---
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_16batch
  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/jxdpvkao)
# segformer_b1_finetuned_segment_pv_p100_16batch

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: 0.0062
- Mean Iou: 0.8656
- Precision: 0.9155

## 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: 0.00016
- 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: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|
| 0.59          | 1.0   | 230  | 0.2289          | 0.5149   | 0.5478    |
| 0.111         | 2.0   | 460  | 0.0320          | 0.7322   | 0.8038    |
| 0.0254        | 3.0   | 690  | 0.0133          | 0.7865   | 0.8738    |
| 0.0115        | 4.0   | 920  | 0.0079          | 0.8335   | 0.8829    |
| 0.0078        | 5.0   | 1150 | 0.0076          | 0.8156   | 0.8598    |
| 0.0061        | 6.0   | 1380 | 0.0061          | 0.8436   | 0.8926    |
| 0.0051        | 7.0   | 1610 | 0.0056          | 0.8478   | 0.9170    |
| 0.0042        | 8.0   | 1840 | 0.0059          | 0.8497   | 0.8975    |
| 0.0038        | 9.0   | 2070 | 0.0062          | 0.8431   | 0.9186    |
| 0.0037        | 10.0  | 2300 | 0.0055          | 0.8529   | 0.9142    |
| 0.0036        | 11.0  | 2530 | 0.0061          | 0.8397   | 0.8834    |
| 0.0035        | 12.0  | 2760 | 0.0055          | 0.8497   | 0.8981    |
| 0.0032        | 13.0  | 2990 | 0.0055          | 0.8485   | 0.9015    |
| 0.0028        | 14.0  | 3220 | 0.0056          | 0.8549   | 0.8979    |
| 0.0028        | 15.0  | 3450 | 0.0059          | 0.8523   | 0.8975    |
| 0.0026        | 16.0  | 3680 | 0.0055          | 0.8579   | 0.9120    |
| 0.0026        | 17.0  | 3910 | 0.0056          | 0.8587   | 0.9110    |
| 0.0024        | 18.0  | 4140 | 0.0074          | 0.8295   | 0.9233    |
| 0.0029        | 19.0  | 4370 | 0.0058          | 0.8548   | 0.9092    |
| 0.0025        | 20.0  | 4600 | 0.0055          | 0.8556   | 0.8914    |
| 0.0025        | 21.0  | 4830 | 0.0054          | 0.8569   | 0.9017    |
| 0.0028        | 22.0  | 5060 | 0.0055          | 0.8622   | 0.9166    |
| 0.0024        | 23.0  | 5290 | 0.0057          | 0.8633   | 0.9216    |
| 0.0022        | 24.0  | 5520 | 0.0059          | 0.8623   | 0.9155    |
| 0.002         | 25.0  | 5750 | 0.0060          | 0.8614   | 0.9046    |
| 0.002         | 26.0  | 5980 | 0.0062          | 0.8563   | 0.9092    |
| 0.0019        | 27.0  | 6210 | 0.0059          | 0.8642   | 0.9125    |
| 0.0018        | 28.0  | 6440 | 0.0060          | 0.8656   | 0.9097    |
| 0.0018        | 29.0  | 6670 | 0.0060          | 0.8632   | 0.9174    |
| 0.0018        | 30.0  | 6900 | 0.0061          | 0.8647   | 0.9172    |
| 0.0018        | 31.0  | 7130 | 0.0062          | 0.8657   | 0.9155    |
| 0.0017        | 32.0  | 7360 | 0.0061          | 0.8650   | 0.9129    |
| 0.0017        | 33.0  | 7590 | 0.0062          | 0.8656   | 0.9138    |
| 0.0017        | 34.0  | 7820 | 0.0064          | 0.8657   | 0.9127    |
| 0.0016        | 35.0  | 8050 | 0.0065          | 0.8665   | 0.9156    |
| 0.0016        | 36.0  | 8280 | 0.0067          | 0.8624   | 0.9051    |
| 0.0015        | 37.0  | 8510 | 0.0065          | 0.8658   | 0.9116    |
| 0.0016        | 38.0  | 8740 | 0.0061          | 0.8660   | 0.9149    |
| 0.0015        | 39.0  | 8970 | 0.0063          | 0.8662   | 0.9155    |
| 0.0015        | 40.0  | 9200 | 0.0062          | 0.8656   | 0.9155    |


### Framework versions

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