transformers pytorch tensorboard segformer vision image-segmentation generated_from_trainer license:other endpoints_compatible region:us
---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: safety-utcustom-train-SF30-RGB-b0
  results: []
---

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# safety-utcustom-train-SF30-RGB-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: 0.7492
- Mean Iou: 0.3878
- Mean Accuracy: 0.8431
- Overall Accuracy: 0.9233
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.7575
- Accuracy Unsafe: 0.9287
- Iou Unlabeled: 0.0
- Iou Safe: 0.2418
- Iou Unsafe: 0.9214

## 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: 9e-06
- 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: 120

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 1.1527        | 5.0   | 10   | 1.1085          | 0.0590   | 0.4585        | 0.1664           | nan                | 0.7704        | 0.1465          | 0.0           | 0.0307   | 0.1464     |
| 1.1326        | 10.0  | 20   | 1.1091          | 0.0963   | 0.6082        | 0.2699           | nan                | 0.9695        | 0.2470          | 0.0           | 0.0419   | 0.2470     |
| 1.0981        | 15.0  | 30   | 1.0980          | 0.1530   | 0.6989        | 0.4242           | nan                | 0.9922        | 0.4055          | 0.0           | 0.0535   | 0.4055     |
| 1.086         | 20.0  | 40   | 1.0822          | 0.1916   | 0.7515        | 0.5256           | nan                | 0.9927        | 0.5103          | 0.0           | 0.0644   | 0.5103     |
| 1.0466        | 25.0  | 50   | 1.0541          | 0.2226   | 0.7909        | 0.6043           | nan                | 0.9902        | 0.5917          | 0.0           | 0.0761   | 0.5917     |
| 1.0533        | 30.0  | 60   | 1.0249          | 0.2444   | 0.8167        | 0.6580           | nan                | 0.9863        | 0.6472          | 0.0           | 0.0861   | 0.6471     |
| 0.9779        | 35.0  | 70   | 1.0010          | 0.2607   | 0.8322        | 0.6966           | nan                | 0.9771        | 0.6874          | 0.0           | 0.0951   | 0.6871     |
| 0.9161        | 40.0  | 80   | 0.9695          | 0.2808   | 0.8487        | 0.7412           | nan                | 0.9635        | 0.7339          | 0.0           | 0.1091   | 0.7334     |
| 0.9843        | 45.0  | 90   | 0.9403          | 0.3004   | 0.8631        | 0.7823           | nan                | 0.9494        | 0.7768          | 0.0           | 0.1254   | 0.7759     |
| 0.9568        | 50.0  | 100  | 0.9071          | 0.3176   | 0.8663        | 0.8169           | nan                | 0.9191        | 0.8135          | 0.0           | 0.1412   | 0.8117     |
| 0.8443        | 55.0  | 110  | 0.8627          | 0.3403   | 0.8656        | 0.8576           | nan                | 0.8742        | 0.8570          | 0.0           | 0.1672   | 0.8537     |
| 0.8765        | 60.0  | 120  | 0.8488          | 0.3450   | 0.8625        | 0.8657           | nan                | 0.8591        | 0.8659          | 0.0           | 0.1729   | 0.8620     |
| 0.899         | 65.0  | 130  | 0.8429          | 0.3481   | 0.8629        | 0.8705           | nan                | 0.8548        | 0.8710          | 0.0           | 0.1772   | 0.8669     |
| 0.7713        | 70.0  | 140  | 0.8085          | 0.3632   | 0.8497        | 0.8939           | nan                | 0.8026        | 0.8969          | 0.0           | 0.1983   | 0.8912     |
| 0.8505        | 75.0  | 150  | 0.7821          | 0.3762   | 0.8465        | 0.9102           | nan                | 0.7786        | 0.9145          | 0.0           | 0.2208   | 0.9079     |
| 0.7352        | 80.0  | 160  | 0.7841          | 0.3819   | 0.8392        | 0.9173           | nan                | 0.7557        | 0.9226          | 0.0           | 0.2304   | 0.9153     |
| 0.7205        | 85.0  | 170  | 0.7502          | 0.3974   | 0.8400        | 0.9325           | nan                | 0.7413        | 0.9388          | 0.0           | 0.2613   | 0.9309     |
| 0.711         | 90.0  | 180  | 0.7417          | 0.3962   | 0.8428        | 0.9313           | nan                | 0.7484        | 0.9373          | 0.0           | 0.2591   | 0.9296     |
| 0.7855        | 95.0  | 190  | 0.7281          | 0.4003   | 0.8439        | 0.9343           | nan                | 0.7473        | 0.9404          | 0.0           | 0.2683   | 0.9327     |
| 0.7632        | 100.0 | 200  | 0.7494          | 0.3883   | 0.8419        | 0.9237           | nan                | 0.7545        | 0.9293          | 0.0           | 0.2430   | 0.9219     |
| 0.8145        | 105.0 | 210  | 0.7495          | 0.3862   | 0.8412        | 0.9219           | nan                | 0.7551        | 0.9274          | 0.0           | 0.2387   | 0.9201     |
| 0.8217        | 110.0 | 220  | 0.7355          | 0.3933   | 0.8422        | 0.9282           | nan                | 0.7502        | 0.9341          | 0.0           | 0.2533   | 0.9265     |
| 0.7784        | 115.0 | 230  | 0.7258          | 0.4088   | 0.8411        | 0.9413           | nan                | 0.7340        | 0.9481          | 0.0           | 0.2864   | 0.9400     |
| 0.8349        | 120.0 | 240  | 0.7492          | 0.3878   | 0.8431        | 0.9233           | nan                | 0.7575        | 0.9287          | 0.0           | 0.2418   | 0.9214     |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
