Instructions to use Expendadeur/agro-bio-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Expendadeur/agro-bio-models with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Expendadeur/agro-bio-models") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_names": [ | |
| "EfficientNetV2M", | |
| "ResNet50", | |
| "MobileNetV3L", | |
| "ConvNeXtBase" | |
| ], | |
| "weights_optuna": [ | |
| 0.22634092985667137, | |
| 0.20263165013640808, | |
| 0.3102530833043855, | |
| 0.260774336702535 | |
| ], | |
| "weights_acc": [ | |
| 0.25192203328509405, | |
| 0.24701519536903035, | |
| 0.2512210564399421, | |
| 0.2498417149059334 | |
| ], | |
| "accuracies": { | |
| "EfficientNetV2M": 0.9551611796982168, | |
| "ResNet50": 0.936556927297668, | |
| "MobileNetV3L": 0.9525034293552812, | |
| "ConvNeXtBase": 0.9472736625514403 | |
| }, | |
| "accuracy_ensemble_mean": 0.9613340192043895, | |
| "accuracy_ensemble_weighted": 0.9613340192043895, | |
| "accuracy_ensemble_optuna": 0.9623628257887518 | |
| } |