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---
license: other
base_model: nvidia/segformer-b2-finetuned-ade-512-512
tags:
- vision
- image-segmentation
- generated_from_trainer
metrics:
- precision
model-index:
- name: segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch_fp
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/z400vwxm)
# segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch_fp
This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b2-finetuned-ade-512-512) on the mouadenna/satellite_PV_dataset_train_test_v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0046
- Mean Iou: 0.8880
- Precision: 0.9115
## 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: 1e-05
- 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: 0.01
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
| 0.6668 | 0.9989 | 229 | 0.4009 | 0.5075 | 0.5321 |
| 0.2583 | 1.9978 | 458 | 0.1436 | 0.6208 | 0.6535 |
| 0.1355 | 2.9967 | 687 | 0.0809 | 0.7078 | 0.7644 |
| 0.088 | 4.0 | 917 | 0.0585 | 0.7472 | 0.8136 |
| 0.0638 | 4.9989 | 1146 | 0.0452 | 0.7737 | 0.8353 |
| 0.05 | 5.9978 | 1375 | 0.0365 | 0.7845 | 0.8394 |
| 0.0401 | 6.9967 | 1604 | 0.0344 | 0.8087 | 0.8717 |
| 0.0332 | 8.0 | 1834 | 0.0277 | 0.8128 | 0.8682 |
| 0.0286 | 8.9989 | 2063 | 0.0188 | 0.8210 | 0.8710 |
| 0.0247 | 9.9978 | 2292 | 0.0148 | 0.8369 | 0.8881 |
| 0.0214 | 10.9967 | 2521 | 0.0133 | 0.8332 | 0.8716 |
| 0.0189 | 12.0 | 2751 | 0.0156 | 0.8286 | 0.8597 |
| 0.017 | 12.9989 | 2980 | 0.0139 | 0.8397 | 0.8726 |
| 0.0151 | 13.9978 | 3209 | 0.0154 | 0.8544 | 0.8943 |
| 0.0139 | 14.9967 | 3438 | 0.0114 | 0.8553 | 0.8897 |
| 0.0127 | 16.0 | 3668 | 0.0108 | 0.8517 | 0.8799 |
| 0.0118 | 16.9989 | 3897 | 0.0075 | 0.8658 | 0.9040 |
| 0.0108 | 17.9978 | 4126 | 0.0094 | 0.8700 | 0.9088 |
| 0.0101 | 18.9967 | 4355 | 0.0084 | 0.8746 | 0.9151 |
| 0.0094 | 20.0 | 4585 | 0.0071 | 0.8693 | 0.8973 |
| 0.0088 | 20.9989 | 4814 | 0.0071 | 0.8668 | 0.8931 |
| 0.0082 | 21.9978 | 5043 | 0.0060 | 0.8786 | 0.9151 |
| 0.008 | 22.9967 | 5272 | 0.0063 | 0.8776 | 0.9109 |
| 0.0075 | 24.0 | 5502 | 0.0066 | 0.8776 | 0.9052 |
| 0.0071 | 24.9989 | 5731 | 0.0060 | 0.8807 | 0.9115 |
| 0.0069 | 25.9978 | 5960 | 0.0062 | 0.8766 | 0.9004 |
| 0.0065 | 26.9967 | 6189 | 0.0059 | 0.8754 | 0.8963 |
| 0.0063 | 28.0 | 6419 | 0.0062 | 0.8825 | 0.9086 |
| 0.006 | 28.9989 | 6648 | 0.0050 | 0.8839 | 0.9101 |
| 0.0059 | 29.9978 | 6877 | 0.0051 | 0.8827 | 0.9069 |
| 0.0057 | 30.9967 | 7106 | 0.0056 | 0.8822 | 0.9053 |
| 0.0055 | 32.0 | 7336 | 0.0047 | 0.8866 | 0.9133 |
| 0.0055 | 32.9989 | 7565 | 0.0046 | 0.8876 | 0.9135 |
| 0.0053 | 33.9978 | 7794 | 0.0052 | 0.8839 | 0.9053 |
| 0.0052 | 34.9967 | 8023 | 0.0048 | 0.8828 | 0.9035 |
| 0.0051 | 36.0 | 8253 | 0.0046 | 0.8897 | 0.9156 |
| 0.005 | 36.9989 | 8482 | 0.0045 | 0.8891 | 0.9137 |
| 0.005 | 37.9978 | 8711 | 0.0047 | 0.8881 | 0.9120 |
| 0.005 | 38.9967 | 8940 | 0.0047 | 0.8879 | 0.9110 |
| 0.0049 | 39.9564 | 9160 | 0.0046 | 0.8880 | 0.9115 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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