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-U13b-80RX1
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: 0.7608695652173914
vit-base-patch16-224-ve-U13b-80RX1
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: 1.2530
- Accuracy: 0.7609
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 0.05
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3157 | 0.99 | 51 | 1.2968 | 0.3478 |
| 1.0334 | 2.0 | 103 | 1.0060 | 0.5217 |
| 0.691 | 2.99 | 154 | 0.7506 | 0.7609 |
| 0.5005 | 4.0 | 206 | 0.6433 | 0.7826 |
| 0.3478 | 4.99 | 257 | 0.5674 | 0.7609 |
| 0.3339 | 6.0 | 309 | 0.6623 | 0.7609 |
| 0.2533 | 6.99 | 360 | 0.6905 | 0.7391 |
| 0.138 | 8.0 | 412 | 0.7251 | 0.7826 |
| 0.1289 | 8.99 | 463 | 0.7467 | 0.7391 |
| 0.152 | 10.0 | 515 | 0.9011 | 0.7174 |
| 0.2609 | 10.99 | 566 | 1.0150 | 0.7174 |
| 0.2202 | 12.0 | 618 | 0.9713 | 0.7826 |
| 0.1083 | 12.99 | 669 | 1.1106 | 0.6739 |
| 0.07 | 14.0 | 721 | 1.1211 | 0.7174 |
| 0.0791 | 14.99 | 772 | 1.1830 | 0.7609 |
| 0.0427 | 16.0 | 824 | 0.7770 | 0.8478 |
| 0.1219 | 16.99 | 875 | 1.0962 | 0.7391 |
| 0.0739 | 18.0 | 927 | 0.9447 | 0.7609 |
| 0.1989 | 18.99 | 978 | 1.1543 | 0.7391 |
| 0.1097 | 20.0 | 1030 | 1.1795 | 0.7609 |
| 0.1204 | 20.99 | 1081 | 1.2679 | 0.6739 |
| 0.0514 | 22.0 | 1133 | 1.0646 | 0.7174 |
| 0.0612 | 22.99 | 1184 | 1.1413 | 0.6957 |
| 0.0207 | 24.0 | 1236 | 0.8928 | 0.7826 |
| 0.1063 | 24.99 | 1287 | 1.1186 | 0.7609 |
| 0.1076 | 26.0 | 1339 | 1.1741 | 0.7609 |
| 0.0714 | 26.99 | 1390 | 1.0977 | 0.8043 |
| 0.062 | 28.0 | 1442 | 1.3965 | 0.7174 |
| 0.0617 | 28.99 | 1493 | 1.1849 | 0.7609 |
| 0.0536 | 30.0 | 1545 | 1.0865 | 0.7826 |
| 0.0707 | 30.99 | 1596 | 1.2081 | 0.7609 |
| 0.0967 | 32.0 | 1648 | 1.3300 | 0.7391 |
| 0.0564 | 32.99 | 1699 | 1.2240 | 0.7826 |
| 0.0435 | 34.0 | 1751 | 1.2391 | 0.7609 |
| 0.043 | 34.99 | 1802 | 1.1813 | 0.7609 |
| 0.0218 | 36.0 | 1854 | 1.2496 | 0.7826 |
| 0.0043 | 36.99 | 1905 | 1.2797 | 0.7174 |
| 0.0051 | 38.0 | 1957 | 1.2493 | 0.7391 |
| 0.0123 | 38.99 | 2008 | 1.2538 | 0.7391 |
| 0.0546 | 39.61 | 2040 | 1.2530 | 0.7609 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0