Instructions to use Prahas10/roof_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prahas10/roof_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Prahas10/roof_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Prahas10/roof_classification") model = AutoModelForImageClassification.from_pretrained("Prahas10/roof_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: google/vit-base-patch32-384 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: Prahas10/roof_classification | |
| 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. --> | |
| # Prahas10/roof_classification | |
| This model is a fine-tuned version of [google/vit-base-patch32-384](https://huggingface.co/google/vit-base-patch32-384) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.0162 | |
| - Validation Loss: 0.2163 | |
| - Train Accuracy: 0.8916 | |
| - Epoch: 24 | |
| ## 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: | |
| - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4825, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.0001} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Validation Loss | Train Accuracy | Epoch | | |
| |:----------:|:---------------:|:--------------:|:-----:| | |
| | 2.5019 | 2.0795 | 0.3735 | 0 | | |
| | 1.7660 | 1.7259 | 0.4458 | 1 | | |
| | 1.0922 | 1.0990 | 0.7590 | 2 | | |
| | 0.6402 | 0.8232 | 0.8193 | 3 | | |
| | 0.4725 | 0.6107 | 0.8675 | 4 | | |
| | 0.2674 | 0.4986 | 0.9157 | 5 | | |
| | 0.1794 | 0.5000 | 0.9157 | 6 | | |
| | 0.2579 | 0.7721 | 0.7349 | 7 | | |
| | 0.1269 | 0.3304 | 0.8675 | 8 | | |
| | 0.0970 | 0.2980 | 0.8795 | 9 | | |
| | 0.1181 | 0.4988 | 0.8193 | 10 | | |
| | 0.1241 | 0.2899 | 0.8795 | 11 | | |
| | 0.2311 | 0.4113 | 0.8795 | 12 | | |
| | 0.0753 | 0.2964 | 0.9157 | 13 | | |
| | 0.0637 | 0.4096 | 0.8675 | 14 | | |
| | 0.0540 | 0.3032 | 0.9036 | 15 | | |
| | 0.0334 | 0.2694 | 0.9277 | 16 | | |
| | 0.0212 | 0.1793 | 0.9639 | 17 | | |
| | 0.0241 | 0.3772 | 0.8554 | 18 | | |
| | 0.0471 | 0.5727 | 0.8675 | 19 | | |
| | 0.0652 | 0.3167 | 0.8916 | 20 | | |
| | 0.0281 | 0.2690 | 0.9036 | 21 | | |
| | 0.0478 | 0.2169 | 0.9277 | 22 | | |
| | 0.0193 | 0.2091 | 0.9880 | 23 | | |
| | 0.0162 | 0.2163 | 0.8916 | 24 | | |
| ### Framework versions | |
| - Transformers 4.38.2 | |
| - TensorFlow 2.15.0 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.2 | |