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# trashbot

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the mraottth/all_locations_pooled dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0191
- Mean Iou: 0.3997
- Mean Accuracy: 0.7995
- Overall Accuracy: 0.7995
- Accuracy Unlabeled: nan
- Accuracy Trash: 0.7995
- Iou Unlabeled: 0.0
- Iou Trash: 0.7995

## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Trash | Iou Unlabeled | Iou Trash |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:|
| 0.0748        | 1.0   | 90   | 0.0386          | 0.3630   | 0.7259        | 0.7259           | nan                | 0.7259         | 0.0           | 0.7259    |
| 0.039         | 2.0   | 180  | 0.0242          | 0.3803   | 0.7607        | 0.7607           | nan                | 0.7607         | 0.0           | 0.7607    |
| 0.0194        | 3.0   | 270  | 0.0242          | 0.3605   | 0.7210        | 0.7210           | nan                | 0.7210         | 0.0           | 0.7210    |
| 0.0112        | 4.0   | 360  | 0.0205          | 0.3995   | 0.7991        | 0.7991           | nan                | 0.7991         | 0.0           | 0.7991    |
| 0.0169        | 5.0   | 450  | 0.0192          | 0.4000   | 0.8000        | 0.8000           | nan                | 0.8000         | 0.0           | 0.8000    |
| 0.041         | 6.0   | 540  | 0.0196          | 0.3838   | 0.7677        | 0.7677           | nan                | 0.7677         | 0.0           | 0.7677    |
| 0.0188        | 7.0   | 630  | 0.0191          | 0.4139   | 0.8277        | 0.8277           | nan                | 0.8277         | 0.0           | 0.8277    |
| 0.0073        | 8.0   | 720  | 0.0190          | 0.4069   | 0.8138        | 0.8138           | nan                | 0.8138         | 0.0           | 0.8138    |
| 0.025         | 9.0   | 810  | 0.0191          | 0.4087   | 0.8174        | 0.8174           | nan                | 0.8174         | 0.0           | 0.8174    |
| 0.006         | 10.0  | 900  | 0.0191          | 0.3997   | 0.7995        | 0.7995           | nan                | 0.7995         | 0.0           | 0.7995    |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

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

trashbot
========


This model is a fine-tuned version of nvidia/mit-b5 on the mraottth/all\_locations\_pooled dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0191
* Mean Iou: 0.3997
* Mean Accuracy: 0.7995
* Overall Accuracy: 0.7995
* Accuracy Unlabeled: nan
* Accuracy Trash: 0.7995
* Iou Unlabeled: 0.0
* Iou Trash: 0.7995


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: 3
* eval\_batch\_size: 3
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #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: 3
* eval\_batch\_size: 3
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #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: 3
* eval\_batch\_size: 3
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2