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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: 0.58125 |
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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: 1.4116 |
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- Accuracy: 0.5813 |
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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_steps: 500 |
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- num_epochs: 30 |
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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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| No log | 1.0 | 10 | 1.3914 | 0.5312 | |
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| No log | 2.0 | 20 | 1.3253 | 0.4875 | |
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| No log | 3.0 | 30 | 1.4217 | 0.4813 | |
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| No log | 4.0 | 40 | 1.3711 | 0.5062 | |
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| No log | 5.0 | 50 | 1.3584 | 0.5 | |
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| No log | 6.0 | 60 | 1.3163 | 0.5 | |
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| No log | 7.0 | 70 | 1.3824 | 0.5188 | |
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| No log | 8.0 | 80 | 1.3882 | 0.525 | |
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| No log | 9.0 | 90 | 1.4126 | 0.5188 | |
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| No log | 10.0 | 100 | 1.3213 | 0.5625 | |
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| No log | 11.0 | 110 | 1.4385 | 0.5 | |
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| No log | 12.0 | 120 | 1.3729 | 0.525 | |
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| No log | 13.0 | 130 | 1.4603 | 0.4938 | |
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| No log | 14.0 | 140 | 1.5326 | 0.4688 | |
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| No log | 15.0 | 150 | 1.3687 | 0.5563 | |
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| No log | 16.0 | 160 | 1.4537 | 0.55 | |
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| No log | 17.0 | 170 | 1.5377 | 0.5188 | |
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| No log | 18.0 | 180 | 1.6417 | 0.4688 | |
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| No log | 19.0 | 190 | 1.5260 | 0.55 | |
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| No log | 20.0 | 200 | 1.6854 | 0.4938 | |
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| No log | 21.0 | 210 | 1.6457 | 0.5062 | |
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| No log | 22.0 | 220 | 1.5855 | 0.5125 | |
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| No log | 23.0 | 230 | 1.5083 | 0.5312 | |
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| No log | 24.0 | 240 | 1.5656 | 0.525 | |
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| No log | 25.0 | 250 | 1.5931 | 0.5125 | |
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| No log | 26.0 | 260 | 1.4351 | 0.5687 | |
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| No log | 27.0 | 270 | 1.5031 | 0.525 | |
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| No log | 28.0 | 280 | 1.4129 | 0.55 | |
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| No log | 29.0 | 290 | 1.5323 | 0.5125 | |
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| No log | 30.0 | 300 | 1.5217 | 0.5625 | |
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### Framework versions |
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- Transformers 4.33.2 |
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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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