---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: parking-utcustom-train-SF-RGBD-b5_1
  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. -->

# parking-utcustom-train-SF-RGBD-b5_1

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/parking-utcustom-train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0476
- Mean Iou: 0.4942
- Mean Accuracy: 0.9883
- Overall Accuracy: 0.9883
- Accuracy Unlabeled: nan
- Accuracy Parking: nan
- Accuracy Unparking: 0.9883
- Iou Unlabeled: nan
- Iou Parking: 0.0
- Iou Unparking: 0.9883

## 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: 6e-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: 150

### Training results

| Training Loss | Epoch | Step | Accuracy Parking | Accuracy Unlabeled | Accuracy Unparking | Iou Parking | Iou Unlabeled | Iou Unparking | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
|:-------------:|:-----:|:----:|:----------------:|:------------------:|:------------------:|:-----------:|:-------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------------:|
| 0.4573        | 20.0  | 20   | nan              | nan                | 0.9829             | 0.0         | 0.0           | 0.9829        | 0.3024          | 0.9829        | 0.3276   | 0.9829           |
| 0.2183        | 40.0  | 40   | nan              | nan                | 0.9953             | 0.0         | 0.0           | 0.9953        | 0.2365          | 0.9953        | 0.3318   | 0.9953           |
| 0.1266        | 60.0  | 60   | nan              | nan                | 1.0                | nan         | nan           | 1.0           | 0.0999          | 1.0           | 1.0      | 1.0              |
| 0.0929        | 80.0  | 80   | nan              | nan                | 0.9972             | 0.0         | nan           | 0.9972        | 0.0590          | 0.9972        | 0.4986   | 0.9972           |
| 0.0649        | 100.0 | 100  | 0.0346           | 0.4992             | 0.9984             | 0.9984      | nan           | nan           | 0.9984          | nan           | 0.0      | 0.9984           |
| 0.0537        | 120.0 | 120  | 0.0377           | 0.4980             | 0.9960             | 0.9960      | nan           | nan           | 0.9960          | nan           | 0.0      | 0.9960           |
| 0.0536        | 140.0 | 140  | 0.0476           | 0.4942             | 0.9883             | 0.9883      | nan           | nan           | 0.9883          | nan           | 0.0      | 0.9883           |


### Framework versions

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