---
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_x3_normalized_p100_4batch
  results: []
---

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[<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/hwghoj9l)
# segformer-b0-finetuned-segments-pv_v1_x3_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: 0.0056
- Mean Iou: 0.8288
- Precision: 0.8928

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|
| 0.0086        | 0.9993 | 687  | 0.0068          | 0.8080   | 0.8515    |
| 0.0061        | 2.0    | 1375 | 0.0056          | 0.8257   | 0.8862    |
| 0.0058        | 2.9993 | 2062 | 0.0056          | 0.8284   | 0.9154    |
| 0.0063        | 4.0    | 2750 | 0.0055          | 0.8212   | 0.9261    |
| 0.0051        | 4.9993 | 3437 | 0.0081          | 0.7851   | 0.9189    |
| 0.0042        | 6.0    | 4125 | 0.0062          | 0.8322   | 0.9034    |
| 0.004         | 6.9993 | 4812 | 0.0067          | 0.8262   | 0.8807    |
| 0.0049        | 8.0    | 5500 | 0.0061          | 0.8271   | 0.9135    |
| 0.0043        | 8.9993 | 6187 | 0.0056          | 0.8288   | 0.8928    |


### Framework versions

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