---
base_model: nvidia/mit-b0
library_name: transformers
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-test
  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-b0-finetuned-test

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2090
- Mean Iou: 0.5696
- Mean Accuracy: 0.6654
- Overall Accuracy: 0.9106
- Accuracy Structure (dimensional): nan
- Accuracy Impervious (planiform): 0.9401
- Accuracy Fences: 0.0
- Accuracy Water storage/tank: nan
- Accuracy Pool < 100 sqft: nan
- Accuracy Pool > 100 sqft: 0.9530
- Accuracy Irrigated planiform: 0.8838
- Accuracy Irrigated dimensional low: 0.8370
- Accuracy Irrigated dimensional high: 0.9432
- Accuracy Irrigated bare: 0.4234
- Accuracy Irrigable planiform: 0.8112
- Accuracy Irrigable dimensional low: 0.5718
- Accuracy Irrigable dimensional high: 0.9410
- Accuracy Irrigable bare: 0.7245
- Accuracy Native planiform: nan
- Accuracy Native dimensional low: 0.0
- Accuracy Native dimensional high: 0.0
- Accuracy Native bare: 0.9472
- Accuracy Udl: nan
- Accuracy Open water: 0.9967
- Accuracy Artificial turf: 0.6743
- Iou Structure (dimensional): 0.0
- Iou Impervious (planiform): 0.8873
- Iou Fences: 0.0
- Iou Water storage/tank: nan
- Iou Pool < 100 sqft: nan
- Iou Pool > 100 sqft: 0.8999
- Iou Irrigated planiform: 0.7859
- Iou Irrigated dimensional low: 0.7122
- Iou Irrigated dimensional high: 0.8937
- Iou Irrigated bare: 0.3648
- Iou Irrigable planiform: 0.7323
- Iou Irrigable dimensional low: 0.4772
- Iou Irrigable dimensional high: 0.8844
- Iou Irrigable bare: 0.6417
- Iou Native planiform: nan
- Iou Native dimensional low: 0.0
- Iou Native dimensional high: 0.0
- Iou Native bare: 0.8356
- Iou Udl: nan
- Iou Open water: 0.9389
- Iou Artificial turf: 0.6287

## 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
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Structure (dimensional) | Accuracy Impervious (planiform) | Accuracy Fences | Accuracy Water storage/tank | Accuracy Pool < 100 sqft | Accuracy Pool > 100 sqft | Accuracy Irrigated planiform | Accuracy Irrigated dimensional low | Accuracy Irrigated dimensional high | Accuracy Irrigated bare | Accuracy Irrigable planiform | Accuracy Irrigable dimensional low | Accuracy Irrigable dimensional high | Accuracy Irrigable bare | Accuracy Native planiform | Accuracy Native dimensional low | Accuracy Native dimensional high | Accuracy Native bare | Accuracy Udl | Accuracy Open water | Accuracy Artificial turf | Iou Structure (dimensional) | Iou Impervious (planiform) | Iou Fences | Iou Water storage/tank | Iou Pool < 100 sqft | Iou Pool > 100 sqft | Iou Irrigated planiform | Iou Irrigated dimensional low | Iou Irrigated dimensional high | Iou Irrigated bare | Iou Irrigable planiform | Iou Irrigable dimensional low | Iou Irrigable dimensional high | Iou Irrigable bare | Iou Native planiform | Iou Native dimensional low | Iou Native dimensional high | Iou Native bare | Iou Udl | Iou Open water | Iou Artificial turf |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------:|:-------------------------------:|:---------------:|:---------------------------:|:------------------------:|:------------------------:|:----------------------------:|:----------------------------------:|:-----------------------------------:|:-----------------------:|:----------------------------:|:----------------------------------:|:-----------------------------------:|:-----------------------:|:-------------------------:|:-------------------------------:|:--------------------------------:|:--------------------:|:------------:|:-------------------:|:------------------------:|:---------------------------:|:--------------------------:|:----------:|:----------------------:|:-------------------:|:-------------------:|:-----------------------:|:-----------------------------:|:------------------------------:|:------------------:|:-----------------------:|:-----------------------------:|:------------------------------:|:------------------:|:--------------------:|:--------------------------:|:---------------------------:|:---------------:|:-------:|:--------------:|:-------------------:|
| 0.2526        | 15.3846 | 200  | 0.2090          | 0.5696   | 0.6654        | 0.9106           | nan                              | 0.9401                          | 0.0             | nan                         | nan                      | 0.9530                   | 0.8838                       | 0.8370                             | 0.9432                              | 0.4234                  | 0.8112                       | 0.5718                             | 0.9410                              | 0.7245                  | nan                       | 0.0                             | 0.0                              | 0.9472               | nan          | 0.9967              | 0.6743                   | 0.0                         | 0.8873                     | 0.0        | nan                    | nan                 | 0.8999              | 0.7859                  | 0.7122                        | 0.8937                         | 0.3648             | 0.7323                  | 0.4772                        | 0.8844                         | 0.6417             | nan                  | 0.0                        | 0.0                         | 0.8356          | nan     | 0.9389         | 0.6287              |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1