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--- |
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license: apache-2.0 |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: emotion-dectect |
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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: 0.8807339449541285 |
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- name: Precision |
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type: precision |
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value: 0.8768597487153273 |
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- name: Recall |
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type: recall |
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value: 0.8807339449541285 |
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- name: F1 |
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type: f1 |
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value: 0.8782945902988435 |
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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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# google-vit-base-patch16-224-cartoon-emotion-detection |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3706 |
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- Accuracy: 0.8807 |
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- Precision: 0.8769 |
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- Recall: 0.8807 |
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- F1: 0.8783 |
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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: 0.00012 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 0.97 | 8 | 0.9902 | 0.5596 | 0.5506 | 0.5596 | 0.5360 | |
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| 1.242 | 1.97 | 16 | 0.5157 | 0.8165 | 0.8195 | 0.8165 | 0.8132 | |
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| 0.4438 | 2.97 | 24 | 0.3871 | 0.8440 | 0.8516 | 0.8440 | 0.8446 | |
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| 0.1768 | 3.97 | 32 | 0.3531 | 0.8624 | 0.8653 | 0.8624 | 0.8585 | |
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| 0.0661 | 4.97 | 40 | 0.3780 | 0.8716 | 0.8693 | 0.8716 | 0.8674 | |
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| 0.0661 | 5.97 | 48 | 0.3747 | 0.8624 | 0.8649 | 0.8624 | 0.8632 | |
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| 0.0375 | 6.97 | 56 | 0.3760 | 0.8991 | 0.8961 | 0.8991 | 0.8971 | |
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| 0.0362 | 7.97 | 64 | 0.4092 | 0.8716 | 0.8684 | 0.8716 | 0.8681 | |
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| 0.0322 | 8.97 | 72 | 0.3499 | 0.8899 | 0.8880 | 0.8899 | 0.8888 | |
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| 0.029 | 9.97 | 80 | 0.3706 | 0.8807 | 0.8769 | 0.8807 | 0.8783 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.11.0 |
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