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# condition-Kitchen

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1685
- Rmse: 1.0810

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rmse   |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 101  | 1.2101          | 1.1001 |
| No log        | 2.0   | 202  | 0.7917          | 0.8898 |
| No log        | 3.0   | 303  | 1.0430          | 1.0213 |
| No log        | 4.0   | 404  | 0.9290          | 0.9639 |
| 2.8472        | 5.0   | 505  | 0.6923          | 0.8321 |
| 2.8472        | 6.0   | 606  | 0.8079          | 0.8989 |
| 2.8472        | 7.0   | 707  | 0.7159          | 0.8461 |
| 2.8472        | 8.0   | 808  | 0.6934          | 0.8327 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "condition-Kitchen", "results": []}]}
transformers pytorch swinv2 image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #swinv2 #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

condition-Kitchen
=================


This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.1685
* Rmse: 1.0810


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: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.0
* Tokenizers 0.13.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.0
* Tokenizers 0.13.0
TAGS
#transformers #pytorch #swinv2 #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.0
* Tokenizers 0.13.0
TAGS
#transformers #pytorch #swinv2 #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 8### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.0
* Tokenizers 0.13.0