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---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: safety-utcustom-train-SF30-RGBD-b0
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. -->
# safety-utcustom-train-SF30-RGBD-b0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN-30 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3227
- Mean Iou: 0.5786
- Mean Accuracy: 0.6222
- Overall Accuracy: 0.9658
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.2552
- Accuracy Unsafe: 0.9891
- Iou Unlabeled: nan
- Iou Safe: 0.1917
- Iou Unsafe: 0.9655
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 0.9925 | 5.0 | 10 | 1.0612 | 0.3101 | 0.5355 | 0.8847 | nan | 0.1625 | 0.9085 | 0.0 | 0.0462 | 0.8841 |
| 0.8589 | 10.0 | 20 | 0.9441 | 0.3303 | 0.5181 | 0.9537 | nan | 0.0529 | 0.9833 | 0.0 | 0.0373 | 0.9537 |
| 0.7016 | 15.0 | 30 | 0.7764 | 0.3274 | 0.5069 | 0.9654 | nan | 0.0172 | 0.9965 | 0.0 | 0.0169 | 0.9654 |
| 0.6093 | 20.0 | 40 | 0.6213 | 0.3339 | 0.5219 | 0.9603 | nan | 0.0538 | 0.9901 | 0.0 | 0.0415 | 0.9603 |
| 0.5281 | 25.0 | 50 | 0.5431 | 0.3355 | 0.5213 | 0.9650 | nan | 0.0476 | 0.9951 | 0.0 | 0.0417 | 0.9649 |
| 0.5077 | 30.0 | 60 | 0.5043 | 0.3361 | 0.5231 | 0.9638 | nan | 0.0524 | 0.9938 | 0.0 | 0.0444 | 0.9638 |
| 0.5197 | 35.0 | 70 | 0.4579 | 0.3379 | 0.5249 | 0.9657 | nan | 0.0543 | 0.9956 | 0.0 | 0.0481 | 0.9656 |
| 0.4477 | 40.0 | 80 | 0.4340 | 0.3395 | 0.5271 | 0.9662 | nan | 0.0583 | 0.9960 | 0.0 | 0.0523 | 0.9661 |
| 0.4371 | 45.0 | 90 | 0.4033 | 0.3407 | 0.5287 | 0.9669 | nan | 0.0607 | 0.9967 | 0.0 | 0.0553 | 0.9669 |
| 0.3972 | 50.0 | 100 | 0.3975 | 0.3420 | 0.5292 | 0.9686 | nan | 0.0600 | 0.9985 | 0.0 | 0.0574 | 0.9686 |
| 0.4101 | 55.0 | 110 | 0.3777 | 0.5215 | 0.5381 | 0.9691 | nan | 0.0778 | 0.9983 | nan | 0.0740 | 0.9690 |
| 0.3528 | 60.0 | 120 | 0.3625 | 0.5360 | 0.5587 | 0.9668 | nan | 0.1229 | 0.9945 | nan | 0.1054 | 0.9667 |
| 0.3552 | 65.0 | 130 | 0.3733 | 0.5550 | 0.5829 | 0.9671 | nan | 0.1726 | 0.9932 | nan | 0.1430 | 0.9669 |
| 0.3798 | 70.0 | 140 | 0.3444 | 0.5598 | 0.5753 | 0.9722 | nan | 0.1515 | 0.9991 | nan | 0.1476 | 0.9720 |
| 0.3235 | 75.0 | 150 | 0.3461 | 0.5651 | 0.6041 | 0.9650 | nan | 0.2187 | 0.9895 | nan | 0.1656 | 0.9647 |
| 0.3457 | 80.0 | 160 | 0.3335 | 0.5638 | 0.5880 | 0.9695 | nan | 0.1806 | 0.9954 | nan | 0.1582 | 0.9693 |
| 0.318 | 85.0 | 170 | 0.3334 | 0.5739 | 0.6114 | 0.9667 | nan | 0.2321 | 0.9908 | nan | 0.1814 | 0.9665 |
| 0.32 | 90.0 | 180 | 0.3307 | 0.5779 | 0.6112 | 0.9684 | nan | 0.2299 | 0.9926 | nan | 0.1877 | 0.9681 |
| 0.3122 | 95.0 | 190 | 0.3263 | 0.5778 | 0.6175 | 0.9667 | nan | 0.2447 | 0.9904 | nan | 0.1891 | 0.9664 |
| 0.3554 | 100.0 | 200 | 0.3227 | 0.5786 | 0.6222 | 0.9658 | nan | 0.2552 | 0.9891 | nan | 0.1917 | 0.9655 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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