---
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_x3_normalized_p100_4batch
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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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/ktaai3s5)
# segformer-b1-finetuned-segments-pv_v1_x3_normalized_p100_4batch

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: 0.0064
- Mean Iou: 0.8466
- Precision: 0.9220

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:---------:|
| 0.0084        | 0.9993  | 687   | 0.0063          | 0.8160   | 0.8736    |
| 0.007         | 2.0     | 1375  | 0.0060          | 0.8262   | 0.9006    |
| 0.006         | 2.9993  | 2062  | 0.0066          | 0.8072   | 0.9214    |
| 0.0049        | 4.0     | 2750  | 0.0054          | 0.8283   | 0.9287    |
| 0.004         | 4.9993  | 3437  | 0.0070          | 0.8326   | 0.9068    |
| 0.0042        | 6.0     | 4125  | 0.0053          | 0.8318   | 0.8834    |
| 0.004         | 6.9993  | 4812  | 0.0053          | 0.8370   | 0.8893    |
| 0.0037        | 8.0     | 5500  | 0.0075          | 0.8049   | 0.9404    |
| 0.0036        | 8.9993  | 6187  | 0.0074          | 0.8222   | 0.9106    |
| 0.0033        | 10.0    | 6875  | 0.0061          | 0.8297   | 0.9161    |
| 0.0031        | 10.9993 | 7562  | 0.0055          | 0.8427   | 0.9086    |
| 0.0033        | 12.0    | 8250  | 0.0052          | 0.8437   | 0.9152    |
| 0.0037        | 12.9993 | 8937  | 0.0055          | 0.8387   | 0.9186    |
| 0.0028        | 14.0    | 9625  | 0.0060          | 0.8416   | 0.9137    |
| 0.0027        | 14.9993 | 10312 | 0.0052          | 0.8489   | 0.9212    |
| 0.003         | 16.0    | 11000 | 0.0065          | 0.8393   | 0.9158    |
| 0.0025        | 16.9993 | 11687 | 0.0063          | 0.8347   | 0.9245    |
| 0.0027        | 18.0    | 12375 | 0.0065          | 0.8439   | 0.9093    |
| 0.0032        | 18.9993 | 13062 | 0.0056          | 0.8495   | 0.9186    |
| 0.0024        | 20.0    | 13750 | 0.0064          | 0.8466   | 0.9220    |


### Framework versions

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