Instructions to use anum231/class2_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anum231/class2_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="anum231/class2_v1") 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("anum231/class2_v1") model = AutoModelForImageClassification.from_pretrained("anum231/class2_v1") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: anum231/class2_v1 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: anum231/class2_v1 | |
| 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. --> | |
| # anum231/class2_v1 | |
| This model is a fine-tuned version of [anum231/class2_v1](https://huggingface.co/anum231/class2_v1) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.4768 | |
| - Validation Loss: 0.3740 | |
| - Train Accuracy: 0.8966 | |
| - Epoch: 9 | |
| ## 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': 1160, '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': 0.01} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Validation Loss | Train Accuracy | Epoch | | |
| |:----------:|:---------------:|:--------------:|:-----:| | |
| | 0.9471 | 0.7465 | 0.4828 | 0 | | |
| | 0.7152 | 0.6636 | 0.5862 | 1 | | |
| | 0.6634 | 0.6322 | 0.6207 | 2 | | |
| | 0.6447 | 0.5829 | 0.6897 | 3 | | |
| | 0.6256 | 0.5359 | 0.7586 | 4 | | |
| | 0.6044 | 0.4895 | 0.8621 | 5 | | |
| | 0.5432 | 0.4623 | 0.8966 | 6 | | |
| | 0.5232 | 0.4666 | 0.8621 | 7 | | |
| | 0.5435 | 0.4061 | 0.8966 | 8 | | |
| | 0.4768 | 0.3740 | 0.8966 | 9 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - TensorFlow 2.15.0 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.1 | |