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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: 0.8242677824267782 |
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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: 0.4274 |
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- Accuracy: 0.8243 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 69 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 512 |
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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: 0.05 |
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- num_epochs: 25 |
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- mixed_precision_training: Native AMP |
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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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| 0.6782 | 1.78 | 15 | 0.5922 | 0.7008 | |
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| 0.5096 | 3.56 | 30 | 0.5153 | 0.7552 | |
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| 0.4434 | 5.33 | 45 | 0.4520 | 0.7762 | |
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| 0.3844 | 7.11 | 60 | 0.4381 | 0.8013 | |
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| 0.3642 | 8.89 | 75 | 0.4359 | 0.8054 | |
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| 0.322 | 10.67 | 90 | 0.4086 | 0.8138 | |
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| 0.2845 | 12.44 | 105 | 0.4111 | 0.8201 | |
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| 0.2588 | 14.22 | 120 | 0.4100 | 0.8159 | |
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| 0.2516 | 16.0 | 135 | 0.4122 | 0.8389 | |
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| 0.2375 | 17.78 | 150 | 0.4085 | 0.8243 | |
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| 0.2309 | 19.56 | 165 | 0.4149 | 0.8117 | |
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| 0.2175 | 21.33 | 180 | 0.4274 | 0.8243 | |
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### Framework versions |
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- Transformers 4.37.2 |
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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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