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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-segments-pv_v1_x3_normalized_p100_4batch
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/ktaai3s5)
# segformer-b1-finetuned-segments-pv_v1_x3_normalized_p100_4batch
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: 0.0064
- Mean Iou: 0.8466
- Precision: 0.9220
## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:---------:|
| 0.0084 | 0.9993 | 687 | 0.0063 | 0.8160 | 0.8736 |
| 0.007 | 2.0 | 1375 | 0.0060 | 0.8262 | 0.9006 |
| 0.006 | 2.9993 | 2062 | 0.0066 | 0.8072 | 0.9214 |
| 0.0049 | 4.0 | 2750 | 0.0054 | 0.8283 | 0.9287 |
| 0.004 | 4.9993 | 3437 | 0.0070 | 0.8326 | 0.9068 |
| 0.0042 | 6.0 | 4125 | 0.0053 | 0.8318 | 0.8834 |
| 0.004 | 6.9993 | 4812 | 0.0053 | 0.8370 | 0.8893 |
| 0.0037 | 8.0 | 5500 | 0.0075 | 0.8049 | 0.9404 |
| 0.0036 | 8.9993 | 6187 | 0.0074 | 0.8222 | 0.9106 |
| 0.0033 | 10.0 | 6875 | 0.0061 | 0.8297 | 0.9161 |
| 0.0031 | 10.9993 | 7562 | 0.0055 | 0.8427 | 0.9086 |
| 0.0033 | 12.0 | 8250 | 0.0052 | 0.8437 | 0.9152 |
| 0.0037 | 12.9993 | 8937 | 0.0055 | 0.8387 | 0.9186 |
| 0.0028 | 14.0 | 9625 | 0.0060 | 0.8416 | 0.9137 |
| 0.0027 | 14.9993 | 10312 | 0.0052 | 0.8489 | 0.9212 |
| 0.003 | 16.0 | 11000 | 0.0065 | 0.8393 | 0.9158 |
| 0.0025 | 16.9993 | 11687 | 0.0063 | 0.8347 | 0.9245 |
| 0.0027 | 18.0 | 12375 | 0.0065 | 0.8439 | 0.9093 |
| 0.0032 | 18.9993 | 13062 | 0.0056 | 0.8495 | 0.9186 |
| 0.0024 | 20.0 | 13750 | 0.0064 | 0.8466 | 0.9220 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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