metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-rotated-dungeons-v103
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: rotated_maps
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8333333333333334
vit-base-patch16-224-rotated-dungeons-v103
This model is a fine-tuned version of google/vit-base-patch16-224 on the rotated_maps dataset. It achieves the following results on the evaluation set:
- Loss: 0.8291
- Accuracy: 0.8333
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.522 | 3.3333 | 20 | 0.8489 | 0.6667 |
| 0.0346 | 6.6667 | 40 | 2.3103 | 0.6667 |
| 0.019 | 10.0 | 60 | 1.4623 | 0.75 |
| 0.017 | 13.3333 | 80 | 0.8291 | 0.8333 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1