---
license: other
base_model: nvidia/segformer-b1-finetuned-ade-512-512
tags:
- vision
- image-segmentation
- generated_from_trainer
metrics:
- precision
model-index:
- name: segformer_b1_finetuned_segment_pv_p100_32batch
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mouadn773/segformer-pv-4batches/runs/l37rzbqs)
# segformer_b1_finetuned_segment_pv_p100_32batch

This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b1-finetuned-ade-512-512) on the mouadenna/satellite_PV_dataset_train_test_v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0059
- Mean Iou: 0.8651
- Precision: 0.9226

## 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: 0.00032
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|
| 0.4433        | 1.0   | 115  | 0.1593          | 0.5991   | 0.6512    |
| 0.0857        | 2.0   | 230  | 0.0270          | 0.7731   | 0.8493    |
| 0.0201        | 3.0   | 345  | 0.0116          | 0.8058   | 0.8974    |
| 0.0108        | 4.0   | 460  | 0.0084          | 0.8146   | 0.8758    |
| 0.0077        | 5.0   | 575  | 0.0070          | 0.8267   | 0.8871    |
| 0.0059        | 6.0   | 690  | 0.0072          | 0.8241   | 0.9128    |
| 0.0051        | 7.0   | 805  | 0.0058          | 0.8433   | 0.9197    |
| 0.0044        | 8.0   | 920  | 0.0059          | 0.8466   | 0.8994    |
| 0.0042        | 9.0   | 1035 | 0.0055          | 0.8474   | 0.9075    |
| 0.0037        | 10.0  | 1150 | 0.0054          | 0.8576   | 0.9100    |
| 0.0033        | 11.0  | 1265 | 0.0056          | 0.8555   | 0.9254    |
| 0.0032        | 12.0  | 1380 | 0.0059          | 0.8455   | 0.8795    |
| 0.0032        | 13.0  | 1495 | 0.0055          | 0.8600   | 0.9226    |
| 0.0033        | 14.0  | 1610 | 0.0057          | 0.8558   | 0.9234    |
| 0.0029        | 15.0  | 1725 | 0.0063          | 0.8533   | 0.9211    |
| 0.003         | 16.0  | 1840 | 0.0072          | 0.8498   | 0.9261    |
| 0.0035        | 17.0  | 1955 | 0.0102          | 0.7815   | 0.9482    |
| 0.0033        | 18.0  | 2070 | 0.0244          | 0.5662   | 0.9688    |
| 0.0028        | 19.0  | 2185 | 0.0256          | 0.5643   | 0.9675    |
| 0.0027        | 20.0  | 2300 | 0.0078          | 0.8405   | 0.9370    |
| 0.0026        | 21.0  | 2415 | 0.0241          | 0.6404   | 0.9706    |
| 0.0024        | 22.0  | 2530 | 0.0492          | 0.3084   | 0.9702    |
| 0.0025        | 23.0  | 2645 | 0.1065          | 0.0107   | 0.9109    |
| 0.0024        | 24.0  | 2760 | 0.0958          | 0.0374   | 0.8273    |
| 0.003         | 25.0  | 2875 | 0.0571          | 0.1779   | 0.9912    |
| 0.0026        | 26.0  | 2990 | 0.0968          | 0.0140   | 0.9839    |
| 0.0023        | 27.0  | 3105 | 0.0454          | 0.2833   | 0.9702    |
| 0.0022        | 28.0  | 3220 | 0.0519          | 0.2828   | 0.9658    |
| 0.0021        | 29.0  | 3335 | 0.0446          | 0.3157   | 0.9698    |
| 0.002         | 30.0  | 3450 | 0.0415          | 0.3630   | 0.9702    |
| 0.002         | 31.0  | 3565 | 0.0308          | 0.4995   | 0.9737    |
| 0.002         | 32.0  | 3680 | 0.0227          | 0.6260   | 0.9700    |
| 0.0019        | 33.0  | 3795 | 0.0131          | 0.7631   | 0.9631    |
| 0.0019        | 34.0  | 3910 | 0.0102          | 0.8131   | 0.9541    |
| 0.0021        | 35.0  | 4025 | 0.0058          | 0.8450   | 0.9449    |
| 0.0018        | 36.0  | 4140 | 0.0073          | 0.8556   | 0.9326    |
| 0.0019        | 37.0  | 4255 | 0.0060          | 0.8601   | 0.9339    |
| 0.0018        | 38.0  | 4370 | 0.0060          | 0.8654   | 0.9244    |
| 0.0019        | 39.0  | 4485 | 0.0061          | 0.8636   | 0.9248    |
| 0.0017        | 40.0  | 4600 | 0.0059          | 0.8651   | 0.9226    |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1