---
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer_finetuned_coasts
  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. -->

# segformer_finetuned_coasts

This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the peldrak/coast dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9618
- Mean Iou: 0.2104
- Mean Accuracy: 0.2661
- Overall Accuracy: 0.7259
- Accuracy Water: nan
- Accuracy Whitewater: 0.0519
- Accuracy Sediment: 0.0150
- Accuracy Other Natural Terrain: 0.0024
- Accuracy Vegetation: 0.5470
- Accuracy Development: 0.0259
- Accuracy Unknown: 0.9547
- Iou Water: 0.0
- Iou Whitewater: 0.0160
- Iou Sediment: 0.0134
- Iou Other Natural Terrain: 0.0022
- Iou Vegetation: 0.4628
- Iou Development: 0.0254
- Iou Unknown: 0.9532

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.05

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|
| 1.7548        | 0.01  | 20   | 1.3306          | 0.1980   | 0.3291        | 0.6833           | nan            | 0.5156              | 0.0933            | 0.0701                         | 0.2872              | 0.0553               | 0.9531           | 0.0       | 0.0267         | 0.0755       | 0.0363                    | 0.2664         | 0.0349          | 0.9461      |
| 1.5812        | 0.02  | 40   | 1.0977          | 0.2226   | 0.2976        | 0.7293           | nan            | 0.1333              | 0.0446            | 0.0449                         | 0.5497              | 0.0618               | 0.9509           | 0.0       | 0.0191         | 0.0357       | 0.0298                    | 0.4724         | 0.0520          | 0.9489      |
| 1.3861        | 0.04  | 60   | 1.0240          | 0.2251   | 0.2920        | 0.7480           | nan            | 0.0760              | 0.0161            | 0.0261                         | 0.6433              | 0.0341               | 0.9564           | 0.0       | 0.0162         | 0.0144       | 0.0189                    | 0.5387         | 0.0327          | 0.9549      |
| 1.023         | 0.05  | 80   | 0.9618          | 0.2104   | 0.2661        | 0.7259           | nan            | 0.0519              | 0.0150            | 0.0024                         | 0.5470              | 0.0259               | 0.9547           | 0.0       | 0.0160         | 0.0134       | 0.0022                    | 0.4628         | 0.0254          | 0.9532      |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3