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SeyedAli
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Image-Arousal-new

Image Classification
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vit
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  1. README.md +80 -0
  2. model.safetensors +1 -1
  3. runs/May01_12-08-37_36f464ddd2f4/events.out.tfevents.1714565318.36f464ddd2f4.944.0 +2 -2
README.md ADDED
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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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+
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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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+
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+ # Image-Arousal-new
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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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