---
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- vision
- image-segmentation
- generated_from_trainer
metrics:
- precision
model-index:
- name: segformer-b0-finetuned-segments-pv_v1_normalized_p100_4batch
  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/g4m4ysqz)
# segformer-b0-finetuned-segments-pv_v1_normalized_p100_4batch

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 mouadenna/satellite_PV_dataset_train_test_v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0074
- Mean Iou: 0.8483
- Precision: 0.9169

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
| 0.0127        | 0.9989  | 229  | 0.0092          | 0.7982   | 0.8641    |
| 0.0077        | 1.9978  | 458  | 0.0094          | 0.7871   | 0.8456    |
| 0.006         | 2.9967  | 687  | 0.0067          | 0.8140   | 0.9089    |
| 0.0051        | 4.0     | 917  | 0.0058          | 0.8358   | 0.8713    |
| 0.0045        | 4.9989  | 1146 | 0.0059          | 0.8258   | 0.8761    |
| 0.0042        | 5.9978  | 1375 | 0.0058          | 0.8415   | 0.9018    |
| 0.0036        | 6.9967  | 1604 | 0.0051          | 0.8513   | 0.9049    |
| 0.0038        | 8.0     | 1834 | 0.0062          | 0.8226   | 0.9256    |
| 0.004         | 8.9989  | 2063 | 0.0057          | 0.8358   | 0.8913    |
| 0.0035        | 9.9978  | 2292 | 0.0053          | 0.8485   | 0.9079    |
| 0.0037        | 10.9967 | 2521 | 0.0059          | 0.8192   | 0.9056    |
| 0.0038        | 12.0    | 2751 | 0.0054          | 0.8487   | 0.8921    |
| 0.0033        | 12.9989 | 2980 | 0.0053          | 0.8541   | 0.9086    |
| 0.0028        | 13.9978 | 3209 | 0.0055          | 0.8551   | 0.8985    |
| 0.0026        | 14.9967 | 3438 | 0.0060          | 0.8483   | 0.9085    |
| 0.0026        | 16.0    | 3668 | 0.0057          | 0.8495   | 0.9076    |
| 0.0024        | 16.9989 | 3897 | 0.0058          | 0.8442   | 0.9083    |
| 0.0038        | 17.9978 | 4126 | 0.0066          | 0.8113   | 0.8910    |
| 0.0031        | 18.9967 | 4355 | 0.0062          | 0.8488   | 0.9108    |
| 0.0026        | 20.0    | 4585 | 0.0058          | 0.8575   | 0.9126    |
| 0.0024        | 20.9989 | 4814 | 0.0057          | 0.8580   | 0.9119    |
| 0.0025        | 21.9978 | 5043 | 0.0059          | 0.8505   | 0.8957    |
| 0.0031        | 22.9967 | 5272 | 0.0062          | 0.8472   | 0.9135    |
| 0.0022        | 24.0    | 5502 | 0.0055          | 0.8598   | 0.9147    |
| 0.0023        | 24.9989 | 5731 | 0.0058          | 0.8621   | 0.9090    |
| 0.0023        | 25.9978 | 5960 | 0.0064          | 0.8498   | 0.9094    |
| 0.0023        | 26.9967 | 6189 | 0.0067          | 0.8428   | 0.9137    |
| 0.0021        | 28.0    | 6419 | 0.0063          | 0.8527   | 0.9076    |
| 0.002         | 28.9989 | 6648 | 0.0065          | 0.8509   | 0.9187    |
| 0.002         | 29.9978 | 6877 | 0.0074          | 0.8424   | 0.9179    |
| 0.002         | 30.9967 | 7106 | 0.0065          | 0.8577   | 0.9116    |
| 0.0019        | 32.0    | 7336 | 0.0067          | 0.8547   | 0.9141    |
| 0.0019        | 32.9989 | 7565 | 0.0072          | 0.8519   | 0.9168    |
| 0.0019        | 33.9978 | 7794 | 0.0067          | 0.8569   | 0.9148    |
| 0.0019        | 34.9967 | 8023 | 0.0070          | 0.8544   | 0.9139    |
| 0.0017        | 36.0    | 8253 | 0.0072          | 0.8510   | 0.9124    |
| 0.0018        | 36.9989 | 8482 | 0.0081          | 0.8425   | 0.9164    |
| 0.0017        | 37.9978 | 8711 | 0.0073          | 0.8512   | 0.9155    |
| 0.0018        | 38.9967 | 8940 | 0.0073          | 0.8495   | 0.9164    |
| 0.0018        | 39.9564 | 9160 | 0.0074          | 0.8483   | 0.9169    |


### Framework versions

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