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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
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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: 0.59375
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- name: Precision
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type: precision
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value: 0.6599395444120348
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- name: Recall
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type: recall
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value: 0.59375
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- name: F1
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type: f1
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value: 0.5919790409999833
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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
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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: 1.1926
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- Accuracy: 0.5938
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- Precision: 0.6599
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- Recall: 0.5938
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- F1: 0.5920
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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: 8
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- eval_batch_size: 8
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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 | 80 | 1.6474 | 0.3375 | 0.3120 | 0.3375 | 0.2259 |
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| No log | 2.0 | 160 | 1.4434 | 0.4625 | 0.5606 | 0.4625 | 0.4112 |
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| No log | 3.0 | 240 | 1.3266 | 0.4875 | 0.5296 | 0.4875 | 0.4516 |
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| No log | 4.0 | 320 | 1.2547 | 0.5375 | 0.5836 | 0.5375 | 0.5342 |
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| No log | 5.0 | 400 | 1.2195 | 0.5875 | 0.6815 | 0.5875 | 0.5900 |
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| No log | 6.0 | 480 | 1.1895 | 0.5563 | 0.5709 | 0.5563 | 0.5424 |
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| 1.2914 | 7.0 | 560 | 1.1572 | 0.5437 | 0.5607 | 0.5437 | 0.5431 |
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| 1.2914 | 8.0 | 640 | 1.1822 | 0.5563 | 0.5602 | 0.5563 | 0.5515 |
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| 1.2914 | 9.0 | 720 | 1.2712 | 0.55 | 0.5695 | 0.55 | 0.5530 |
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| 1.2914 | 10.0 | 800 | 1.2196 | 0.5625 | 0.5701 | 0.5625 | 0.5559 |
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| 1.2914 | 11.0 | 880 | 1.2460 | 0.5312 | 0.5584 | 0.5312 | 0.5357 |
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| 1.2914 | 12.0 | 960 | 1.2473 | 0.5563 | 0.5710 | 0.5563 | 0.5553 |
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| 0.5247 | 13.0 | 1040 | 1.2438 | 0.575 | 0.5908 | 0.575 | 0.5761 |
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| 0.5247 | 14.0 | 1120 | 1.3033 | 0.5312 | 0.5391 | 0.5312 | 0.5305 |
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| 0.5247 | 15.0 | 1200 | 1.2928 | 0.5625 | 0.5861 | 0.5625 | 0.5673 |
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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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