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: 0.8187772925764192
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: 0.5072
- Accuracy: 0.8188
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4952 | 1.16 | 150 | 0.5743 | 0.7009 |
| 0.4475 | 2.33 | 300 | 0.4772 | 0.7729 |
| 0.4287 | 3.49 | 450 | 0.4688 | 0.7642 |
| 0.2978 | 4.65 | 600 | 0.5202 | 0.7707 |
| 0.3176 | 5.81 | 750 | 0.4475 | 0.7926 |
| 0.2602 | 6.98 | 900 | 0.4760 | 0.7882 |
| 0.2526 | 8.14 | 1050 | 0.4766 | 0.8188 |
| 0.1601 | 9.3 | 1200 | 0.4925 | 0.8122 |
| 0.1925 | 10.47 | 1350 | 0.5308 | 0.8079 |
| 0.1343 | 11.63 | 1500 | 0.5072 | 0.8188 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0