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--- |
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license: apache-2.0 |
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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: vit_base_aihub_model_py |
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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: 0.9985872380503885 |
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- name: Precision |
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type: precision |
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value: 0.9989954885489135 |
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- name: Recall |
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type: recall |
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value: 0.998161142953993 |
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- name: F1 |
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type: f1 |
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value: 0.9985770990024514 |
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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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# vit_base_aihub_model_py |
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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: 0.0217 |
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- Accuracy: 0.9986 |
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- Precision: 0.9990 |
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- Recall: 0.9982 |
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- F1: 0.9986 |
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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: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 512 |
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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_ratio: 0.1 |
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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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| 0.1235 | 1.0 | 149 | 0.0936 | 0.9858 | 0.9845 | 0.9814 | 0.9830 | |
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| 0.067 | 2.0 | 299 | 0.0622 | 0.9878 | 0.9909 | 0.9813 | 0.9859 | |
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| 0.049 | 3.0 | 448 | 0.0322 | 0.9968 | 0.9969 | 0.9959 | 0.9964 | |
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| 0.0477 | 4.0 | 598 | 0.0249 | 0.9978 | 0.9985 | 0.9965 | 0.9975 | |
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| 0.0336 | 4.98 | 745 | 0.0217 | 0.9986 | 0.9990 | 0.9982 | 0.9986 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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