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---
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
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