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: 0.10869565217391304
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: 25872499347325405328572416.0000
- Accuracy: 0.1087
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
- 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 |
|---|---|---|---|---|
| 21407918734188223332876288.0000 | 1.0 | 103 | 25872499347325405328572416.0000 | 0.1087 |
| 19230841377306649816989696.0000 | 2.0 | 206 | 25872499347325405328572416.0000 | 0.1087 |
| 22859301179210058793222144.0000 | 3.0 | 309 | 25872499347325405328572416.0000 | 0.1087 |
| 23584992401720978670878720.0000 | 4.0 | 412 | 25872499347325405328572416.0000 | 0.1087 |
| 24310687313580712431452160.0000 | 5.0 | 515 | 25872499347325405328572416.0000 | 0.1087 |
| 24310687313580712431452160.0000 | 6.0 | 618 | 25872499347325405328572416.0000 | 0.1087 |
| 22496457412629005795852288.0000 | 7.0 | 721 | 25872499347325405328572416.0000 | 0.1087 |
| 21045071278258356452589568.0000 | 8.0 | 824 | 25872499347325405328572416.0000 | 0.1087 |
| 21045071278258356452589568.0000 | 9.0 | 927 | 25872499347325405328572416.0000 | 0.1087 |
| 23343098402008016231596032.0000 | 10.0 | 1030 | 25872499347325405328572416.0000 | 0.1087 |
| 23222148635139925673508864.0000 | 11.0 | 1133 | 25872499347325405328572416.0000 | 0.1087 |
| 23222150479814334762450944.0000 | 12.0 | 1236 | 25872499347325405328572416.0000 | 0.1087 |
| 21407918734188223332876288.0000 | 13.0 | 1339 | 25872499347325405328572416.0000 | 0.1087 |
| 21407916889513814243934208.0000 | 14.0 | 1442 | 25872499347325405328572416.0000 | 0.1087 |
| 21770764345443681124220928.0000 | 15.0 | 1545 | 25872499347325405328572416.0000 | 0.1087 |
| 22496455567954601001877504.0000 | 16.0 | 1648 | 25872499347325405328572416.0000 | 0.1087 |
| 22859303023884467882164224.0000 | 17.0 | 1751 | 25872499347325405328572416.0000 | 0.1087 |
| 19593686988562107608334336.0000 | 18.0 | 1854 | 25872499347325405328572416.0000 | 0.1087 |
| 22859304868558872676139008.0000 | 19.0 | 1957 | 25872499347325405328572416.0000 | 0.1087 |
| 21528866656381904802021376.0000 | 20.0 | 2060 | 25872499347325405328572416.0000 | 0.1087 |
| 17053764020425078448586752.0000 | 21.0 | 2163 | 25872499347325405328572416.0000 | 0.1087 |
| 22133609956699138915565568.0000 | 22.0 | 2266 | 25872499347325405328572416.0000 | 0.1087 |
| 21045074967607170335506432.0000 | 23.0 | 2369 | 25872499347325405328572416.0000 | 0.1087 |
| 21407915044839405154992128.0000 | 24.0 | 2472 | 25872499347325405328572416.0000 | 0.1087 |
| 21770762500769272035278848.0000 | 25.0 | 2575 | 25872499347325405328572416.0000 | 0.1087 |
| 23947841702325254640107520.0000 | 26.0 | 2678 | 25872499347325405328572416.0000 | 0.1087 |
| 21045071278258356452589568.0000 | 27.0 | 2781 | 25872499347325405328572416.0000 | 0.1087 |
| 21770762500769272035278848.0000 | 28.0 | 2884 | 25872499347325405328572416.0000 | 0.1087 |
| 21407918734188223332876288.0000 | 29.0 | 2987 | 25872499347325405328572416.0000 | 0.1087 |
| 21528866656381904802021376.0000 | 30.0 | 3090 | 25872499347325405328572416.0000 | 0.1087 |
| 21045073122932761246564352.0000 | 31.0 | 3193 | 25872499347325405328572416.0000 | 0.1087 |
| 23584994246395387759820800.0000 | 32.0 | 3296 | 25872499347325405328572416.0000 | 0.1087 |
| 21045069433583947363647488.0000 | 33.0 | 3399 | 25872499347325405328572416.0000 | 0.1087 |
| 22859304868558872676139008.0000 | 34.0 | 3502 | 25872499347325405328572416.0000 | 0.1087 |
| 21407920578862628126851072.0000 | 35.0 | 3605 | 25872499347325405328572416.0000 | 0.1087 |
| 21045074967607170335506432.0000 | 36.0 | 3708 | 25872499347325405328572416.0000 | 0.1087 |
| 21770764345443681124220928.0000 | 37.0 | 3811 | 25872499347325405328572416.0000 | 0.1087 |
| 22496457412629005795852288.0000 | 38.0 | 3914 | 25872499347325405328572416.0000 | 0.1087 |
| 21407918734188223332876288.0000 | 39.0 | 4017 | 25872499347325405328572416.0000 | 0.1087 |
| 23222148635139925673508864.0000 | 40.0 | 4120 | 25872499347325405328572416.0000 | 0.1087 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0