| | --- |
| | license: apache-2.0 |
| | tags: |
| | - image-classification |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - recall |
| | - f1 |
| | - precision |
| | model-index: |
| | - name: vit-base-augmented-ph2-patch-16 |
| | results: [] |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # vit-base-augmented-ph2-patch-16 |
| |
|
| | 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 ahishamm/Augmented_PH2_db_sharpened dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5420 |
| | - Accuracy: 0.8444 |
| | - Recall: 0.8444 |
| | - F1: 0.8444 |
| | - Precision: 0.8444 |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | More information needed |
| | |
| | ## Training and evaluation data |
| | |
| | More information needed |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 0.0002 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 4 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
| | | 0.0592 | 0.36 | 50 | 0.7161 | 0.8068 | 0.8068 | 0.8068 | 0.8068 | |
| | | 0.0703 | 0.72 | 100 | 0.5420 | 0.8444 | 0.8444 | 0.8444 | 0.8444 | |
| | | 0.0042 | 1.09 | 150 | 0.5557 | 0.8821 | 0.8821 | 0.8821 | 0.8821 | |
| | | 0.0034 | 1.45 | 200 | 0.6464 | 0.8701 | 0.8701 | 0.8701 | 0.8701 | |
| | | 0.0023 | 1.81 | 250 | 0.7943 | 0.8410 | 0.8410 | 0.8410 | 0.8410 | |
| | | 0.0018 | 2.17 | 300 | 0.7109 | 0.8598 | 0.8598 | 0.8598 | 0.8598 | |
| | | 0.0015 | 2.54 | 350 | 0.7254 | 0.8598 | 0.8598 | 0.8598 | 0.8598 | |
| | | 0.0013 | 2.9 | 400 | 0.7364 | 0.8598 | 0.8598 | 0.8598 | 0.8598 | |
| | | 0.0013 | 3.26 | 450 | 0.7438 | 0.8615 | 0.8615 | 0.8615 | 0.8615 | |
| | | 0.0012 | 3.62 | 500 | 0.7489 | 0.8615 | 0.8615 | 0.8615 | 0.8615 | |
| | | 0.0012 | 3.99 | 550 | 0.7506 | 0.8615 | 0.8615 | 0.8615 | 0.8615 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.30.2 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.13.1 |
| | - Tokenizers 0.13.3 |
| | |
