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# segformer-b0-drivable-area_segmentation

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2781
- Mean Iou: 0.3551
- Mean Accuracy: 0.4409
- Overall Accuracy: 0.9059
- Per Category Iou: [0.16086782865321247, 0.904459670467452, 0.0]
- Per Category Accuracy: [0.39100126742712293, 0.9316527965687352, 0.0]

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                              | Per Category Accuracy                          |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------:|:----------------------------------------------:|
| 0.0717        | 2.44  | 100  | 0.3113          | 0.3302   | 0.3801        | 0.9033           | [0.08799146351473365, 0.902610436143179, 0.0] | [0.2023568751179786, 0.9379215030926394, 0.0]  |
| 0.0079        | 4.88  | 200  | 0.2781          | 0.3551   | 0.4409        | 0.9059           | [0.16086782865321247, 0.904459670467452, 0.0] | [0.39100126742712293, 0.9316527965687352, 0.0] |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "segformer-b0-drivable-area_segmentation", "results": []}]}
transformers pytorch tensorboard segformer generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #license-other #endpoints_compatible #region-us 

segformer-b0-drivable-area\_segmentation
========================================


This model is a fine-tuned version of nvidia/mit-b0 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2781
* Mean Iou: 0.3551
* Mean Accuracy: 0.4409
* Overall Accuracy: 0.9059
* Per Category Iou: [0.16086782865321247, 0.904459670467452, 0.0]
* Per Category Accuracy: [0.39100126742712293, 0.9316527965687352, 0.0]


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: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #license-other #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2