metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
base_model: microsoft/beit-base-patch16-224
model-index:
- name: BEiT-finetuned
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: cifar10
type: cifar10
args: plain_text
metrics:
- type: accuracy
value: 0.9918
name: Accuracy
BEiT-finetuned
This model is a fine-tuned version of microsoft/beit-base-patch16-224 on the cifar10 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0256
- Accuracy: 0.9918
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|---|---|---|---|---|
| 0.3296 | 1.0 | 351 | 0.0492 | 0.9862 |
| 0.2353 | 2.0 | 702 | 0.0331 | 0.9894 |
| 0.2127 | 3.0 | 1053 | 0.0256 | 0.9918 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1