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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: my_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: 0.939
---
<!-- 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_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: 0.3194
- Accuracy: 0.939
## 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: 5e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8638 | 0.99 | 62 | 0.9578 | 0.913 |
| 0.6163 | 2.0 | 125 | 0.7060 | 0.911 |
| 0.5103 | 2.99 | 187 | 0.4994 | 0.936 |
| 0.3659 | 4.0 | 250 | 0.4539 | 0.927 |
| 0.3207 | 4.99 | 312 | 0.3999 | 0.933 |
| 0.2523 | 6.0 | 375 | 0.3799 | 0.921 |
| 0.2257 | 6.99 | 437 | 0.3703 | 0.922 |
| 0.1937 | 8.0 | 500 | 0.3160 | 0.936 |
| 0.1854 | 8.99 | 562 | 0.3229 | 0.93 |
| 0.2048 | 9.92 | 620 | 0.3194 | 0.939 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|