| | --- |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224-in21k |
| | tags: |
| | - generated_from_keras_callback |
| | model-index: |
| | - name: volvoDon/petro-daemon |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information Keras had access to. You should |
| | probably proofread and complete it, then remove this comment. --> |
| |
|
| | # volvoDon/petro-daemon |
| |
|
| | This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on a [DataSet of petrologic cross sections](https://huggingface.co/datasets/volvoDon/petrology-sections). |
| | It achieves the following results on the evaluation set: |
| | - Train Loss: 0.8890 |
| | - Validation Loss: 1.1803 |
| | - Train Accuracy: 0.6 |
| | - Epoch: 19 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | Currently it is just a proof of concept and does a great job identifiying Olivine |
| | It currently is not ready for a production enviroment but the results are promising, with an improved dataset I'm confident better results could be acheived. |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 300, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} |
| | - training_precision: float32 |
| |
|
| | ### Training results |
| |
|
| | | Train Loss | Validation Loss | Train Accuracy | Epoch | |
| | |:----------:|:---------------:|:--------------:|:-----:| |
| | | 1.6519 | 1.7095 | 0.2 | 0 | |
| | | 1.5905 | 1.6747 | 0.2 | 1 | |
| | | 1.5690 | 1.6342 | 0.2 | 2 | |
| | | 1.5170 | 1.5931 | 0.2 | 3 | |
| | | 1.4764 | 1.5528 | 0.6 | 4 | |
| | | 1.3835 | 1.5079 | 0.6 | 5 | |
| | | 1.3420 | 1.4717 | 0.6 | 6 | |
| | | 1.3171 | 1.4232 | 0.6 | 7 | |
| | | 1.2897 | 1.3905 | 0.6 | 8 | |
| | | 1.2702 | 1.3794 | 0.6 | 9 | |
| | | 1.2023 | 1.3351 | 0.6 | 10 | |
| | | 1.1480 | 1.3384 | 0.6 | 11 | |
| | | 1.1434 | 1.3419 | 0.6 | 12 | |
| | | 1.0499 | 1.3226 | 0.6 | 13 | |
| | | 1.0672 | 1.2647 | 0.6 | 14 | |
| | | 1.0526 | 1.1533 | 0.6 | 15 | |
| | | 1.0184 | 1.1546 | 0.6 | 16 | |
| | | 0.9505 | 1.2491 | 0.6 | 17 | |
| | | 0.9578 | 1.2809 | 0.4 | 18 | |
| | | 0.8890 | 1.1803 | 0.6 | 19 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.32.1 |
| | - TensorFlow 2.12.0 |
| | - Datasets 2.14.4 |
| | - Tokenizers 0.13.3 |
| |
|
