Instructions to use mikejrodd/esca_grapeleaf_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use mikejrodd/esca_grapeleaf_classifier with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://mikejrodd/esca_grapeleaf_classifier") - Notebooks
- Google Colab
- Kaggle
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- Accuracy: 0.92
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### Confusion Matrix
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- **True Positives (TP)**: `esca` correctly identified as `esca`: 468
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- **True Negatives (TN)**: `healthy` correctly identified as `healthy`: 1197
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- **False Positives (FP)**: `healthy` incorrectly identified as `esca`: 12
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- **False Negatives (FN)**: `esca` incorrectly identified as `healthy`: 128
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### License
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The data used to train this model is licensed under the CC0 Public Domain Dedication. The model itself is licensed under the MIT License.
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- Accuracy: 0.92
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### License
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The data used to train this model is licensed under the CC0 Public Domain Dedication. The model itself is licensed under the MIT License.
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