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: 0.8133047210300429
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: 0.5427
- Accuracy: 0.8133
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 |
|---|---|---|---|---|
| 0.49 | 1.15 | 150 | 0.5494 | 0.7232 |
| 0.4285 | 2.29 | 300 | 0.4832 | 0.7768 |
| 0.461 | 3.44 | 450 | 0.4907 | 0.7618 |
| 0.3535 | 4.58 | 600 | 0.4597 | 0.7811 |
| 0.2758 | 5.73 | 750 | 0.5102 | 0.7790 |
| 0.2705 | 6.87 | 900 | 0.4669 | 0.8004 |
| 0.2614 | 8.02 | 1050 | 0.4598 | 0.8004 |
| 0.2213 | 9.16 | 1200 | 0.4797 | 0.8112 |
| 0.1682 | 10.31 | 1350 | 0.5601 | 0.7876 |
| 0.144 | 11.45 | 1500 | 0.4544 | 0.8155 |
| 0.1269 | 12.6 | 1650 | 0.4904 | 0.8262 |
| 0.1638 | 13.74 | 1800 | 0.5052 | 0.8197 |
| 0.0869 | 14.89 | 1950 | 0.5427 | 0.8133 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0