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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-invitrace-food
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9684
---
<!-- 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. -->
# vit-invitrace-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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1286
- Accuracy: 0.9684
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6128 | 0.2132 | 100 | 0.4694 | 0.9044 |
| 0.3905 | 0.4264 | 200 | 0.5236 | 0.8484 |
| 0.4315 | 0.6397 | 300 | 0.3988 | 0.8884 |
| 0.4028 | 0.8529 | 400 | 0.2213 | 0.9432 |
| 0.1097 | 1.0661 | 500 | 0.2963 | 0.92 |
| 0.1883 | 1.2793 | 600 | 0.2047 | 0.9448 |
| 0.137 | 1.4925 | 700 | 0.1695 | 0.9548 |
| 0.2309 | 1.7058 | 800 | 0.2159 | 0.9384 |
| 0.094 | 1.9190 | 900 | 0.1987 | 0.9452 |
| 0.0282 | 2.1322 | 1000 | 0.1861 | 0.9528 |
| 0.0231 | 2.3454 | 1100 | 0.1944 | 0.9476 |
| 0.0409 | 2.5586 | 1200 | 0.1625 | 0.96 |
| 0.0386 | 2.7719 | 1300 | 0.1486 | 0.9616 |
| 0.0249 | 2.9851 | 1400 | 0.1736 | 0.9572 |
| 0.012 | 3.1983 | 1500 | 0.1469 | 0.9624 |
| 0.0304 | 3.4115 | 1600 | 0.1405 | 0.9644 |
| 0.0052 | 3.6247 | 1700 | 0.1498 | 0.9636 |
| 0.0247 | 3.8380 | 1800 | 0.1286 | 0.9684 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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