.0 | 0.6515 |
| 0.0694 | 10.0 | 160 | 0.0725 | 0.3503 | 0.7005 | 0.7005 | nan | 0.7005 | 0.0 | 0.7005 |
| 0.0589 | 11.25 | 180 | 0.0663 | 0.2629 | 0.5259 | 0.5259 | nan | 0.5259 | 0.0 | 0.5259 |
| 0.0473 | 12.5 | 200 | 0.0604 | 0.3685 | 0.7369 | 0.7369 | nan | 0.7369 | 0.0 | 0.7369 |
| 0.0433 | 13.75 | 220 | 0.0569 | 0.3055 | 0.6109 | 0.6109 | nan | 0.6109 | 0.0 | 0.6109 |
| 0.0511 | 15.0 | 240 | 0.0546 | 0.3572 | 0.7145 | 0.7145 | nan | 0.7145 | 0.0 | 0.7145 |
| 0.04 | 16.25 | 260 | 0.0536 | 0.3234 | 0.6467 | 0.6467 | nan | 0.6467 | 0.0 | 0.6467 |
| 0.0365 | 17.5 | 280 | 0.0555 | 0.3086 | 0.6171 | 0.6171 | nan | 0.6171 | 0.0 | 0.6171 |
| 0.0314 | 18.75 | 300 | 0.0505 | 0.3595 | 0.7191 | 0.7191 | nan | 0.7191 | 0.0 | 0.7191 |
| 0.0295 | 20.0 | 320 | 0.0536 | 0.3079 | 0.6159 | 0.6159 | nan | 0.6159 | 0.0 | 0.6159 |
| 0.0337 | 21.25 | 340 | 0.0490 | 0.3446 | 0.6891 | 0.6891 | nan | 0.6891 | 0.0 | 0.6891 |
| 0.0325 | 22.5 | 360 | 0.0489 | 0.3946 | 0.7891 | 0.7891 | nan | 0.7891 | 0.0 | 0.7891 |
| 0.0314 | 23.75 | 380 | 0.0514 | 0.3184 | 0.6368 | 0.6368 | nan | 0.6368 | 0.0 | 0.6368 |
| 0.0267 | 25.0 | 400 | 0.0485 | 0.3572 | 0.7144 | 0.7144 | nan | 0.7144 | 0.0 | 0.7144 |
| 0.0321 | 26.25 | 420 | 0.0490 | 0.3787 | 0.7573 | 0.7573 | nan | 0.7573 | 0.0 | 0.7573 |
| 0.025 | 27.5 | 440 | 0.0474 | 0.3615 | 0.7230 | 0.7230 | nan | 0.7230 | 0.0 | 0.7230 |
| 0.0225 | 28.75 | 460 | 0.0472 | 0.3660 | 0.7319 | 0.7319 | nan | 0.7319 | 0.0 | 0.7319 |
| 0.0247 | 30.0 | 480 | 0.0502 | 0.3488 | 0.6976 | 0.6976 | nan | 0.6976 | 0.0 | 0.6976 |
| 0.0216 | 31.25 | 500 | 0.0483 | 0.3536 | 0.7072 | 0.7072 | nan | 0.7072 | 0.0 | 0.7072 |
| 0.0195 | 32.5 | 520 | 0.0508 | 0.3289 | 0.6578 | 0.6578 | nan | 0.6578 | 0.0 | 0.6578 |
| 0.0259 | 33.75 | 540 | 0.0496 | 0.3846 | 0.7692 | 0.7692 | nan | 0.7692 | 0.0 | 0.7692 |
| 0.0242 | 35.0 | 560 | 0.0487 | 0.3464 | 0.6928 | 0.6928 | nan | 0.6928 | 0.0 | 0.6928 |
| 0.0217 | 36.25 | 580 | 0.0503 | 0.3325 | 0.6650 | 0.6650 | nan | 0.6650 | 0.0 | 0.6650 |
| 0.0204 | 37.5 | 600 | 0.0502 | 0.3429 | 0.6858 | 0.6858 | nan | 0.6858 | 0.0 | 0.6858 |
| 0.0204 | 38.75 | 620 | 0.0507 | 0.3457 | 0.6913 | 0.6913 | nan | 0.6913 | 0.0 | 0.6913 |
| 0.0191 | 40.0 | 640 | 0.0494 | 0.3494 | 0.6988 | 0.6988 | nan | 0.6988 | 0.0 | 0.6988 |
| 0.0204 | 41.25 | 660 | 0.0503 | 0.3426 | 0.6852 | 0.6852 | nan | 0.6852 | 0.0 | 0.6852 |
| 0.019 | 42.5 | 680 | 0.0485 | 0.3616 | 0.7232 | 0.7232 | nan | 0.7232 | 0.0 | 0.7232 |
| 0.0198 | 43.75 | 700 | 0.0494 | 0.3504 | 0.7008 | 0.7008 | nan | 0.7008 | 0.0 | 0.7008 |
| 0.0212 | 45.0 | 720 | 0.0491 | 0.3531 | 0.7062 | 0.7062 | nan | 0.7062 | 0.0 | 0.7062 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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