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---
license: apache-2.0
tags:
- generated_from_trainer
- image-classification
- pytorch
datasets:
- cats_vs_dogs
metrics:
- accuracy
model-index:
- name: vit-base-cats-vs-dogs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cats_vs_dogs
type: cats_vs_dogs
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9934510250569476
---
<!-- 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-cats-vs-dogs
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 cats_vs_dogs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0202
- Accuracy: 0.9935
## 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: 64
- eval_batch_size: 64
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.064 | 1.0 | 311 | 0.0483 | 0.9849 |
| 0.0622 | 2.0 | 622 | 0.0275 | 0.9903 |
| 0.0366 | 3.0 | 933 | 0.0262 | 0.9917 |
| 0.0294 | 4.0 | 1244 | 0.0219 | 0.9932 |
| 0.0161 | 5.0 | 1555 | 0.0202 | 0.9935 |
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.1.dev0
- Tokenizers 0.10.3
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