---
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_normalized_t4_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/ovjr83to)
# segformer-b1-finetuned-segments-pv_v1_normalized_t4_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: 0.0107
- Mean Iou: 0.8767
- Precision: 0.9236

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Precision |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
| 0.0061        | 0.9935  | 114  | 0.0077          | 0.7881   | 0.8686    |
| 0.0043        | 1.9956  | 229  | 0.0063          | 0.8084   | 0.8541    |
| 0.0051        | 2.9978  | 344  | 0.0052          | 0.8338   | 0.9445    |
| 0.0141        | 4.0     | 459  | 0.0054          | 0.8421   | 0.8811    |
| 0.0043        | 4.9935  | 573  | 0.0052          | 0.8414   | 0.9099    |
| 0.0027        | 5.9956  | 688  | 0.0057          | 0.8450   | 0.8967    |
| 0.0054        | 6.9978  | 803  | 0.0052          | 0.8505   | 0.9364    |
| 0.0024        | 8.0     | 918  | 0.0064          | 0.8408   | 0.9001    |
| 0.0024        | 8.9935  | 1032 | 0.0068          | 0.8378   | 0.9158    |
| 0.0033        | 9.9956  | 1147 | 0.0055          | 0.8643   | 0.9156    |
| 0.0014        | 10.9978 | 1262 | 0.0055          | 0.8611   | 0.9048    |
| 0.0019        | 12.0    | 1377 | 0.0070          | 0.8410   | 0.8900    |
| 0.0025        | 12.9935 | 1491 | 0.0060          | 0.8629   | 0.9112    |
| 0.0018        | 13.9956 | 1606 | 0.0063          | 0.8577   | 0.9294    |
| 0.002         | 14.9978 | 1721 | 0.0063          | 0.8539   | 0.8888    |
| 0.002         | 16.0    | 1836 | 0.0072          | 0.8598   | 0.9172    |
| 0.0021        | 16.9935 | 1950 | 0.0062          | 0.8555   | 0.9074    |
| 0.0018        | 17.9956 | 2065 | 0.0069          | 0.8598   | 0.9167    |
| 0.0018        | 18.9978 | 2180 | 0.0074          | 0.8556   | 0.9160    |
| 0.002         | 20.0    | 2295 | 0.0067          | 0.8662   | 0.9117    |
| 0.003         | 20.9935 | 2409 | 0.0062          | 0.8724   | 0.9245    |
| 0.0027        | 21.9956 | 2524 | 0.0067          | 0.8727   | 0.9124    |
| 0.0013        | 22.9978 | 2639 | 0.0068          | 0.8684   | 0.9147    |
| 0.0011        | 24.0    | 2754 | 0.0070          | 0.8723   | 0.9165    |
| 0.0014        | 24.9935 | 2868 | 0.0074          | 0.8709   | 0.9257    |
| 0.0011        | 25.9956 | 2983 | 0.0075          | 0.8697   | 0.9139    |
| 0.001         | 26.9978 | 3098 | 0.0071          | 0.8780   | 0.9273    |
| 0.0011        | 28.0    | 3213 | 0.0075          | 0.8743   | 0.9182    |
| 0.0008        | 28.9935 | 3327 | 0.0080          | 0.8744   | 0.9234    |
| 0.0007        | 29.9956 | 3442 | 0.0086          | 0.8692   | 0.9205    |
| 0.001         | 30.9978 | 3557 | 0.0083          | 0.8720   | 0.9145    |
| 0.0009        | 32.0    | 3672 | 0.0084          | 0.8745   | 0.9167    |
| 0.0009        | 32.9935 | 3786 | 0.0084          | 0.8717   | 0.9155    |
| 0.0011        | 33.9956 | 3901 | 0.0084          | 0.8756   | 0.9279    |
| 0.0007        | 34.9978 | 4016 | 0.0090          | 0.8777   | 0.9233    |
| 0.0008        | 36.0    | 4131 | 0.0090          | 0.8744   | 0.9173    |
| 0.0011        | 36.9935 | 4245 | 0.0097          | 0.8753   | 0.9192    |
| 0.0008        | 37.9956 | 4360 | 0.0091          | 0.8757   | 0.9260    |
| 0.0009        | 38.9978 | 4475 | 0.0091          | 0.8739   | 0.9173    |
| 0.0008        | 40.0    | 4590 | 0.0103          | 0.8760   | 0.9274    |
| 0.0008        | 40.9935 | 4704 | 0.0106          | 0.8749   | 0.9263    |
| 0.0008        | 41.9956 | 4819 | 0.0097          | 0.8753   | 0.9238    |
| 0.0009        | 42.9978 | 4934 | 0.0099          | 0.8730   | 0.9159    |
| 0.0006        | 44.0    | 5049 | 0.0101          | 0.8757   | 0.9247    |
| 0.0006        | 44.9935 | 5163 | 0.0104          | 0.8756   | 0.9217    |
| 0.0007        | 45.9956 | 5278 | 0.0106          | 0.8720   | 0.9175    |
| 0.0006        | 46.9978 | 5393 | 0.0107          | 0.8753   | 0.9202    |
| 0.0005        | 48.0    | 5508 | 0.0107          | 0.8757   | 0.9224    |
| 0.0007        | 48.9935 | 5622 | 0.0107          | 0.8764   | 0.9227    |
| 0.0008        | 49.6732 | 5700 | 0.0107          | 0.8767   | 0.9236    |


### Framework versions

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