---
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_t4_4batch_augx3
  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/1oi9s8vu)
# segformer-b1-finetuned-segments-pv_v1_normalized_t4_4batch_augx3

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: 0.0111
- Mean Iou: 0.8679
- Precision: 0.9181

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Mean Iou | Precision |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:---------:|
| 0.0086        | 0.9978  | 343   | 0.0064          | 0.8154   | 0.9105    |
| 0.0031        | 1.9985  | 687   | 0.0058          | 0.8290   | 0.9324    |
| 0.0059        | 2.9993  | 1031  | 0.0061          | 0.8077   | 0.9352    |
| 0.0031        | 4.0     | 1375  | 0.0057          | 0.8347   | 0.9182    |
| 0.0043        | 4.9978  | 1718  | 0.0059          | 0.8275   | 0.8747    |
| 0.0034        | 5.9985  | 2062  | 0.0054          | 0.8499   | 0.8922    |
| 0.0035        | 6.9993  | 2406  | 0.0055          | 0.8508   | 0.9072    |
| 0.0037        | 8.0     | 2750  | 0.0055          | 0.8466   | 0.9201    |
| 0.0028        | 8.9978  | 3093  | 0.0054          | 0.8590   | 0.9139    |
| 0.0031        | 9.9985  | 3437  | 0.0055          | 0.8562   | 0.9135    |
| 0.002         | 10.9993 | 3781  | 0.0054          | 0.8619   | 0.9175    |
| 0.0019        | 12.0    | 4125  | 0.0056          | 0.8649   | 0.9131    |
| 0.0023        | 12.9978 | 4468  | 0.0061          | 0.8632   | 0.9195    |
| 0.0028        | 13.9985 | 4812  | 0.0061          | 0.8553   | 0.9174    |
| 0.0041        | 14.9993 | 5156  | 0.0072          | 0.8573   | 0.9172    |
| 0.002         | 16.0    | 5500  | 0.0063          | 0.8643   | 0.9136    |
| 0.0019        | 16.9978 | 5843  | 0.0068          | 0.8637   | 0.9185    |
| 0.0019        | 17.9985 | 6187  | 0.0073          | 0.8598   | 0.9123    |
| 0.0015        | 18.9993 | 6531  | 0.0070          | 0.8620   | 0.9108    |
| 0.0019        | 20.0    | 6875  | 0.0073          | 0.8602   | 0.9163    |
| 0.0017        | 20.9978 | 7218  | 0.0071          | 0.8669   | 0.9229    |
| 0.0014        | 21.9985 | 7562  | 0.0081          | 0.8633   | 0.9198    |
| 0.0027        | 22.9993 | 7906  | 0.0089          | 0.8573   | 0.9138    |
| 0.0017        | 24.0    | 8250  | 0.0086          | 0.8570   | 0.9114    |
| 0.0011        | 24.9978 | 8593  | 0.0087          | 0.8635   | 0.9152    |
| 0.0013        | 25.9985 | 8937  | 0.0100          | 0.8583   | 0.9203    |
| 0.0012        | 26.9993 | 9281  | 0.0085          | 0.8651   | 0.9113    |
| 0.0015        | 28.0    | 9625  | 0.0091          | 0.8697   | 0.9179    |
| 0.0011        | 28.9978 | 9968  | 0.0091          | 0.8684   | 0.9204    |
| 0.0012        | 29.9985 | 10312 | 0.0099          | 0.8658   | 0.9152    |
| 0.001         | 30.9993 | 10656 | 0.0098          | 0.8663   | 0.9170    |
| 0.0011        | 32.0    | 11000 | 0.0100          | 0.8680   | 0.9174    |
| 0.0008        | 32.9978 | 11343 | 0.0102          | 0.8675   | 0.9181    |
| 0.0009        | 33.9985 | 11687 | 0.0107          | 0.8669   | 0.9180    |
| 0.0011        | 34.9993 | 12031 | 0.0107          | 0.8681   | 0.9214    |
| 0.001         | 36.0    | 12375 | 0.0113          | 0.8677   | 0.9166    |
| 0.001         | 36.9978 | 12718 | 0.0115          | 0.8669   | 0.9179    |
| 0.001         | 37.9985 | 13062 | 0.0109          | 0.8694   | 0.9206    |
| 0.0008        | 38.9993 | 13406 | 0.0112          | 0.8681   | 0.9175    |
| 0.0009        | 39.9127 | 13720 | 0.0111          | 0.8679   | 0.9181    |


### Framework versions

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