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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-patch16-224-in21k-face-recognition |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.999957997311828 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vit-base-patch16-224-in21k-face-recognition |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0015 |
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- Accuracy: 1.0000 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.00012 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.0368 | 1.0 | 372 | 0.0346 | 1.0000 | |
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| 0.0094 | 2.0 | 744 | 0.0092 | 1.0000 | |
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| 0.0046 | 3.0 | 1116 | 0.0047 | 1.0000 | |
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| 0.0029 | 4.0 | 1488 | 0.0029 | 1.0 | |
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| 0.0022 | 5.0 | 1860 | 0.0023 | 0.9999 | |
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| 0.0017 | 6.0 | 2232 | 0.0017 | 1.0 | |
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| 0.0015 | 7.0 | 2604 | 0.0015 | 1.0 | |
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| 0.0014 | 8.0 | 2976 | 0.0015 | 1.0000 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.13.2 |
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- Tokenizers 0.11.0 |
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