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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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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: attraction-classifier |
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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: .faces |
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split: train |
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args: .faces |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8254437869822485 |
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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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# attraction-classifier |
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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: 0.4452 |
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- Accuracy: 0.8254 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 69 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 10 |
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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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| 0.622 | 0.99 | 42 | 0.4917 | 0.7825 | |
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| 0.5018 | 1.99 | 84 | 0.4727 | 0.7840 | |
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| 0.4308 | 2.98 | 126 | 0.4231 | 0.8254 | |
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| 0.3811 | 4.0 | 169 | 0.4085 | 0.8254 | |
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| 0.304 | 4.99 | 211 | 0.4239 | 0.8062 | |
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| 0.2844 | 5.99 | 253 | 0.4529 | 0.8047 | |
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| 0.2549 | 6.98 | 295 | 0.4248 | 0.8254 | |
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| 0.2162 | 8.0 | 338 | 0.4202 | 0.8195 | |
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| 0.2073 | 8.99 | 380 | 0.4388 | 0.8328 | |
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| 0.1751 | 9.94 | 420 | 0.4452 | 0.8254 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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