
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ## labels
- 0: Object
- 1: Recycle
- 2: Non-Recycle 


# vit-base-patch16-224

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1510
- Accuracy: 0.9443

## 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: 5e-05
- train_batch_size: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1438        | 1.0   | 150  | 0.1645          | 0.9353   |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224", "results": []}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

## labels
=========


* 0: Object
* 1: Recycle
* 2: Non-Recycle


vit-base-patch16-224
====================


This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1510
* Accuracy: 0.9443


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: 5e-05
* train\_batch\_size: 60
* eval\_batch\_size: 60
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 240
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3



## labels
=========


* 0: Object
* 1: Recycle
* 2: Non-Recycle


vit-base-patch16-224
====================


This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1510
* Accuracy: 0.9443


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: 5e-05
* train\_batch\_size: 60
* eval\_batch\_size: 60
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 240
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## labels
=========


* 0: Object
* 1: Recycle
* 2: Non-Recycle


vit-base-patch16-224
====================


This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1510
* Accuracy: 0.9443


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: 5e-05
* train\_batch\_size: 60
* eval\_batch\_size: 60
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 240
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
## labels
=========


* 0: Object
* 1: Recycle
* 2: Non-Recycle


vit-base-patch16-224
====================


This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1510
* Accuracy: 0.9443


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: 5e-05
* train\_batch\_size: 60
* eval\_batch\_size: 60
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 240
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 1### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3