đ vc-bantai-vit-withoutAMBI-adunest-v3
This model is a fine - tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It can be used for image classification tasks and achieves certain performance on the evaluation set.
đ Quick Start
This model is a fine - tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8889
- Accuracy: 0.8218
đ Documentation
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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Accuracy |
No log |
0.38 |
100 |
0.8208 |
0.7147 |
No log |
0.76 |
200 |
0.8861 |
0.7595 |
No log |
1.14 |
300 |
0.4306 |
0.7910 |
No log |
1.52 |
400 |
0.5222 |
0.8245 |
0.3448 |
1.9 |
500 |
0.8621 |
0.7602 |
0.3448 |
2.28 |
600 |
0.2902 |
0.8801 |
0.3448 |
2.66 |
700 |
0.3687 |
0.8426 |
0.3448 |
3.04 |
800 |
0.3585 |
0.8694 |
0.3448 |
3.42 |
900 |
0.6546 |
0.7897 |
0.2183 |
3.8 |
1000 |
0.3881 |
0.8272 |
0.2183 |
4.18 |
1100 |
0.9650 |
0.7709 |
0.2183 |
4.56 |
1200 |
0.6444 |
0.7917 |
0.2183 |
4.94 |
1300 |
0.4685 |
0.8707 |
0.2183 |
5.32 |
1400 |
0.4972 |
0.8506 |
0.157 |
5.7 |
1500 |
0.4010 |
0.8513 |
0.157 |
6.08 |
1600 |
0.4629 |
0.8419 |
0.157 |
6.46 |
1700 |
0.4258 |
0.8714 |
0.157 |
6.84 |
1800 |
0.4383 |
0.8573 |
0.157 |
7.22 |
1900 |
0.5324 |
0.8493 |
0.113 |
7.6 |
2000 |
0.3212 |
0.8942 |
0.113 |
7.98 |
2100 |
0.8621 |
0.8326 |
0.113 |
8.37 |
2200 |
0.6050 |
0.8131 |
0.113 |
8.75 |
2300 |
0.7173 |
0.7991 |
0.113 |
9.13 |
2400 |
0.5313 |
0.8125 |
0.0921 |
9.51 |
2500 |
0.6584 |
0.8158 |
0.0921 |
9.89 |
2600 |
0.8727 |
0.7930 |
0.0921 |
10.27 |
2700 |
0.4222 |
0.8922 |
0.0921 |
10.65 |
2800 |
0.5811 |
0.8265 |
0.0921 |
11.03 |
2900 |
0.6175 |
0.8372 |
0.0701 |
11.41 |
3000 |
0.3914 |
0.8835 |
0.0701 |
11.79 |
3100 |
0.3364 |
0.8654 |
0.0701 |
12.17 |
3200 |
0.6223 |
0.8359 |
0.0701 |
12.55 |
3300 |
0.7830 |
0.8125 |
0.0701 |
12.93 |
3400 |
0.4356 |
0.8942 |
0.0552 |
13.31 |
3500 |
0.7553 |
0.8232 |
0.0552 |
13.69 |
3600 |
0.9107 |
0.8292 |
0.0552 |
14.07 |
3700 |
0.6108 |
0.8580 |
0.0552 |
14.45 |
3800 |
0.5732 |
0.8567 |
0.0552 |
14.83 |
3900 |
0.5087 |
0.8614 |
0.0482 |
15.21 |
4000 |
0.8889 |
0.8218 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
đ License
This model is licensed under the Apache - 2.0 license.
Model Index
Property |
Details |
Model Name |
vc - bantai - vit - withoutAMBI - adunest - v3 |
Task |
Image Classification |
Dataset |
imagefolder (args: Violation - Classification---Raw - 10) |
Metrics |
Accuracy: 0.8218352310783658 |