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-RGBD-b5
  results: []
---

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# safety-utcustom-train-SF30-RGBD-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:
- Loss: 0.1952
- Mean Iou: 0.6486
- Mean Accuracy: 0.7199
- Overall Accuracy: 0.9704
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.4523
- Accuracy Unsafe: 0.9874
- Iou Unlabeled: nan
- Iou Safe: 0.3271
- Iou Unsafe: 0.9700

## 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: 1e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 0.8758        | 5.0   | 10   | 0.9831          | 0.3415   | 0.6100        | 0.9154           | nan                | 0.2839        | 0.9362          | 0.0           | 0.1099   | 0.9147     |
| 0.7637        | 10.0  | 20   | 0.7236          | 0.3771   | 0.6275        | 0.9582           | nan                | 0.2745        | 0.9806          | 0.0           | 0.1735   | 0.9578     |
| 0.6698        | 15.0  | 30   | 0.5510          | 0.3789   | 0.6286        | 0.9593           | nan                | 0.2755        | 0.9818          | 0.0           | 0.1776   | 0.9590     |
| 0.5935        | 20.0  | 40   | 0.4632          | 0.3822   | 0.6388        | 0.9591           | nan                | 0.2967        | 0.9809          | 0.0           | 0.1877   | 0.9588     |
| 0.5108        | 25.0  | 50   | 0.4239          | 0.3814   | 0.6492        | 0.9560           | nan                | 0.3214        | 0.9769          | 0.0           | 0.1887   | 0.9556     |
| 0.4597        | 30.0  | 60   | 0.4134          | 0.3845   | 0.6422        | 0.9596           | nan                | 0.3034        | 0.9811          | 0.0           | 0.1943   | 0.9592     |
| 0.4307        | 35.0  | 70   | 0.3918          | 0.3900   | 0.6516        | 0.9594           | nan                | 0.3229        | 0.9803          | 0.0           | 0.2111   | 0.9590     |
| 0.367         | 40.0  | 80   | 0.3578          | 0.3885   | 0.6600        | 0.9582           | nan                | 0.3415        | 0.9784          | 0.0           | 0.2077   | 0.9577     |
| 0.3249        | 45.0  | 90   | 0.3395          | 0.3921   | 0.6587        | 0.9607           | nan                | 0.3360        | 0.9813          | 0.0           | 0.2161   | 0.9603     |
| 0.292         | 50.0  | 100  | 0.3124          | 0.3969   | 0.6622        | 0.9633           | nan                | 0.3408        | 0.9837          | 0.0           | 0.2280   | 0.9629     |
| 0.2766        | 55.0  | 110  | 0.2820          | 0.4078   | 0.6878        | 0.9644           | nan                | 0.3925        | 0.9831          | 0.0           | 0.2594   | 0.9639     |
| 0.2347        | 60.0  | 120  | 0.2673          | 0.6169   | 0.7000        | 0.9641           | nan                | 0.4181        | 0.9820          | nan           | 0.2701   | 0.9636     |
| 0.226         | 65.0  | 130  | 0.2350          | 0.6280   | 0.6854        | 0.9698           | nan                | 0.3818        | 0.9891          | nan           | 0.2865   | 0.9694     |
| 0.3262        | 70.0  | 140  | 0.2354          | 0.6338   | 0.7125        | 0.9674           | nan                | 0.4402        | 0.9848          | nan           | 0.3006   | 0.9670     |
| 0.1991        | 75.0  | 150  | 0.2231          | 0.6363   | 0.7169        | 0.9676           | nan                | 0.4492        | 0.9846          | nan           | 0.3056   | 0.9671     |
| 0.2106        | 80.0  | 160  | 0.2089          | 0.6399   | 0.7152        | 0.9688           | nan                | 0.4444        | 0.9860          | nan           | 0.3114   | 0.9683     |
| 0.1995        | 85.0  | 170  | 0.1969          | 0.6493   | 0.7179        | 0.9709           | nan                | 0.4478        | 0.9880          | nan           | 0.3281   | 0.9704     |
| 0.1981        | 90.0  | 180  | 0.1909          | 0.6503   | 0.7136        | 0.9716           | nan                | 0.4381        | 0.9892          | nan           | 0.3293   | 0.9712     |
| 0.1875        | 95.0  | 190  | 0.1965          | 0.6473   | 0.7231        | 0.9697           | nan                | 0.4598        | 0.9864          | nan           | 0.3254   | 0.9692     |
| 0.2088        | 100.0 | 200  | 0.1952          | 0.6486   | 0.7199        | 0.9704           | nan                | 0.4523        | 0.9874          | nan           | 0.3271   | 0.9700     |


### Framework versions

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