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: 0.7102137767220903
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: 0.6785
- Accuracy: 0.7102
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: 16
- eval_batch_size: 16
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6169 | 0.63 | 150 | 0.5812 | 0.6841 |
| 0.4724 | 1.27 | 300 | 0.5037 | 0.7553 |
| 0.5545 | 1.9 | 450 | 0.5195 | 0.7553 |
| 0.3928 | 2.53 | 600 | 0.5964 | 0.7102 |
| 0.3937 | 3.16 | 750 | 0.4637 | 0.7933 |
| 0.3897 | 3.8 | 900 | 0.4548 | 0.8076 |
| 0.356 | 4.43 | 1050 | 0.5327 | 0.7648 |
| 0.319 | 5.06 | 1200 | 0.5126 | 0.7720 |
| 0.3526 | 5.7 | 1350 | 0.6785 | 0.7102 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0