---
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-segments-pv_v1_normalized_p100_4batch_fp
  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/huggingface/runs/kxwmffd1)
# segformer-b1-finetuned-segments-pv_v1_normalized_p100_4batch_fp

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: 0.0012
- Mean Iou: 0.9589
- Precision: 0.9794

## 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: 0.0004
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.001
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Precision |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
| 0.0641        | 0.9989  | 229  | 0.0082          | 0.8288   | 0.8881    |
| 0.0077        | 1.9978  | 458  | 0.0070          | 0.8228   | 0.8650    |
| 0.0058        | 2.9967  | 687  | 0.0042          | 0.8827   | 0.9339    |
| 0.005         | 4.0     | 917  | 0.0039          | 0.8849   | 0.9172    |
| 0.0044        | 4.9989  | 1146 | 0.0071          | 0.7938   | 0.8122    |
| 0.0049        | 5.9978  | 1375 | 0.0036          | 0.8914   | 0.9402    |
| 0.0045        | 6.9967  | 1604 | 0.0042          | 0.8729   | 0.9280    |
| 0.0038        | 8.0     | 1834 | 0.0035          | 0.8889   | 0.9433    |
| 0.0034        | 8.9989  | 2063 | 0.0030          | 0.9038   | 0.9357    |
| 0.0032        | 9.9978  | 2292 | 0.0026          | 0.9115   | 0.9501    |
| 0.003         | 10.9967 | 2521 | 0.0026          | 0.9136   | 0.9482    |
| 0.0031        | 12.0    | 2751 | 0.0026          | 0.9132   | 0.9461    |
| 0.0029        | 12.9989 | 2980 | 0.0026          | 0.9144   | 0.9493    |
| 0.0026        | 13.9978 | 3209 | 0.0023          | 0.9202   | 0.9414    |
| 0.0025        | 14.9967 | 3438 | 0.0024          | 0.9175   | 0.9456    |
| 0.003         | 16.0    | 3668 | 0.0032          | 0.8926   | 0.9640    |
| 0.0035        | 16.9989 | 3897 | 0.0041          | 0.8741   | 0.9007    |
| 0.0029        | 17.9978 | 4126 | 0.0022          | 0.9229   | 0.9598    |
| 0.0024        | 18.9967 | 4355 | 0.0022          | 0.9239   | 0.9549    |
| 0.0022        | 20.0    | 4585 | 0.0020          | 0.9308   | 0.9601    |
| 0.0021        | 20.9989 | 4814 | 0.0019          | 0.9325   | 0.9689    |
| 0.0021        | 21.9978 | 5043 | 0.0019          | 0.9334   | 0.9630    |
| 0.002         | 22.9967 | 5272 | 0.0018          | 0.9368   | 0.9631    |
| 0.002         | 24.0    | 5502 | 0.0019          | 0.9333   | 0.9684    |
| 0.002         | 24.9989 | 5731 | 0.0018          | 0.9381   | 0.9613    |
| 0.0022        | 25.9978 | 5960 | 0.0018          | 0.9369   | 0.9610    |
| 0.0019        | 26.9967 | 6189 | 0.0017          | 0.9413   | 0.9677    |
| 0.0018        | 28.0    | 6419 | 0.0016          | 0.9429   | 0.9629    |
| 0.0017        | 28.9989 | 6648 | 0.0016          | 0.9444   | 0.9642    |
| 0.0017        | 29.9978 | 6877 | 0.0015          | 0.9465   | 0.9741    |
| 0.0016        | 30.9967 | 7106 | 0.0014          | 0.9492   | 0.9718    |
| 0.0016        | 32.0    | 7336 | 0.0014          | 0.9499   | 0.9687    |
| 0.0015        | 32.9989 | 7565 | 0.0015          | 0.9469   | 0.9737    |
| 0.0016        | 33.9978 | 7794 | 0.0014          | 0.9514   | 0.9721    |
| 0.0015        | 34.9967 | 8023 | 0.0013          | 0.9542   | 0.9719    |
| 0.0014        | 36.0    | 8253 | 0.0013          | 0.9546   | 0.9694    |
| 0.0014        | 36.9989 | 8482 | 0.0012          | 0.9569   | 0.9740    |
| 0.0014        | 37.9978 | 8711 | 0.0012          | 0.9579   | 0.9781    |
| 0.0014        | 38.9967 | 8940 | 0.0012          | 0.9584   | 0.9759    |
| 0.0013        | 39.9564 | 9160 | 0.0012          | 0.9589   | 0.9794    |


### Framework versions

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