metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar100
metrics:
- accuracy
model-index:
- name: swin-base-finetuned-cifar100
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar100
type: cifar100
config: cifar100
split: train
args: cifar100
metrics:
- name: Accuracy
type: accuracy
value: 0.9201
swin-base-finetuned-cifar100
This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the cifar100 dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.9201
- Loss: 0.3670
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: 4e-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: 5
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.3536 | 1.0 | 781 | 0.9052 | 0.3141 |
| 0.3254 | 2.0 | 1562 | 0.9117 | 0.2991 |
| 0.0936 | 3.0 | 2343 | 0.9138 | 0.3322 |
| 0.1054 | 4.0 | 3124 | 0.9158 | 0.3483 |
| 0.0269 | 5.0 | 3905 | 0.9201 | 0.3670 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2