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-ve-b-U10-12
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: 0.7450980392156863
vit-base-patch16-224-ve-b-U10-12
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: 0.9868
- Accuracy: 0.7451
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: 5.5e-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: 0.05
- num_epochs: 12
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 0.96 | 6 | 1.3771 | 0.3137 |
1.3705 | 1.92 | 12 | 1.3219 | 0.5490 |
1.3705 | 2.88 | 18 | 1.2517 | 0.5490 |
1.2535 | 4.0 | 25 | 1.1875 | 0.5882 |
1.1079 | 4.96 | 31 | 1.1237 | 0.6078 |
1.1079 | 5.92 | 37 | 1.1003 | 0.6275 |
1.0048 | 6.88 | 43 | 1.0609 | 0.6863 |
0.9172 | 8.0 | 50 | 1.0668 | 0.6078 |
0.9172 | 8.96 | 56 | 1.0031 | 0.6667 |
0.8558 | 9.92 | 62 | 0.9868 | 0.7451 |
0.8558 | 10.88 | 68 | 0.9763 | 0.7451 |
0.8284 | 11.52 | 72 | 0.9733 | 0.7451 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0