my_awesome_face_model
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: 1.3503
- Accuracy: 0.4938
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: 6
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 0.9 | 3 | 2.0832 | 0.1 |
No log | 1.8 | 6 | 2.0749 | 0.1 |
2.0704 | 3.0 | 10 | 2.0478 | 0.1812 |
2.0704 | 3.9 | 13 | 2.0288 | 0.2 |
2.0704 | 4.8 | 16 | 1.9896 | 0.2812 |
1.9609 | 6.0 | 20 | 1.9322 | 0.3125 |
1.9609 | 6.9 | 23 | 1.8715 | 0.3438 |
1.9609 | 7.8 | 26 | 1.8056 | 0.3688 |
1.7478 | 9.0 | 30 | 1.7196 | 0.4125 |
1.7478 | 9.9 | 33 | 1.6635 | 0.3937 |
1.7478 | 10.8 | 36 | 1.6160 | 0.3875 |
1.5379 | 12.0 | 40 | 1.6014 | 0.425 |
1.5379 | 12.9 | 43 | 1.5505 | 0.4188 |
1.5379 | 13.8 | 46 | 1.5187 | 0.4188 |
1.3858 | 15.0 | 50 | 1.4938 | 0.4562 |
1.3858 | 15.9 | 53 | 1.4853 | 0.4562 |
1.3858 | 16.8 | 56 | 1.4664 | 0.4875 |
1.2551 | 18.0 | 60 | 1.4488 | 0.4375 |
1.2551 | 18.9 | 63 | 1.4531 | 0.4375 |
1.2551 | 19.8 | 66 | 1.3847 | 0.525 |
1.1573 | 21.0 | 70 | 1.3790 | 0.4813 |
1.1573 | 21.9 | 73 | 1.4168 | 0.4625 |
1.1573 | 22.8 | 76 | 1.4159 | 0.5 |
1.0833 | 24.0 | 80 | 1.3935 | 0.4813 |
1.0833 | 24.9 | 83 | 1.3965 | 0.4688 |
1.0833 | 25.8 | 86 | 1.3705 | 0.4875 |
1.0091 | 27.0 | 90 | 1.3879 | 0.4625 |
1.0091 | 27.9 | 93 | 1.3777 | 0.5062 |
1.0091 | 28.8 | 96 | 1.3867 | 0.4813 |
0.9632 | 30.0 | 100 | 1.3659 | 0.5188 |
0.9632 | 30.9 | 103 | 1.3398 | 0.4875 |
0.9632 | 31.8 | 106 | 1.3555 | 0.4688 |
0.9259 | 33.0 | 110 | 1.3621 | 0.5062 |
0.9259 | 33.9 | 113 | 1.3569 | 0.4938 |
0.9259 | 34.8 | 116 | 1.3397 | 0.5312 |
0.8994 | 36.0 | 120 | 1.3314 | 0.525 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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