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: 0.756043956043956
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: 0.5514
- Accuracy: 0.7560
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: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.5592 | 0.59 | 150 | 0.7210 | 0.6 |
0.5506 | 1.17 | 300 | 0.5884 | 0.6703 |
0.4778 | 1.76 | 450 | 0.5711 | 0.6967 |
0.427 | 2.34 | 600 | 0.5350 | 0.7473 |
0.4146 | 2.93 | 750 | 0.4936 | 0.7626 |
0.3544 | 3.52 | 900 | 0.6238 | 0.7253 |
0.3431 | 4.1 | 1050 | 0.5962 | 0.7055 |
0.3273 | 4.69 | 1200 | 0.5514 | 0.7560 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0