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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_recognition |
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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: 0.6125 |
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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_recognition |
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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: 1.2014 |
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- Accuracy: 0.6125 |
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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: cosine_with_restarts |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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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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| 2.0842 | 1.0 | 10 | 2.0668 | 0.175 | |
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| 2.039 | 2.0 | 20 | 2.0070 | 0.2875 | |
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| 1.9285 | 3.0 | 30 | 1.8789 | 0.4062 | |
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| 1.7699 | 4.0 | 40 | 1.6942 | 0.425 | |
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| 1.6135 | 5.0 | 50 | 1.5758 | 0.4313 | |
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| 1.5056 | 6.0 | 60 | 1.4884 | 0.55 | |
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| 1.3896 | 7.0 | 70 | 1.3999 | 0.5437 | |
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| 1.2804 | 8.0 | 80 | 1.3563 | 0.5437 | |
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| 1.2043 | 9.0 | 90 | 1.3244 | 0.55 | |
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| 1.1231 | 10.0 | 100 | 1.2775 | 0.6062 | |
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| 1.0652 | 11.0 | 110 | 1.2567 | 0.575 | |
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| 1.0005 | 12.0 | 120 | 1.2833 | 0.5563 | |
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| 0.9878 | 13.0 | 130 | 1.2277 | 0.5687 | |
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| 0.9714 | 14.0 | 140 | 1.2557 | 0.5563 | |
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| 0.9057 | 15.0 | 150 | 1.2187 | 0.6125 | |
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| 0.8854 | 16.0 | 160 | 1.2612 | 0.5437 | |
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| 0.8478 | 17.0 | 170 | 1.2450 | 0.5437 | |
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| 0.8601 | 18.0 | 180 | 1.2456 | 0.5375 | |
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| 0.8498 | 19.0 | 190 | 1.2413 | 0.5875 | |
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| 0.8775 | 20.0 | 200 | 1.1928 | 0.6 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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