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# huggingpics-package-demo

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3761
- Acc: 0.9403

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Acc    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0328        | 1.0   | 24   | 0.9442          | 0.7463 |
| 0.8742        | 2.0   | 48   | 0.7099          | 0.9403 |
| 0.6451        | 3.0   | 72   | 0.5050          | 0.9403 |
| 0.508         | 4.0   | 96   | 0.3761          | 0.9403 |


### Framework versions

- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["image-classification", "huggingpics", "generated_from_trainer"], "model-index": [{"name": "huggingpics-package-demo", "results": []}]}
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 

huggingpics-package-demo
========================


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


* Loss: 0.3761
* Acc: 0.9403


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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* 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 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* 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 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Tokenizers 0.10.3