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: 0.7733050847457628
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: 0.5835
- Accuracy: 0.7733
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: 0.05
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5128 | 1.13 | 150 | 0.5352 | 0.7331 |
| 0.4228 | 2.26 | 300 | 0.4504 | 0.7775 |
| 0.3536 | 3.38 | 450 | 0.5563 | 0.7542 |
| 0.3233 | 4.51 | 600 | 0.5737 | 0.7797 |
| 0.324 | 5.64 | 750 | 0.4690 | 0.8030 |
| 0.2677 | 6.77 | 900 | 0.5285 | 0.7669 |
| 0.2755 | 7.89 | 1050 | 0.4705 | 0.7797 |
| 0.1914 | 9.02 | 1200 | 0.5835 | 0.7733 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0