metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: UL_base_classification
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: 0.8921161825726142
UL_base_classification
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: 0.3125
- Accuracy: 0.8921
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: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8296 | 0.9756 | 20 | 0.5683 | 0.8230 |
| 0.4462 | 2.0 | 41 | 0.3949 | 0.8603 |
| 0.3588 | 2.9756 | 61 | 0.3633 | 0.8575 |
| 0.3196 | 4.0 | 82 | 0.3247 | 0.8852 |
| 0.2921 | 4.9756 | 102 | 0.3374 | 0.8728 |
| 0.2688 | 6.0 | 123 | 0.3125 | 0.8921 |
| 0.2366 | 6.8293 | 140 | 0.3137 | 0.8866 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1