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# segformer-v-mesh-0

This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the Onegafer/vehicle_segmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0360
- Mean Iou: 0.4403
- Mean Accuracy: 0.8806
- Overall Accuracy: 0.8806
- Accuracy Background: nan
- Accuracy Windows: 0.8806
- Iou Background: 0.0
- Iou Windows: 0.8806

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Windows | Iou Background | Iou Windows |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:|
| 0.2932        | 0.16  | 20   | 0.3269          | 0.2578   | 0.5156        | 0.5156           | nan                 | 0.5156           | 0.0            | 0.5156      |
| 0.1417        | 0.31  | 40   | 0.1235          | 0.3790   | 0.7580        | 0.7580           | nan                 | 0.7580           | 0.0            | 0.7580      |
| 0.0952        | 0.47  | 60   | 0.1245          | 0.4606   | 0.9211        | 0.9211           | nan                 | 0.9211           | 0.0            | 0.9211      |
| 0.0778        | 0.62  | 80   | 0.0628          | 0.4042   | 0.8084        | 0.8084           | nan                 | 0.8084           | 0.0            | 0.8084      |
| 0.0448        | 0.78  | 100  | 0.0512          | 0.4161   | 0.8322        | 0.8322           | nan                 | 0.8322           | 0.0            | 0.8322      |
| 0.0323        | 0.94  | 120  | 0.0435          | 0.4167   | 0.8334        | 0.8334           | nan                 | 0.8334           | 0.0            | 0.8334      |
| 0.0337        | 1.09  | 140  | 0.0405          | 0.4131   | 0.8262        | 0.8262           | nan                 | 0.8262           | 0.0            | 0.8262      |
| 0.0586        | 1.25  | 160  | 0.0409          | 0.4509   | 0.9017        | 0.9017           | nan                 | 0.9017           | 0.0            | 0.9017      |
| 0.0591        | 1.41  | 180  | 0.0404          | 0.4310   | 0.8620        | 0.8620           | nan                 | 0.8620           | 0.0            | 0.8620      |
| 0.0953        | 1.56  | 200  | 0.0386          | 0.4366   | 0.8732        | 0.8732           | nan                 | 0.8732           | 0.0            | 0.8732      |
| 0.0607        | 1.72  | 220  | 0.0374          | 0.4414   | 0.8828        | 0.8828           | nan                 | 0.8828           | 0.0            | 0.8828      |
| 0.0387        | 1.88  | 240  | 0.0360          | 0.4403   | 0.8806        | 0.8806           | nan                 | 0.8806           | 0.0            | 0.8806      |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "base_model": "nvidia/mit-b2", "model-index": [{"name": "segformer-v-mesh-0", "results": []}]}
transformers pytorch safetensors segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b2 license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b2 #license-other #endpoints_compatible #region-us 

segformer-v-mesh-0
==================


This model is a fine-tuned version of nvidia/mit-b2 on the Onegafer/vehicle\_segmentation dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0360
* Mean Iou: 0.4403
* Mean Accuracy: 0.8806
* Overall Accuracy: 0.8806
* Accuracy Background: nan
* Accuracy Windows: 0.8806
* Iou Background: 0.0
* Iou Windows: 0.8806


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: 2


### Training results



### Framework versions


* Transformers 4.28.1
* 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: 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: 2 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b2 #license-other #endpoints_compatible #region-us 
 ### 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: 2 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b2 #license-other #endpoints_compatible #region-us 
### 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: 2### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3