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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-arsenic
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: indian_food_images
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9993451211525868
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-arsenic
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 indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0048
- Accuracy: 0.9993
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1855 | 0.1848 | 100 | 0.1918 | 0.9312 |
| 0.1792 | 0.3697 | 200 | 0.1740 | 0.9365 |
| 0.1688 | 0.5545 | 300 | 0.0782 | 0.9692 |
| 0.1238 | 0.7394 | 400 | 0.2158 | 0.9227 |
| 0.0969 | 0.9242 | 500 | 0.0449 | 0.9843 |
| 0.0326 | 1.1091 | 600 | 0.1554 | 0.9574 |
| 0.1057 | 1.2939 | 700 | 0.0845 | 0.9738 |
| 0.0805 | 1.4787 | 800 | 0.0712 | 0.9823 |
| 0.0889 | 1.6636 | 900 | 0.0718 | 0.9797 |
| 0.0503 | 1.8484 | 1000 | 0.0251 | 0.9935 |
| 0.0225 | 2.0333 | 1100 | 0.0177 | 0.9967 |
| 0.0049 | 2.2181 | 1200 | 0.0246 | 0.9921 |
| 0.0152 | 2.4030 | 1300 | 0.0083 | 0.9987 |
| 0.08 | 2.5878 | 1400 | 0.0214 | 0.9941 |
| 0.0043 | 2.7726 | 1500 | 0.0069 | 0.9980 |
| 0.0501 | 2.9575 | 1600 | 0.0151 | 0.9967 |
| 0.0186 | 3.1423 | 1700 | 0.0078 | 0.9974 |
| 0.0033 | 3.3272 | 1800 | 0.0139 | 0.9961 |
| 0.0023 | 3.5120 | 1900 | 0.0076 | 0.9987 |
| 0.0054 | 3.6969 | 2000 | 0.0048 | 0.9993 |
| 0.0168 | 3.8817 | 2100 | 0.0066 | 0.9987 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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