End of training
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README.md
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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: emotion_classification
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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: 0.4875
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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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# emotion_classification
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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: 1.3327
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- Accuracy: 0.4875
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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: 42
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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: 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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| 1.8526 | 1.0 | 10 | 1.8929 | 0.3563 |
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| 1.7464 | 2.0 | 20 | 1.7105 | 0.3625 |
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| 1.6096 | 3.0 | 30 | 1.5898 | 0.4625 |
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| 1.4988 | 4.0 | 40 | 1.5056 | 0.5188 |
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| 1.4218 | 5.0 | 50 | 1.4349 | 0.4938 |
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| 1.3439 | 6.0 | 60 | 1.4127 | 0.525 |
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| 1.2799 | 7.0 | 70 | 1.3780 | 0.55 |
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| 1.2037 | 8.0 | 80 | 1.3463 | 0.5 |
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| 1.1637 | 9.0 | 90 | 1.3236 | 0.55 |
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| 1.1361 | 10.0 | 100 | 1.2950 | 0.5437 |
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| 1.0836 | 11.0 | 110 | 1.3059 | 0.525 |
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| 1.046 | 12.0 | 120 | 1.2707 | 0.525 |
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| 1.0277 | 13.0 | 130 | 1.2686 | 0.5563 |
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| 1.0236 | 14.0 | 140 | 1.2790 | 0.5062 |
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| 0.9926 | 15.0 | 150 | 1.2763 | 0.5687 |
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### Framework versions
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- Transformers 4.33.1
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.5
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- Tokenizers 0.13.3
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