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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
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/mmtwbyor)
# segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch
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: nan
- Mean Iou: 0.0
- Precision: 1.0
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
| 0.01 | 0.9989 | 229 | 0.0088 | 0.8105 | 0.8817 |
| 0.0062 | 1.9978 | 458 | 0.0075 | 0.8201 | 0.8726 |
| 0.0049 | 2.9967 | 687 | 0.0063 | 0.8297 | 0.8867 |
| 0.0053 | 4.0 | 917 | 0.0055 | 0.8425 | 0.8845 |
| 0.0037 | 4.9989 | 1146 | 0.0058 | 0.8380 | 0.8823 |
| 0.0039 | 5.9978 | 1375 | 0.0211 | 0.6114 | 0.9766 |
| 0.0037 | 6.9967 | 1604 | 0.3403 | 0.0 | 1.0 |
| 0.0002 | 8.0 | 1834 | nan | 0.0 | 1.0 |
| 0.0003 | 8.9989 | 2063 | nan | 0.0 | 1.0 |
| 0.0864 | 9.9978 | 2292 | nan | 0.0 | 1.0 |
| 0.0035 | 10.9967 | 2521 | nan | 0.0 | 1.0 |
| 0.0045 | 12.0 | 2751 | nan | 0.0 | 1.0 |
| 0.0039 | 12.9989 | 2980 | nan | 0.0 | 1.0 |
| 0.8023 | 13.9978 | 3209 | nan | 0.0 | 1.0 |
| 0.0041 | 14.9967 | 3438 | nan | 0.0 | 1.0 |
| 7.0711 | 16.0 | 3668 | nan | 0.0 | 1.0 |
| 0.0039 | 16.9989 | 3897 | nan | 0.0 | 1.0 |
| 19.4385 | 17.9978 | 4126 | nan | 0.0 | 1.0 |
| 0.0001 | 18.9967 | 4355 | nan | 0.0 | 1.0 |
| 1.7398 | 20.0 | 4585 | nan | 0.0 | 1.0 |
| 0.2879 | 20.9989 | 4814 | nan | 0.0 | 1.0 |
| 0.0005 | 21.9978 | 5043 | nan | 0.0 | 1.0 |
| 5.8398 | 22.9967 | 5272 | nan | 0.0 | 1.0 |
| 0.0004 | 24.0 | 5502 | nan | 0.0 | 1.0 |
| 0.0002 | 24.9989 | 5731 | nan | 0.0 | 1.0 |
| 0.0016 | 25.9978 | 5960 | nan | 0.0 | 1.0 |
| 0.0034 | 26.9967 | 6189 | nan | 0.0 | 1.0 |
| 0.0004 | 28.0 | 6419 | nan | 0.0 | 1.0 |
| 0.0036 | 28.9989 | 6648 | nan | 0.0 | 1.0 |
| 0.0314 | 29.9978 | 6877 | nan | 0.0 | 1.0 |
| 0.0921 | 30.9967 | 7106 | nan | 0.0 | 1.0 |
| 89.1025 | 32.0 | 7336 | nan | 0.0 | 1.0 |
| 0.0073 | 32.9989 | 7565 | nan | 0.0 | 1.0 |
| 0.0126 | 33.9978 | 7794 | nan | 0.0 | 1.0 |
| 0.0094 | 34.9967 | 8023 | nan | 0.0 | 1.0 |
| 0.0001 | 36.0 | 8253 | nan | 0.0 | 1.0 |
| 4.3987 | 36.9989 | 8482 | nan | 0.0 | 1.0 |
| 0.0005 | 37.9978 | 8711 | nan | 0.0 | 1.0 |
| 0.0202 | 38.9967 | 8940 | nan | 0.0 | 1.0 |
| 0.1612 | 39.9564 | 9160 | nan | 0.0 | 1.0 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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