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: 0.8114406779661016
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: 0.4385
- Accuracy: 0.8114
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 |
|---|---|---|---|---|
| 0.65 | 1.5 | 25 | 0.5581 | 0.7394 |
| 0.4953 | 3.01 | 50 | 0.4969 | 0.7542 |
| 0.4541 | 4.51 | 75 | 0.4627 | 0.7775 |
| 0.38 | 6.02 | 100 | 0.4566 | 0.7839 |
| 0.3357 | 7.52 | 125 | 0.4352 | 0.8072 |
| 0.307 | 9.02 | 150 | 0.4656 | 0.7881 |
| 0.2701 | 10.53 | 175 | 0.4294 | 0.8093 |
| 0.244 | 12.03 | 200 | 0.4797 | 0.8114 |
| 0.2294 | 13.53 | 225 | 0.4235 | 0.8263 |
| 0.2017 | 15.04 | 250 | 0.4744 | 0.8157 |
| 0.199 | 16.54 | 275 | 0.4450 | 0.8136 |
| 0.1793 | 18.05 | 300 | 0.4385 | 0.8114 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0