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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9
---
<!-- 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. -->
# my_awesome_food_model
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: 0.8834
- Accuracy: 0.9
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.6073 | 0.99 | 62 | 3.3725 | 0.818 |
| 2.2956 | 2.0 | 125 | 2.1579 | 0.854 |
| 1.7042 | 2.99 | 187 | 1.6201 | 0.887 |
| 1.3278 | 4.0 | 250 | 1.3513 | 0.89 |
| 1.1314 | 4.99 | 312 | 1.1549 | 0.908 |
| 1.007 | 6.0 | 375 | 1.0737 | 0.889 |
| 0.905 | 6.99 | 437 | 0.9600 | 0.906 |
| 0.8227 | 8.0 | 500 | 0.9113 | 0.912 |
| 0.7948 | 8.99 | 562 | 0.8908 | 0.909 |
| 0.7598 | 9.92 | 620 | 0.8834 | 0.9 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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