vit-base-food101 / README.md
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: vit-base-food101-demo-v5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8539405940594059
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-food101-demo-v5
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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5493
- Accuracy: 0.8539
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.657 | 1.0 | 4735 | 0.9732 | 0.7459 |
| 0.9869 | 2.0 | 9470 | 0.7987 | 0.7884 |
| 0.71 | 3.0 | 14205 | 0.6364 | 0.8311 |
| 0.4961 | 4.0 | 18940 | 0.5595 | 0.8487 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1