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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: 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: 0.7955974842767296 |
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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: 0.4691 |
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- Accuracy: 0.7956 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.15 |
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- num_epochs: 15 |
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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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| 0.6703 | 0.34 | 15 | 0.6354 | 0.7327 | |
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| 0.5449 | 0.67 | 30 | 0.5836 | 0.7421 | |
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| 0.5407 | 1.01 | 45 | 0.5594 | 0.7421 | |
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| 0.5255 | 1.34 | 60 | 0.5294 | 0.7547 | |
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| 0.5586 | 1.68 | 75 | 0.5171 | 0.7642 | |
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| 0.5438 | 2.01 | 90 | 0.5212 | 0.7704 | |
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| 0.4807 | 2.35 | 105 | 0.5181 | 0.7390 | |
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| 0.6202 | 2.68 | 120 | 0.4972 | 0.7704 | |
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| 0.5021 | 3.02 | 135 | 0.4566 | 0.7987 | |
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| 0.4313 | 3.35 | 150 | 0.4852 | 0.7925 | |
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| 0.3532 | 3.69 | 165 | 0.4378 | 0.8113 | |
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| 0.3577 | 4.02 | 180 | 0.4515 | 0.8019 | |
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| 0.4736 | 4.36 | 195 | 0.4498 | 0.7893 | |
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| 0.3516 | 4.69 | 210 | 0.4408 | 0.8239 | |
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| 0.4437 | 5.03 | 225 | 0.4611 | 0.7799 | |
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| 0.3543 | 5.36 | 240 | 0.4294 | 0.8208 | |
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| 0.4029 | 5.7 | 255 | 0.4155 | 0.8428 | |
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| 0.3808 | 6.03 | 270 | 0.4116 | 0.8302 | |
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| 0.3211 | 6.37 | 285 | 0.4009 | 0.8302 | |
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| 0.2949 | 6.7 | 300 | 0.4321 | 0.8176 | |
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| 0.2663 | 7.04 | 315 | 0.4229 | 0.8396 | |
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| 0.3049 | 7.37 | 330 | 0.4110 | 0.8365 | |
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| 0.1303 | 7.71 | 345 | 0.4288 | 0.8333 | |
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| 0.2079 | 8.04 | 360 | 0.4218 | 0.8208 | |
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| 0.208 | 8.38 | 375 | 0.3908 | 0.8365 | |
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| 0.2067 | 8.72 | 390 | 0.5191 | 0.7862 | |
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| 0.1635 | 9.05 | 405 | 0.4691 | 0.7956 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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