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---
library_name: transformers
license: other
base_model: nvidia/segformer-b3-finetuned-cityscapes-1024-1024
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: route_background_semantic
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. -->
# route_background_semantic
This model is a fine-tuned version of [nvidia/segformer-b3-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b3-finetuned-cityscapes-1024-1024) on the Logiroad/route_background_semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2360
- Mean Iou: 0.1916
- Mean Accuracy: 0.2447
- Overall Accuracy: 0.2962
- Accuracy Unlabeled: nan
- Accuracy Découpe: 0.2865
- Accuracy Reflet météo: 0.0
- Accuracy Autre réparation: 0.3437
- Accuracy Glaçage ou ressuage: 0.0386
- Accuracy Emergence: 0.5549
- Iou Unlabeled: 0.0
- Iou Découpe: 0.2515
- Iou Reflet météo: 0.0
- Iou Autre réparation: 0.3230
- Iou Glaçage ou ressuage: 0.0369
- Iou Emergence: 0.5379
## 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: 4
- eval_batch_size: 4
- seed: 1337
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: polynomial
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Découpe | Accuracy Reflet météo | Accuracy Autre réparation | Accuracy Glaçage ou ressuage | Accuracy Emergence | Iou Unlabeled | Iou Découpe | Iou Reflet météo | Iou Autre réparation | Iou Glaçage ou ressuage | Iou Emergence |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:---------------------:|:-------------------------:|:----------------------------:|:------------------:|:-------------:|:-----------:|:----------------:|:--------------------:|:-----------------------:|:-------------:|
| 0.2715 | 1.0 | 2427 | 0.2682 | 0.0521 | 0.0669 | 0.1828 | nan | 0.0813 | 0.0 | 0.2533 | 0.0 | 0.0 | 0.0 | 0.0766 | 0.0 | 0.2362 | 0.0 | 0.0 |
| 0.2815 | 2.0 | 4854 | 0.2682 | 0.1165 | 0.1436 | 0.1593 | nan | 0.1108 | 0.0 | 0.1982 | 0.0 | 0.4090 | 0.0 | 0.1014 | 0.0 | 0.1916 | 0.0 | 0.4057 |
| 0.2638 | 3.0 | 7281 | 0.2420 | 0.1664 | 0.2100 | 0.2564 | nan | 0.2346 | 0.0 | 0.3039 | 0.0030 | 0.5085 | 0.0 | 0.2128 | 0.0 | 0.2854 | 0.0030 | 0.4973 |
| 0.2703 | 4.0 | 9708 | 0.2333 | 0.1941 | 0.2475 | 0.3074 | nan | 0.2843 | 0.0 | 0.3612 | 0.0446 | 0.5473 | 0.0 | 0.2512 | 0.0 | 0.3383 | 0.0429 | 0.5320 |
| 0.2197 | 4.1203 | 10000 | 0.2360 | 0.1916 | 0.2447 | 0.2962 | nan | 0.2865 | 0.0 | 0.3437 | 0.0386 | 0.5549 | 0.0 | 0.2515 | 0.0 | 0.3230 | 0.0369 | 0.5379 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
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