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-b5
  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-RGB-b5

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN-30 dataset.
It achieves the following results on the evaluation set:
- Accuracy Safe: 0.8299
- Accuracy Unlabeled: nan
- Accuracy Unsafe: 0.9036
- Iou Safe: 0.3480
- Iou Unlabeled: 0.0
- Iou Unsafe: 0.8996
- Loss: 0.5783
- Mean Accuracy: 0.8668
- Mean Iou: 0.4158
- Overall Accuracy: 0.9013

## 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: 2e-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 | Accuracy Safe | Accuracy Unlabeled | Accuracy Unsafe | Iou Safe | Iou Unlabeled | Iou Unsafe | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
|:-------------:|:-----:|:----:|:-------------:|:------------------:|:---------------:|:--------:|:-------------:|:----------:|:---------------:|:-------------:|:--------:|:----------------:|
| 1.0614        | 5.0   | 10   | 0.1904        | nan                | 0.5439          | 0.0682   | 0.0           | 0.5350     | 1.0385          | 0.3672        | 0.2011   | 0.5327           |
| 1.0269        | 10.0  | 20   | 0.4801        | nan                | 0.5773          | 0.1795   | 0.0           | 0.5719     | 0.9975          | 0.5287        | 0.2505   | 0.5742           |
| 1.0005        | 15.0  | 30   | 0.6270        | nan                | 0.6316          | 0.2261   | 0.0           | 0.6269     | 0.9428          | 0.6293        | 0.2843   | 0.6315           |
| 0.9716        | 20.0  | 40   | 0.6870        | nan                | 0.6802          | 0.2529   | 0.0           | 0.6756     | 0.8918          | 0.6836        | 0.3095   | 0.6804           |
| 0.9255        | 25.0  | 50   | 0.7339        | nan                | 0.7081          | 0.2805   | 0.0           | 0.7037     | 0.8542          | 0.7210        | 0.3281   | 0.7089           |
| 0.9256        | 30.0  | 60   | 0.7705        | nan                | 0.7229          | 0.2781   | 0.0           | 0.7189     | 0.8330          | 0.7467        | 0.3324   | 0.7244           |
| 0.8167        | 35.0  | 70   | 0.7622        | nan                | 0.7349          | 0.3004   | 0.0           | 0.7311     | 0.8114          | 0.7485        | 0.3438   | 0.7358           |
| 0.7927        | 40.0  | 80   | 0.7776        | nan                | 0.7594          | 0.3154   | 0.0           | 0.7559     | 0.7793          | 0.7685        | 0.3571   | 0.7600           |
| 0.8227        | 45.0  | 90   | 0.8020        | nan                | 0.7821          | 0.3152   | 0.0           | 0.7789     | 0.7574          | 0.7920        | 0.3647   | 0.7827           |
| 0.81          | 50.0  | 100  | 0.8114        | nan                | 0.7983          | 0.3140   | 0.0           | 0.7955     | 0.7370          | 0.8049        | 0.3698   | 0.7987           |
| 0.7198        | 55.0  | 110  | 0.8002        | nan                | 0.8194          | 0.3303   | 0.0           | 0.8162     | 0.7118          | 0.8098        | 0.3822   | 0.8188           |
| 0.7523        | 60.0  | 120  | 0.7877        | nan                | 0.8482          | 0.3457   | 0.0           | 0.8443     | 0.6832          | 0.8179        | 0.3967   | 0.8462           |
| 0.7239        | 65.0  | 130  | 0.8112        | nan                | 0.8485          | 0.3197   | 0.0           | 0.8453     | 0.6745          | 0.8298        | 0.3883   | 0.8473           |
| 0.6235        | 70.0  | 140  | 0.7906        | nan                | 0.8686          | 0.3507   | 0.0           | 0.8649     | 0.6419          | 0.8296        | 0.4052   | 0.8662           |
| 0.6887        | 75.0  | 150  | 0.7951        | nan                | 0.8758          | 0.3568   | 0.0           | 0.8720     | 0.6302          | 0.8354        | 0.4096   | 0.8732           |
| 0.6079        | 80.0  | 160  | 0.8069        | nan                | 0.8879          | 0.3561   | 0.0           | 0.8841     | 0.6120          | 0.8474        | 0.4134   | 0.8853           |
| 0.6022        | 85.0  | 170  | 0.8126        | nan                | 0.9062          | 0.3699   | 0.0           | 0.9020     | 0.5849          | 0.8594        | 0.4240   | 0.9032           |
| 0.5748        | 90.0  | 180  | 0.8053        | nan                | 0.9047          | 0.3793   | 0.0           | 0.9005     | 0.5802          | 0.8550        | 0.4266   | 0.9016           |
| 0.6228        | 95.0  | 190  | 0.8164        | nan                | 0.9050          | 0.3624   | 0.0           | 0.9007     | 0.5793          | 0.8607        | 0.4210   | 0.9022           |
| 0.5332        | 100.0 | 200  | 0.8214        | nan                | 0.9134          | 0.3623   | 0.0           | 0.9091     | 0.5616          | 0.8674        | 0.4238   | 0.9105           |
| 0.6655        | 105.0 | 210  | 0.8262        | nan                | 0.9072          | 0.3572   | 0.0           | 0.9031     | 0.5688          | 0.8667        | 0.4201   | 0.9046           |
| 0.5835        | 110.0 | 220  | 0.8233        | nan                | 0.9092          | 0.3599   | 0.0           | 0.9050     | 0.5653          | 0.8662        | 0.4216   | 0.9064           |
| 0.5764        | 115.0 | 230  | 0.8099        | nan                | 0.9165          | 0.3783   | 0.0           | 0.9120     | 0.5460          | 0.8632        | 0.4301   | 0.9131           |
| 0.5621        | 120.0 | 240  | 0.8299        | nan                | 0.9036          | 0.3480   | 0.0           | 0.8996     | 0.5783          | 0.8668        | 0.4158   | 0.9013           |


### Framework versions

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