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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: vit-base-patch16-224-in21k-finetuned-lora-food101
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: food101
type: food101
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.855973597359736
name: Accuracy
---
<!-- 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-base-patch16-224-in21k-finetuned-lora-food101
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: 0.5152
- Accuracy: 0.8560
## 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: 0.005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8353 | 1.0 | 133 | 0.6692 | 0.8168 |
| 0.702 | 2.0 | 266 | 0.5892 | 0.8393 |
| 0.6419 | 2.99 | 399 | 0.5615 | 0.8455 |
| 0.5742 | 4.0 | 533 | 0.5297 | 0.8535 |
| 0.4942 | 4.99 | 665 | 0.5152 | 0.8560 |
### Framework versions
- PEFT 0.5.0.dev0
- Transformers 4.32.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
[notebook](https://github.com/andysingal/CV_public/blob/main/Image-classification/notebooks/image_classification_peft_lora.ipynb) |