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# segformer-finetuned-lane-1k-steps

This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-cityscapes-512-1024](https://huggingface.co/nvidia/segformer-b0-finetuned-cityscapes-512-1024) on the Efferbach/lane_master dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0548
- Mean Iou: 0.0708
- Mean Accuracy: 0.1236
- Overall Accuracy: 0.1217
- Accuracy Background: nan
- Accuracy Left: 0.1893
- Accuracy Right: 0.0578
- Iou Background: 0.0
- Iou Left: 0.1581
- Iou Right: 0.0544

## 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Left | Accuracy Right | Iou Background | Iou Left | Iou Right |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:|
| 0.1           | 1.0   | 308  | 0.0862          | 0.0008   | 0.0013        | 0.0012           | nan                 | 0.0025        | 0.0            | 0.0            | 0.0025   | 0.0       |
| 0.0596        | 2.0   | 616  | 0.0597          | 0.0712   | 0.1126        | 0.1132           | nan                 | 0.0940        | 0.1313         | 0.0            | 0.0907   | 0.1228    |
| 0.0506        | 3.0   | 924  | 0.0551          | 0.0682   | 0.1171        | 0.1152           | nan                 | 0.1805        | 0.0536         | 0.0            | 0.1539   | 0.0508    |
| 0.0494        | 3.25  | 1000 | 0.0548          | 0.0708   | 0.1236        | 0.1217           | nan                 | 0.1893        | 0.0578         | 0.0            | 0.1581   | 0.0544    |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "other", "tags": ["image-segmentation", "vision", "generated_from_trainer"], "model-index": [{"name": "segformer-finetuned-lane-1k-steps", "results": []}]}
transformers pytorch tensorboard segformer image-segmentation vision generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #license-other #endpoints_compatible #region-us 

segformer-finetuned-lane-1k-steps
=================================


This model is a fine-tuned version of nvidia/segformer-b0-finetuned-cityscapes-512-1024 on the Efferbach/lane\_master dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0548
* Mean Iou: 0.0708
* Mean Accuracy: 0.1236
* Overall Accuracy: 0.1217
* Accuracy Background: nan
* Accuracy Left: 0.1893
* Accuracy Right: 0.0578
* Iou Background: 0.0
* Iou Left: 0.1581
* Iou Right: 0.0544


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: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 1000


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 1000 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #license-other #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 1000 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #license-other #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 1000### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3