metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: attraction-classifier
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8158995815899581
attraction-classifier
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4121
- Accuracy: 0.8159
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: 32
- eval_batch_size: 32
- seed: 69
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 25
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6839 | 0.89 | 15 | 0.6438 | 0.6757 |
| 0.5555 | 1.78 | 30 | 0.5198 | 0.7364 |
| 0.4995 | 2.67 | 45 | 0.5212 | 0.7469 |
| 0.4177 | 3.56 | 60 | 0.4447 | 0.7866 |
| 0.415 | 4.44 | 75 | 0.4438 | 0.7929 |
| 0.3737 | 5.33 | 90 | 0.4302 | 0.7866 |
| 0.3588 | 6.22 | 105 | 0.4452 | 0.7992 |
| 0.3343 | 7.11 | 120 | 0.4666 | 0.7908 |
| 0.3095 | 8.0 | 135 | 0.4727 | 0.7720 |
| 0.2951 | 8.89 | 150 | 0.4162 | 0.8138 |
| 0.2819 | 9.78 | 165 | 0.4299 | 0.8159 |
| 0.257 | 10.67 | 180 | 0.4497 | 0.8033 |
| 0.2625 | 11.56 | 195 | 0.4642 | 0.7971 |
| 0.2287 | 12.44 | 210 | 0.4121 | 0.8159 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0