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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_16batch
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/jxdpvkao)
# segformer_b1_finetuned_segment_pv_p100_16batch
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: 0.0062
- Mean Iou: 0.8656
- Precision: 0.9155
## 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: 0.00016
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|
| 0.59 | 1.0 | 230 | 0.2289 | 0.5149 | 0.5478 |
| 0.111 | 2.0 | 460 | 0.0320 | 0.7322 | 0.8038 |
| 0.0254 | 3.0 | 690 | 0.0133 | 0.7865 | 0.8738 |
| 0.0115 | 4.0 | 920 | 0.0079 | 0.8335 | 0.8829 |
| 0.0078 | 5.0 | 1150 | 0.0076 | 0.8156 | 0.8598 |
| 0.0061 | 6.0 | 1380 | 0.0061 | 0.8436 | 0.8926 |
| 0.0051 | 7.0 | 1610 | 0.0056 | 0.8478 | 0.9170 |
| 0.0042 | 8.0 | 1840 | 0.0059 | 0.8497 | 0.8975 |
| 0.0038 | 9.0 | 2070 | 0.0062 | 0.8431 | 0.9186 |
| 0.0037 | 10.0 | 2300 | 0.0055 | 0.8529 | 0.9142 |
| 0.0036 | 11.0 | 2530 | 0.0061 | 0.8397 | 0.8834 |
| 0.0035 | 12.0 | 2760 | 0.0055 | 0.8497 | 0.8981 |
| 0.0032 | 13.0 | 2990 | 0.0055 | 0.8485 | 0.9015 |
| 0.0028 | 14.0 | 3220 | 0.0056 | 0.8549 | 0.8979 |
| 0.0028 | 15.0 | 3450 | 0.0059 | 0.8523 | 0.8975 |
| 0.0026 | 16.0 | 3680 | 0.0055 | 0.8579 | 0.9120 |
| 0.0026 | 17.0 | 3910 | 0.0056 | 0.8587 | 0.9110 |
| 0.0024 | 18.0 | 4140 | 0.0074 | 0.8295 | 0.9233 |
| 0.0029 | 19.0 | 4370 | 0.0058 | 0.8548 | 0.9092 |
| 0.0025 | 20.0 | 4600 | 0.0055 | 0.8556 | 0.8914 |
| 0.0025 | 21.0 | 4830 | 0.0054 | 0.8569 | 0.9017 |
| 0.0028 | 22.0 | 5060 | 0.0055 | 0.8622 | 0.9166 |
| 0.0024 | 23.0 | 5290 | 0.0057 | 0.8633 | 0.9216 |
| 0.0022 | 24.0 | 5520 | 0.0059 | 0.8623 | 0.9155 |
| 0.002 | 25.0 | 5750 | 0.0060 | 0.8614 | 0.9046 |
| 0.002 | 26.0 | 5980 | 0.0062 | 0.8563 | 0.9092 |
| 0.0019 | 27.0 | 6210 | 0.0059 | 0.8642 | 0.9125 |
| 0.0018 | 28.0 | 6440 | 0.0060 | 0.8656 | 0.9097 |
| 0.0018 | 29.0 | 6670 | 0.0060 | 0.8632 | 0.9174 |
| 0.0018 | 30.0 | 6900 | 0.0061 | 0.8647 | 0.9172 |
| 0.0018 | 31.0 | 7130 | 0.0062 | 0.8657 | 0.9155 |
| 0.0017 | 32.0 | 7360 | 0.0061 | 0.8650 | 0.9129 |
| 0.0017 | 33.0 | 7590 | 0.0062 | 0.8656 | 0.9138 |
| 0.0017 | 34.0 | 7820 | 0.0064 | 0.8657 | 0.9127 |
| 0.0016 | 35.0 | 8050 | 0.0065 | 0.8665 | 0.9156 |
| 0.0016 | 36.0 | 8280 | 0.0067 | 0.8624 | 0.9051 |
| 0.0015 | 37.0 | 8510 | 0.0065 | 0.8658 | 0.9116 |
| 0.0016 | 38.0 | 8740 | 0.0061 | 0.8660 | 0.9149 |
| 0.0015 | 39.0 | 8970 | 0.0063 | 0.8662 | 0.9155 |
| 0.0015 | 40.0 | 9200 | 0.0062 | 0.8656 | 0.9155 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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