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-1e-4-15ep
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8867063492063492
vit-base-1e-4-15ep
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: 0.3897
- Accuracy: 0.8867
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: 0.0001
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5399 | 1.0 | 275 | 0.4756 | 0.8676 |
| 0.2126 | 2.0 | 550 | 0.4134 | 0.8875 |
| 0.0726 | 3.0 | 825 | 0.4687 | 0.8775 |
| 0.0345 | 4.0 | 1100 | 0.4552 | 0.8883 |
| 0.0123 | 5.0 | 1375 | 0.5129 | 0.8851 |
| 0.0068 | 6.0 | 1650 | 0.4877 | 0.8954 |
| 0.0063 | 7.0 | 1925 | 0.4667 | 0.9018 |
| 0.0055 | 8.0 | 2200 | 0.4697 | 0.9030 |
| 0.0021 | 9.0 | 2475 | 0.4620 | 0.9054 |
| 0.0039 | 10.0 | 2750 | 0.4652 | 0.9058 |
| 0.0027 | 11.0 | 3025 | 0.4658 | 0.9058 |
| 0.0024 | 12.0 | 3300 | 0.4668 | 0.9078 |
| 0.0021 | 13.0 | 3575 | 0.4671 | 0.9078 |
| 0.0019 | 14.0 | 3850 | 0.4681 | 0.9062 |
| 0.002 | 15.0 | 4125 | 0.4682 | 0.9062 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2