---
license: apache-2.0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-pavement
  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. -->

# segformer-b0-pavement

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the reannayang/FL_pavement dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4558
- Mean Iou: 0.6310
- Mean Accuracy: 0.7758
- Overall Accuracy: 0.9687
- Per Category Iou: [0.0, 0.9582107718835582, 0.9831802335937301, 0.0, 0.9070478290281362, 0.9376628700260592]
- Per Category Accuracy: [nan, 0.964865692983352, 0.9920279343235298, 0.0, 0.9513585956798234, 0.9709244156080405]

## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                           | Per Category Accuracy                                                                      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|
| 1.0751        | 10.0  | 20   | 1.1494          | 0.5858   | 0.7574        | 0.9436           | [0.0, 0.9446293533289976, 0.9477727359877293, 0.0, 0.7979866954491387, 0.8246812143200128] | [nan, 0.9457026861403329, 0.951885181714315, 0.0, 0.9519892783938614, 0.93727071521693]    |
| 0.8732        | 20.0  | 40   | 0.6625          | 0.6209   | 0.7710        | 0.9635           | [0.0, 0.9549288283268325, 0.9793625315294657, 0.0, 0.8879129036248917, 0.903449846293977]  | [nan, 0.9626825656214891, 0.9869186233102687, 0.0, 0.9430020497188206, 0.9623139192917564] |
| 0.4736        | 30.0  | 60   | 0.5124          | 0.6302   | 0.7753        | 0.9687           | [0.0, 0.9600678403726158, 0.9813250559739467, 0.0, 0.9086066088704611, 0.9311320479537963] | [nan, 0.968414870799714, 0.990282039102918, 0.0, 0.9501760655910022, 0.9678319134099385]   |
| 0.503         | 40.0  | 80   | 0.4726          | 0.6305   | 0.7753        | 0.9680           | [0.0, 0.9574198453934862, 0.9821685397548652, 0.0, 0.9074944966980188, 0.9358034824412265] | [nan, 0.9645337554897355, 0.9906414881189263, 0.0, 0.953329479161192, 0.9678925507079404]  |
| 0.6762        | 50.0  | 100  | 0.4558          | 0.6310   | 0.7758        | 0.9687           | [0.0, 0.9582107718835582, 0.9831802335937301, 0.0, 0.9070478290281362, 0.9376628700260592] | [nan, 0.964865692983352, 0.9920279343235298, 0.0, 0.9513585956798234, 0.9709244156080405]  |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1