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:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Expendadeur/agro-bio-models") - Notebooks
- Google Colab
- Kaggle
Download online_weights.json from Expendadeur/agro-bio-models: direct link, hf CLI and curl.
- Browser
- Download file 678 Bytes
-
https://huggingface.co/Expendadeur/agro-bio-models/resolve/main/online_weights.json
- Command line
-
hf download hf://Expendadeur/agro-bio-models/online_weights.json
-
curl -L -o online_weights.json https://huggingface.co/Expendadeur/agro-bio-models/resolve/main/online_weights.json
678 Bytes
| { | |
| "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 | |
| } |