---
license: other
base_model: nvidia/segformer-b2-finetuned-ade-512-512
tags:
- vision
- image-segmentation
- generated_from_trainer
metrics:
- precision
model-index:
- name: segformer-b2-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/z400vwxm)
# segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch_fp

This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b2-finetuned-ade-512-512) on the mouadenna/satellite_PV_dataset_train_test_v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0046
- Mean Iou: 0.8880
- Precision: 0.9115

## 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: 1e-05
- 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: 0.01
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Precision |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
| 0.6668        | 0.9989  | 229  | 0.4009          | 0.5075   | 0.5321    |
| 0.2583        | 1.9978  | 458  | 0.1436          | 0.6208   | 0.6535    |
| 0.1355        | 2.9967  | 687  | 0.0809          | 0.7078   | 0.7644    |
| 0.088         | 4.0     | 917  | 0.0585          | 0.7472   | 0.8136    |
| 0.0638        | 4.9989  | 1146 | 0.0452          | 0.7737   | 0.8353    |
| 0.05          | 5.9978  | 1375 | 0.0365          | 0.7845   | 0.8394    |
| 0.0401        | 6.9967  | 1604 | 0.0344          | 0.8087   | 0.8717    |
| 0.0332        | 8.0     | 1834 | 0.0277          | 0.8128   | 0.8682    |
| 0.0286        | 8.9989  | 2063 | 0.0188          | 0.8210   | 0.8710    |
| 0.0247        | 9.9978  | 2292 | 0.0148          | 0.8369   | 0.8881    |
| 0.0214        | 10.9967 | 2521 | 0.0133          | 0.8332   | 0.8716    |
| 0.0189        | 12.0    | 2751 | 0.0156          | 0.8286   | 0.8597    |
| 0.017         | 12.9989 | 2980 | 0.0139          | 0.8397   | 0.8726    |
| 0.0151        | 13.9978 | 3209 | 0.0154          | 0.8544   | 0.8943    |
| 0.0139        | 14.9967 | 3438 | 0.0114          | 0.8553   | 0.8897    |
| 0.0127        | 16.0    | 3668 | 0.0108          | 0.8517   | 0.8799    |
| 0.0118        | 16.9989 | 3897 | 0.0075          | 0.8658   | 0.9040    |
| 0.0108        | 17.9978 | 4126 | 0.0094          | 0.8700   | 0.9088    |
| 0.0101        | 18.9967 | 4355 | 0.0084          | 0.8746   | 0.9151    |
| 0.0094        | 20.0    | 4585 | 0.0071          | 0.8693   | 0.8973    |
| 0.0088        | 20.9989 | 4814 | 0.0071          | 0.8668   | 0.8931    |
| 0.0082        | 21.9978 | 5043 | 0.0060          | 0.8786   | 0.9151    |
| 0.008         | 22.9967 | 5272 | 0.0063          | 0.8776   | 0.9109    |
| 0.0075        | 24.0    | 5502 | 0.0066          | 0.8776   | 0.9052    |
| 0.0071        | 24.9989 | 5731 | 0.0060          | 0.8807   | 0.9115    |
| 0.0069        | 25.9978 | 5960 | 0.0062          | 0.8766   | 0.9004    |
| 0.0065        | 26.9967 | 6189 | 0.0059          | 0.8754   | 0.8963    |
| 0.0063        | 28.0    | 6419 | 0.0062          | 0.8825   | 0.9086    |
| 0.006         | 28.9989 | 6648 | 0.0050          | 0.8839   | 0.9101    |
| 0.0059        | 29.9978 | 6877 | 0.0051          | 0.8827   | 0.9069    |
| 0.0057        | 30.9967 | 7106 | 0.0056          | 0.8822   | 0.9053    |
| 0.0055        | 32.0    | 7336 | 0.0047          | 0.8866   | 0.9133    |
| 0.0055        | 32.9989 | 7565 | 0.0046          | 0.8876   | 0.9135    |
| 0.0053        | 33.9978 | 7794 | 0.0052          | 0.8839   | 0.9053    |
| 0.0052        | 34.9967 | 8023 | 0.0048          | 0.8828   | 0.9035    |
| 0.0051        | 36.0    | 8253 | 0.0046          | 0.8897   | 0.9156    |
| 0.005         | 36.9989 | 8482 | 0.0045          | 0.8891   | 0.9137    |
| 0.005         | 37.9978 | 8711 | 0.0047          | 0.8881   | 0.9120    |
| 0.005         | 38.9967 | 8940 | 0.0047          | 0.8879   | 0.9110    |
| 0.0049        | 39.9564 | 9160 | 0.0046          | 0.8880   | 0.9115    |


### Framework versions

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