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---
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_segment_pv_p100_32batch
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/segformer-pv-4batches/runs/l37rzbqs)
# segformer_b1_finetuned_segment_pv_p100_32batch
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: 0.0059
- Mean Iou: 0.8651
- Precision: 0.9226
## 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: 0.00032
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|
| 0.4433 | 1.0 | 115 | 0.1593 | 0.5991 | 0.6512 |
| 0.0857 | 2.0 | 230 | 0.0270 | 0.7731 | 0.8493 |
| 0.0201 | 3.0 | 345 | 0.0116 | 0.8058 | 0.8974 |
| 0.0108 | 4.0 | 460 | 0.0084 | 0.8146 | 0.8758 |
| 0.0077 | 5.0 | 575 | 0.0070 | 0.8267 | 0.8871 |
| 0.0059 | 6.0 | 690 | 0.0072 | 0.8241 | 0.9128 |
| 0.0051 | 7.0 | 805 | 0.0058 | 0.8433 | 0.9197 |
| 0.0044 | 8.0 | 920 | 0.0059 | 0.8466 | 0.8994 |
| 0.0042 | 9.0 | 1035 | 0.0055 | 0.8474 | 0.9075 |
| 0.0037 | 10.0 | 1150 | 0.0054 | 0.8576 | 0.9100 |
| 0.0033 | 11.0 | 1265 | 0.0056 | 0.8555 | 0.9254 |
| 0.0032 | 12.0 | 1380 | 0.0059 | 0.8455 | 0.8795 |
| 0.0032 | 13.0 | 1495 | 0.0055 | 0.8600 | 0.9226 |
| 0.0033 | 14.0 | 1610 | 0.0057 | 0.8558 | 0.9234 |
| 0.0029 | 15.0 | 1725 | 0.0063 | 0.8533 | 0.9211 |
| 0.003 | 16.0 | 1840 | 0.0072 | 0.8498 | 0.9261 |
| 0.0035 | 17.0 | 1955 | 0.0102 | 0.7815 | 0.9482 |
| 0.0033 | 18.0 | 2070 | 0.0244 | 0.5662 | 0.9688 |
| 0.0028 | 19.0 | 2185 | 0.0256 | 0.5643 | 0.9675 |
| 0.0027 | 20.0 | 2300 | 0.0078 | 0.8405 | 0.9370 |
| 0.0026 | 21.0 | 2415 | 0.0241 | 0.6404 | 0.9706 |
| 0.0024 | 22.0 | 2530 | 0.0492 | 0.3084 | 0.9702 |
| 0.0025 | 23.0 | 2645 | 0.1065 | 0.0107 | 0.9109 |
| 0.0024 | 24.0 | 2760 | 0.0958 | 0.0374 | 0.8273 |
| 0.003 | 25.0 | 2875 | 0.0571 | 0.1779 | 0.9912 |
| 0.0026 | 26.0 | 2990 | 0.0968 | 0.0140 | 0.9839 |
| 0.0023 | 27.0 | 3105 | 0.0454 | 0.2833 | 0.9702 |
| 0.0022 | 28.0 | 3220 | 0.0519 | 0.2828 | 0.9658 |
| 0.0021 | 29.0 | 3335 | 0.0446 | 0.3157 | 0.9698 |
| 0.002 | 30.0 | 3450 | 0.0415 | 0.3630 | 0.9702 |
| 0.002 | 31.0 | 3565 | 0.0308 | 0.4995 | 0.9737 |
| 0.002 | 32.0 | 3680 | 0.0227 | 0.6260 | 0.9700 |
| 0.0019 | 33.0 | 3795 | 0.0131 | 0.7631 | 0.9631 |
| 0.0019 | 34.0 | 3910 | 0.0102 | 0.8131 | 0.9541 |
| 0.0021 | 35.0 | 4025 | 0.0058 | 0.8450 | 0.9449 |
| 0.0018 | 36.0 | 4140 | 0.0073 | 0.8556 | 0.9326 |
| 0.0019 | 37.0 | 4255 | 0.0060 | 0.8601 | 0.9339 |
| 0.0018 | 38.0 | 4370 | 0.0060 | 0.8654 | 0.9244 |
| 0.0019 | 39.0 | 4485 | 0.0061 | 0.8636 | 0.9248 |
| 0.0017 | 40.0 | 4600 | 0.0059 | 0.8651 | 0.9226 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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