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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: belajar_huggingface
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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[:10000]
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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: 0.5
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- name: Precision
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type: precision
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value: 0.5110750617136487
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- name: Recall
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type: recall
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value: 0.5
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- name: F1
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type: f1
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value: 0.49895214791397935
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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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# belajar_huggingface
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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: 1.3425
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- Accuracy: 0.5
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- Precision: 0.5111
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- Recall: 0.5
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- F1: 0.4990
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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: 0.00025
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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 | 1.7396 | 0.2938 | 0.2269 | 0.2938 | 0.2006 |
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| No log | 2.0 | 80 | 1.7746 | 0.3812 | 0.4438 | 0.3812 | 0.3612 |
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| No log | 3.0 | 120 | 1.4630 | 0.3875 | 0.3634 | 0.3875 | 0.3280 |
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| No log | 4.0 | 160 | 1.4815 | 0.3812 | 0.3819 | 0.3812 | 0.3604 |
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| No log | 5.0 | 200 | 1.2788 | 0.475 | 0.5219 | 0.475 | 0.4553 |
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| No log | 6.0 | 240 | 1.2866 | 0.5312 | 0.5366 | 0.5312 | 0.5311 |
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| No log | 7.0 | 280 | 1.4916 | 0.4313 | 0.4654 | 0.4313 | 0.4053 |
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| No log | 8.0 | 320 | 1.3428 | 0.5125 | 0.5307 | 0.5125 | 0.5158 |
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| No log | 9.0 | 360 | 1.4789 | 0.4188 | 0.4177 | 0.4188 | 0.4054 |
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| No log | 10.0 | 400 | 1.6132 | 0.4375 | 0.4619 | 0.4375 | 0.4323 |
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| No log | 11.0 | 440 | 1.5168 | 0.4875 | 0.5142 | 0.4875 | 0.4911 |
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| No log | 12.0 | 480 | 1.4779 | 0.5312 | 0.5566 | 0.5312 | 0.5323 |
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| 0.9086 | 13.0 | 520 | 1.5962 | 0.4813 | 0.4911 | 0.4813 | 0.4798 |
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| 0.9086 | 14.0 | 560 | 1.5281 | 0.5188 | 0.5613 | 0.5188 | 0.5220 |
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| 0.9086 | 15.0 | 600 | 1.5682 | 0.525 | 0.5536 | 0.525 | 0.5283 |
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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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