ViT-VGGFace
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8361
- Accuracy: 0.8306
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: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.6402 | 0.9982 | 416 | 1.7366 | 0.7127 |
| 1.1955 | 1.9988 | 833 | 1.2342 | 0.7782 |
| 0.9051 | 2.9994 | 1250 | 1.0314 | 0.8023 |
| 0.7446 | 4.0 | 1667 | 0.9074 | 0.8172 |
| 0.8081 | 4.9910 | 2080 | 0.8361 | 0.8306 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+rocm6.0
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
google/vit-base-patch16-224-in21k