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# nateraw/food

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 nateraw/food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4501
- Accuracy: 0.8913

## 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: 128
- eval_batch_size: 128
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8271        | 1.0   | 592  | 0.6070          | 0.8562   |
| 0.4376        | 2.0   | 1184 | 0.4947          | 0.8691   |
| 0.2089        | 3.0   | 1776 | 0.4876          | 0.8747   |
| 0.0882        | 4.0   | 2368 | 0.4639          | 0.8857   |
| 0.0452        | 5.0   | 2960 | 0.4501          | 0.8913   |


### Framework versions

- Transformers 4.9.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 1.9.1.dev0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer", "image-classification", "pytorch"], "datasets": ["food101"], "metrics": ["accuracy"], "model-index": [{"name": "food101_outputs", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "food-101", "type": "food101", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8912871287128713, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:food101 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-food101 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

nateraw/food
============


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the nateraw/food101 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4501
* Accuracy: 0.8913


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: 128
* eval\_batch\_size: 128
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.9.0.dev0
* Pytorch 1.9.0+cu102
* Datasets 1.9.1.dev0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.9.0.dev0
* Pytorch 1.9.0+cu102
* Datasets 1.9.1.dev0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-food101 #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: 128
* eval\_batch\_size: 128
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.9.0.dev0
* Pytorch 1.9.0+cu102
* Datasets 1.9.1.dev0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-food101 #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: 128
* eval\_batch\_size: 128
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.9.0.dev0
* Pytorch 1.9.0+cu102
* Datasets 1.9.1.dev0
* Tokenizers 0.10.3