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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: 0.0189
- Mean Iou: 0.4050
- Mean Accuracy: 0.8101
- Overall Accuracy: 0.8101
- Accuracy Unlabeled: nan
- Accuracy Trash: 0.8101
- Iou Unlabeled: 0.0
- Iou Trash: 0.8101

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:|
| 0.0592        | 1.0   | 90   | 0.0387          | 0.3723   | 0.7446        | 0.7446           | nan                | 0.7446         | 0.0           | 0.7446    |
| 0.0402        | 2.0   | 180  | 0.0281          | 0.4123   | 0.8247        | 0.8247           | nan                | 0.8247         | 0.0           | 0.8247    |
| 0.0209        | 3.0   | 270  | 0.0246          | 0.3691   | 0.7382        | 0.7382           | nan                | 0.7382         | 0.0           | 0.7382    |
| 0.0117        | 4.0   | 360  | 0.0210          | 0.3882   | 0.7763        | 0.7763           | nan                | 0.7763         | 0.0           | 0.7763    |
| 0.019         | 5.0   | 450  | 0.0198          | 0.3822   | 0.7644        | 0.7644           | nan                | 0.7644         | 0.0           | 0.7644    |
| 0.0445        | 6.0   | 540  | 0.0199          | 0.3771   | 0.7542        | 0.7542           | nan                | 0.7542         | 0.0           | 0.7542    |
| 0.0195        | 7.0   | 630  | 0.0191          | 0.4177   | 0.8354        | 0.8354           | nan                | 0.8354         | 0.0           | 0.8354    |
| 0.008         | 8.0   | 720  | 0.0191          | 0.4060   | 0.8119        | 0.8119           | nan                | 0.8119         | 0.0           | 0.8119    |
| 0.0268        | 9.0   | 810  | 0.0188          | 0.4083   | 0.8166        | 0.8166           | nan                | 0.8166         | 0.0           | 0.8166    |
| 0.0061        | 10.0  | 900  | 0.0189          | 0.4050   | 0.8101        | 0.8101           | nan                | 0.8101         | 0.0           | 0.8101    |


### 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: 0.0189
* Mean Iou: 0.4050
* Mean Accuracy: 0.8101
* Overall Accuracy: 0.8101
* Accuracy Unlabeled: nan
* Accuracy Trash: 0.8101
* Iou Unlabeled: 0.0
* Iou Trash: 0.8101


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