| ---
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| library_name: transformers
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| license: apache-2.0
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| base_model: google/vit-base-patch16-224
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| tags:
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| - image-classification
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| - vision-transformer
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| - flowers
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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: flower-vit
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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: custom flower dataset
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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: 0.9636363636363636
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| - name: Precision
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| type: precision
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| value: 0.9632702640149449
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| - name: Recall
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| type: recall
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| value: 0.9636363636363636
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| - name: F1
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| type: f1
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| value: 0.9632142875960066
|
| ---
|
|
|
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| should probably proofread and complete it, then remove this comment. -->
|
|
|
| # flower-vit
|
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the custom flower dataset dataset.
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| It achieves the following results on the evaluation set:
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| - Loss: 0.1623
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| - Accuracy: 0.9636
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| - Precision: 0.9633
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| - Recall: 0.9636
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| - F1: 0.9632
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|
|
| ## Model description
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|
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| More information needed
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|
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| ## Intended uses & limitations
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|
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| More information needed
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|
|
| ## Training and evaluation data
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|
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| More information needed
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|
|
| ## Training procedure
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|
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| ### Training hyperparameters
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|
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| The following hyperparameters were used during training:
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| - learning_rate: 0.0003
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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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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| - lr_scheduler_type: linear
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| - num_epochs: 5
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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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| | 0.1765 | 1.0 | 138 | 0.1646 | 0.9673 | 0.9679 | 0.9673 | 0.9673 |
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| | 0.1386 | 2.0 | 276 | 0.1291 | 0.9673 | 0.9681 | 0.9673 | 0.9673 |
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| | 0.0889 | 3.0 | 414 | 0.1214 | 0.9673 | 0.9681 | 0.9673 | 0.9673 |
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| | 0.0857 | 4.0 | 552 | 0.1183 | 0.9673 | 0.9681 | 0.9673 | 0.9673 |
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| | 0.0942 | 5.0 | 690 | 0.1177 | 0.9673 | 0.9681 | 0.9673 | 0.9673 |
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|
|
|
|
| ### Framework versions
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|
|
| - Transformers 5.5.4
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| - Pytorch 2.11.0+cpu
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| - Datasets 4.8.4
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| - Tokenizers 0.22.2
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| |
