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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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- precision
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- recall
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- f1
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model-index:
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- name: emotion_classification_v1.1
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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[:5000]
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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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- name: Precision
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type: precision
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value: 0.6064414347689876
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- name: Recall
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type: recall
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value: 0.575
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- name: F1
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type: f1
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value: 0.5730570699748332
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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_v1.1
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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: 1.2449
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- Accuracy: 0.575
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- Precision: 0.6064
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- Recall: 0.575
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- F1: 0.5731
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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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 15
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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 | 1.0 | 40 | 1.8287 | 0.325 | 0.2995 | 0.325 | 0.2695 |
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| No log | 2.0 | 80 | 1.5621 | 0.475 | 0.4171 | 0.475 | 0.4104 |
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| No log | 3.0 | 120 | 1.4485 | 0.4188 | 0.3786 | 0.4188 | 0.3710 |
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| No log | 4.0 | 160 | 1.4040 | 0.4313 | 0.5179 | 0.4313 | 0.3963 |
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| No log | 5.0 | 200 | 1.3333 | 0.4938 | 0.5016 | 0.4938 | 0.4654 |
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| No log | 6.0 | 240 | 1.3076 | 0.4688 | 0.4698 | 0.4688 | 0.4437 |
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| No log | 7.0 | 280 | 1.3531 | 0.4813 | 0.5289 | 0.4813 | 0.4834 |
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| No log | 8.0 | 320 | 1.3118 | 0.4688 | 0.4606 | 0.4688 | 0.4619 |
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| No log | 9.0 | 360 | 1.3326 | 0.4938 | 0.5629 | 0.4938 | 0.4744 |
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| No log | 10.0 | 400 | 1.2693 | 0.4938 | 0.4825 | 0.4938 | 0.4777 |
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| No log | 11.0 | 440 | 1.2310 | 0.55 | 0.5747 | 0.55 | 0.5441 |
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| No log | 12.0 | 480 | 1.2673 | 0.5375 | 0.5418 | 0.5375 | 0.5316 |
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| 1.0804 | 13.0 | 520 | 1.3161 | 0.5125 | 0.5321 | 0.5125 | 0.5048 |
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| 1.0804 | 14.0 | 560 | 1.2517 | 0.55 | 0.5550 | 0.55 | 0.5430 |
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| 1.0804 | 15.0 | 600 | 1.3344 | 0.5 | 0.5023 | 0.5 | 0.4848 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.0
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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