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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-YB
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8219685282320272
---
<!-- 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-YB
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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3922
- Accuracy: 0.8220
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5973 | 0.49 | 100 | 0.4747 | 0.7797 |
| 0.4672 | 0.99 | 200 | 0.4363 | 0.7979 |
| 0.3914 | 1.48 | 300 | 0.4090 | 0.8115 |
| 0.3749 | 1.97 | 400 | 0.4001 | 0.8189 |
| 0.3281 | 2.47 | 500 | 0.4023 | 0.8183 |
| 0.3187 | 2.96 | 600 | 0.3922 | 0.8220 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.0