Vit-Cifar100 / README.md
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---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- cifar100
metrics:
- accuracy
model-index:
- name: vit-base-beans-demo-v5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Cifar100
type: cifar100
args: cifar100
metrics:
- name: Accuracy
type: accuracy
value: 0.8985
---
<!-- 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-beans-demo-v5
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 Cifar100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4420
- Accuracy: 0.8985
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.08 | 1.0 | 3125 | 0.6196 | 0.8262 |
| 0.3816 | 2.0 | 6250 | 0.5322 | 0.8555 |
| 0.1619 | 3.0 | 9375 | 0.4817 | 0.8765 |
| 0.0443 | 4.0 | 12500 | 0.4420 | 0.8985 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1