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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: 0.575 |
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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: 1.2677 |
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- Accuracy: 0.575 |
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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: 3e-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: 3 |
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- total_train_batch_size: 48 |
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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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| 0.9379 | 0.97 | 13 | 1.2947 | 0.4875 | |
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| 0.9235 | 1.95 | 26 | 1.3397 | 0.475 | |
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| 0.8298 | 3.0 | 40 | 1.2971 | 0.5563 | |
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| 0.8883 | 3.98 | 53 | 1.3434 | 0.4875 | |
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| 0.8547 | 4.95 | 66 | 1.3226 | 0.475 | |
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| 0.8129 | 6.0 | 80 | 1.3077 | 0.5062 | |
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| 0.8095 | 6.97 | 93 | 1.2503 | 0.525 | |
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| 0.7764 | 7.95 | 106 | 1.2989 | 0.5312 | |
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| 0.7004 | 9.0 | 120 | 1.3383 | 0.4813 | |
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| 0.7013 | 9.97 | 133 | 1.3370 | 0.5125 | |
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| 0.6416 | 10.95 | 146 | 1.3073 | 0.5125 | |
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| 0.5831 | 12.0 | 160 | 1.3192 | 0.5 | |
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| 0.5968 | 12.97 | 173 | 1.2394 | 0.5375 | |
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| 0.5434 | 13.95 | 186 | 1.3389 | 0.5188 | |
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| 0.4605 | 15.0 | 200 | 1.2951 | 0.525 | |
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| 0.4674 | 15.97 | 213 | 1.2038 | 0.5687 | |
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| 0.3953 | 16.95 | 226 | 1.4019 | 0.5062 | |
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| 0.3595 | 18.0 | 240 | 1.4442 | 0.4813 | |
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| 0.3619 | 18.98 | 253 | 1.4213 | 0.525 | |
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| 0.3304 | 19.95 | 266 | 1.2937 | 0.5437 | |
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| 0.34 | 21.0 | 280 | 1.3024 | 0.5687 | |
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| 0.4215 | 21.98 | 293 | 1.4018 | 0.5375 | |
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| 0.3606 | 22.95 | 306 | 1.4221 | 0.5375 | |
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| 0.3402 | 24.0 | 320 | 1.4987 | 0.4313 | |
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| 0.3058 | 24.98 | 333 | 1.5120 | 0.5125 | |
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| 0.3047 | 25.95 | 346 | 1.5749 | 0.5 | |
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| 0.3616 | 27.0 | 360 | 1.4293 | 0.5188 | |
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| 0.3315 | 27.98 | 373 | 1.5326 | 0.5312 | |
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| 0.3535 | 28.95 | 386 | 1.5095 | 0.5188 | |
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| 0.3056 | 29.25 | 390 | 1.5366 | 0.5 | |
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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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