metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-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: 0.7313780260707635
vit-base-patch16-224-classifier
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5720
- Accuracy: 0.7314
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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.646 | 1.0 | 537 | 0.6400 | 0.6420 |
| 0.5941 | 2.0 | 1074 | 0.5874 | 0.6974 |
| 0.5259 | 3.0 | 1611 | 0.5849 | 0.7142 |
| 0.5459 | 4.0 | 2148 | 0.5645 | 0.7197 |
| 0.5086 | 5.0 | 2685 | 0.5554 | 0.7230 |
| 0.5397 | 6.0 | 3222 | 0.5540 | 0.7295 |
| 0.5646 | 7.0 | 3759 | 0.5491 | 0.7272 |
| 0.4564 | 8.0 | 4296 | 0.5771 | 0.7235 |
| 0.4951 | 9.0 | 4833 | 0.5518 | 0.7267 |
| 0.5074 | 10.0 | 5370 | 0.5556 | 0.7300 |
| 0.5512 | 11.0 | 5907 | 0.5739 | 0.7165 |
| 0.5003 | 12.0 | 6444 | 0.5648 | 0.7235 |
| 0.4442 | 13.0 | 6981 | 0.5581 | 0.7230 |
| 0.4787 | 14.0 | 7518 | 0.5556 | 0.7402 |
| 0.4944 | 15.0 | 8055 | 0.5589 | 0.7342 |
| 0.4678 | 16.0 | 8592 | 0.5567 | 0.7379 |
| 0.5569 | 17.0 | 9129 | 0.5601 | 0.7314 |
| 0.4164 | 18.0 | 9666 | 0.5619 | 0.7365 |
| 0.4406 | 19.0 | 10203 | 0.5711 | 0.7309 |
| 0.453 | 20.0 | 10740 | 0.5720 | 0.7314 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2