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# planes-trains-automobiles

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the huggingpics dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0534
- Accuracy: 0.9851


## Model description

Autogenerated by HuggingPics🤗🖼️

Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).

Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images


#### automobiles

![automobiles](images/automobiles.jpg)

#### planes

![planes](images/planes.jpg)

#### trains

![trains](images/trains.jpg)


## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0283        | 1.0   | 48   | 0.0434          | 0.9851   |
| 0.0224        | 2.0   | 96   | 0.0548          | 0.9851   |
| 0.0203        | 3.0   | 144  | 0.0445          | 0.9851   |
| 0.0195        | 4.0   | 192  | 0.0534          | 0.9851   |


### Framework versions

- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["huggingpics", "image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model_index": [{"name": "planes-trains-automobiles", "results": [{"task": {"name": "Image Classification", "type": "image-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9850746268656716}}]}]}
transformers pytorch tensorboard vit image-classification huggingpics generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

planes-trains-automobiles
=========================


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the huggingpics dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0534
* Accuracy: 0.9851


Model description
-----------------


Autogenerated by HuggingPics️


Create your own image classifier for anything by running the demo on Google Colab.


Report any issues with the demo at the github repo.


Example Images
--------------


#### automobiles


!automobiles


#### planes


!planes


#### trains


!trains


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: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3



#### automobiles


!automobiles #### planes


!planes #### trains


!trains


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: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 #### automobiles


!automobiles #### planes


!planes #### trains


!trains


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: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
#### automobiles


!automobiles#### planes


!planes#### trains


!trains


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: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3