---
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_3x_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/ydmwnhgs)
# segformer-b1-finetuned-segments-pv_v1_3x_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: 0.0087
- Mean Iou: 0.8602
- Precision: 0.9152

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:---------:|
| 0.0073        | 0.9993  | 687   | 0.0072          | 0.7997   | 0.8395    |
| 0.0052        | 2.0     | 1375  | 0.0069          | 0.8039   | 0.8906    |
| 0.0051        | 2.9993  | 2062  | 0.0060          | 0.8301   | 0.8951    |
| 0.0048        | 4.0     | 2750  | 0.0057          | 0.8223   | 0.9070    |
| 0.0039        | 4.9993  | 3437  | 0.0054          | 0.8433   | 0.9104    |
| 0.0042        | 6.0     | 4125  | 0.0054          | 0.8414   | 0.8779    |
| 0.0031        | 6.9993  | 4812  | 0.0052          | 0.8453   | 0.8852    |
| 0.0034        | 8.0     | 5500  | 0.0051          | 0.8526   | 0.9146    |
| 0.0036        | 8.9993  | 6187  | 0.0059          | 0.8319   | 0.8884    |
| 0.0027        | 10.0    | 6875  | 0.0058          | 0.8453   | 0.8990    |
| 0.0028        | 10.9993 | 7562  | 0.0052          | 0.8552   | 0.9152    |
| 0.0027        | 12.0    | 8250  | 0.0062          | 0.8459   | 0.9038    |
| 0.0032        | 12.9993 | 8937  | 0.0056          | 0.8506   | 0.9163    |
| 0.0024        | 14.0    | 9625  | 0.0062          | 0.8529   | 0.9189    |
| 0.0035        | 14.9993 | 10312 | 0.0058          | 0.8464   | 0.9102    |
| 0.0024        | 16.0    | 11000 | 0.0059          | 0.8575   | 0.9126    |
| 0.0023        | 16.9993 | 11687 | 0.0057          | 0.8527   | 0.9201    |
| 0.0024        | 18.0    | 12375 | 0.0060          | 0.8573   | 0.9177    |
| 0.0028        | 18.9993 | 13062 | 0.0063          | 0.8601   | 0.9064    |
| 0.0023        | 20.0    | 13750 | 0.0061          | 0.8589   | 0.9164    |
| 0.002         | 20.9993 | 14437 | 0.0061          | 0.8611   | 0.9046    |
| 0.002         | 22.0    | 15125 | 0.0057          | 0.8633   | 0.9143    |
| 0.002         | 22.9993 | 15812 | 0.0067          | 0.8552   | 0.9133    |
| 0.0018        | 24.0    | 16500 | 0.0068          | 0.8594   | 0.9174    |
| 0.0021        | 24.9993 | 17187 | 0.0063          | 0.8545   | 0.9111    |
| 0.0023        | 26.0    | 17875 | 0.0055          | 0.8642   | 0.9149    |
| 0.0019        | 26.9993 | 18562 | 0.0060          | 0.8627   | 0.9152    |
| 0.0017        | 28.0    | 19250 | 0.0063          | 0.8658   | 0.9148    |
| 0.0017        | 28.9993 | 19937 | 0.0067          | 0.8644   | 0.9085    |
| 0.0017        | 30.0    | 20625 | 0.0068          | 0.8578   | 0.9110    |
| 0.0017        | 30.9993 | 21312 | 0.0067          | 0.8585   | 0.9130    |
| 0.0015        | 32.0    | 22000 | 0.0069          | 0.8613   | 0.9103    |
| 0.0015        | 32.9993 | 22687 | 0.0073          | 0.8599   | 0.9200    |
| 0.0014        | 34.0    | 23375 | 0.0074          | 0.8605   | 0.9181    |
| 0.0014        | 34.9993 | 24062 | 0.0079          | 0.8581   | 0.9174    |
| 0.0013        | 36.0    | 24750 | 0.0081          | 0.8582   | 0.9123    |
| 0.0013        | 36.9993 | 25437 | 0.0084          | 0.8599   | 0.9166    |
| 0.0012        | 38.0    | 26125 | 0.0084          | 0.8603   | 0.9139    |
| 0.0013        | 38.9993 | 26812 | 0.0092          | 0.8599   | 0.9193    |
| 0.0012        | 39.9709 | 27480 | 0.0087          | 0.8602   | 0.9152    |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1