
# sayakpaul/mit-b0-finetuned-pets

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the [Oxford Pets](https://www.robots.ox.ac.uk/~vgg/data/pets/) dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1481
- Validation Loss: 0.1962
- Epoch: 9

## 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:
- optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.2821     | 0.2146          | 0     |
| 0.2090     | 0.1983          | 1     |
| 0.1920     | 0.2002          | 2     |
| 0.1805     | 0.1868          | 3     |
| 0.1716     | 0.1920          | 4     |
| 0.1651     | 0.1850          | 5     |
| 0.1537     | 0.1943          | 6     |
| 0.1570     | 0.1842          | 7     |
| 0.1462     | 0.1833          | 8     |
| 0.1481     | 0.1962          | 9     |


### Framework versions

- Transformers 4.25.1
- TensorFlow 2.10.1
- Tokenizers 0.13.2
{"license": "other", "library_name": "keras", "tags": ["vision", "image-segmentation"], "datasets": ["oxford_pets"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "Dog"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg", "example_title": "Cat"}]}
keras tf segformer vision image-segmentation dataset:oxford_pets license:other region:us
TAGS
#keras #tf #segformer #vision #image-segmentation #dataset-oxford_pets #license-other #region-us 

sayakpaul/mit-b0-finetuned-pets
===============================


This model is a fine-tuned version of nvidia/mit-b0 on the Oxford Pets dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.1481
* Validation Loss: 0.1962
* Epoch: 9


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:


* optimizer: {'name': 'Adam', 'learning\_rate': 6e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.25.1
* TensorFlow 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 6e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.25.1
* TensorFlow 2.10.1
* Tokenizers 0.13.2
TAGS
#keras #tf #segformer #vision #image-segmentation #dataset-oxford_pets #license-other #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 6e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.25.1
* TensorFlow 2.10.1
* Tokenizers 0.13.2
TAGS
#keras #tf #segformer #vision #image-segmentation #dataset-oxford_pets #license-other #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 6e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.25.1
* TensorFlow 2.10.1
* Tokenizers 0.13.2