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# finetuned-ViT-Indian-Food-Classification-v1

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 Human_Action_Recognition dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2665
- Accuracy: 0.9341

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2019        | 0.3   | 100  | 0.9317          | 0.8555   |
| 0.6664        | 0.6   | 200  | 0.5432          | 0.8959   |
| 0.5096        | 0.9   | 300  | 0.4700          | 0.8990   |
| 0.6116        | 1.2   | 400  | 0.4504          | 0.8799   |
| 0.4326        | 1.5   | 500  | 0.3856          | 0.8980   |
| 0.3349        | 1.8   | 600  | 0.3471          | 0.9129   |
| 0.5141        | 2.1   | 700  | 0.3708          | 0.9033   |
| 0.32          | 2.4   | 800  | 0.3338          | 0.9139   |
| 0.2611        | 2.7   | 900  | 0.3159          | 0.9171   |
| 0.1836        | 3.0   | 1000 | 0.2696          | 0.9299   |
| 0.2492        | 3.3   | 1100 | 0.2979          | 0.9214   |
| 0.1846        | 3.6   | 1200 | 0.3165          | 0.9203   |
| 0.1505        | 3.9   | 1300 | 0.2806          | 0.9288   |
| 0.1854        | 4.2   | 1400 | 0.2665          | 0.9341   |
| 0.124         | 4.5   | 1500 | 0.2695          | 0.9341   |
| 0.0719        | 4.8   | 1600 | 0.2668          | 0.9320   |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "finetuned-ViT-Indian-Food-Classification-v1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "Human_Action_Recognition", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9341126461211477, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

finetuned-ViT-Indian-Food-Classification-v1
===========================================


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


* Loss: 0.2665
* Accuracy: 0.9341


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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1