metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: cifar
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
config: plain_text
split: train[:5000]
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.883
cifar
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4714
- Accuracy: 0.883
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 |
|---|---|---|---|---|
| 1.7956 | 0.99 | 62 | 1.6395 | 0.817 |
| 0.8981 | 2.0 | 125 | 0.8510 | 0.858 |
| 0.6049 | 2.99 | 187 | 0.6666 | 0.878 |
| 0.5427 | 4.0 | 250 | 0.5796 | 0.88 |
| 0.4318 | 4.99 | 312 | 0.5110 | 0.889 |
| 0.3952 | 6.0 | 375 | 0.4339 | 0.907 |
| 0.3544 | 6.99 | 437 | 0.4432 | 0.902 |
| 0.3612 | 8.0 | 500 | 0.4213 | 0.898 |
| 0.3522 | 8.99 | 562 | 0.4474 | 0.884 |
| 0.3096 | 9.92 | 620 | 0.4714 | 0.883 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3