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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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metrics: |
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- accuracy |
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model-index: |
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- name: Image-Arousal-new |
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results: [] |
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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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# Image-Arousal-new |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6535 |
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- Accuracy: 0.4591 |
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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: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 4 |
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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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| 1.2322 | 0.1855 | 100 | 1.2411 | 0.4452 | |
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| 1.1613 | 0.3711 | 200 | 1.2600 | 0.3987 | |
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| 1.2851 | 0.5566 | 300 | 1.2428 | 0.4052 | |
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| 1.1931 | 0.7421 | 400 | 1.2041 | 0.4559 | |
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| 1.1098 | 0.9276 | 500 | 1.1918 | 0.4586 | |
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| 1.1714 | 1.1132 | 600 | 1.1806 | 0.4721 | |
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| 1.1216 | 1.2987 | 700 | 1.1692 | 0.4651 | |
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| 1.2208 | 1.4842 | 800 | 1.1801 | 0.4614 | |
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| 1.0644 | 1.6698 | 900 | 1.1775 | 0.4596 | |
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| 1.1638 | 1.8553 | 1000 | 1.2031 | 0.4721 | |
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| 0.9559 | 2.0408 | 1100 | 1.2392 | 0.4521 | |
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| 0.8442 | 2.2263 | 1200 | 1.2544 | 0.4661 | |
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| 0.8713 | 2.4119 | 1300 | 1.2792 | 0.4744 | |
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| 0.8442 | 2.5974 | 1400 | 1.2618 | 0.4647 | |
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| 0.831 | 2.7829 | 1500 | 1.3202 | 0.4554 | |
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| 0.7774 | 2.9685 | 1600 | 1.3087 | 0.4572 | |
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| 0.5501 | 3.1540 | 1700 | 1.4975 | 0.4600 | |
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| 0.6069 | 3.3395 | 1800 | 1.5869 | 0.4512 | |
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| 0.4397 | 3.5250 | 1900 | 1.6458 | 0.4387 | |
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| 0.4468 | 3.7106 | 2000 | 1.6341 | 0.4493 | |
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| 0.4198 | 3.8961 | 2100 | 1.6535 | 0.4591 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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