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---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- snacks
metrics:
- accuracy
model-index:
- name: vit-snacks
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Matthijs/snacks
type: snacks
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9392670157068063
---
<!-- 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-snacks
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 Matthijs/snacks dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2754
- Accuracy: 0.9393
## Model description
upload any image of your fave yummy snack
## Intended uses & limitations
there are only 20 different varieties of snacks
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8724 | 0.33 | 100 | 0.9118 | 0.8670 |
| 0.5628 | 0.66 | 200 | 0.6873 | 0.8471 |
| 0.4421 | 0.99 | 300 | 0.4995 | 0.8691 |
| 0.2837 | 1.32 | 400 | 0.4008 | 0.9026 |
| 0.1645 | 1.65 | 500 | 0.3702 | 0.9058 |
| 0.1604 | 1.98 | 600 | 0.3981 | 0.8921 |
| 0.0498 | 2.31 | 700 | 0.3185 | 0.9204 |
| 0.0406 | 2.64 | 800 | 0.3427 | 0.9141 |
| 0.1049 | 2.97 | 900 | 0.3444 | 0.9173 |
| 0.0272 | 3.3 | 1000 | 0.3168 | 0.9246 |
| 0.0186 | 3.63 | 1100 | 0.3142 | 0.9288 |
| 0.0203 | 3.96 | 1200 | 0.2931 | 0.9298 |
| 0.007 | 4.29 | 1300 | 0.2754 | 0.9393 |
| 0.0072 | 4.62 | 1400 | 0.2778 | 0.9403 |
| 0.0073 | 4.95 | 1500 | 0.2782 | 0.9393 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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