Instructions to use maviya25/potato-disesase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use maviya25/potato-disesase with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://maviya25/potato-disesase") - Notebooks
- Google Colab
- Kaggle
Potato Disease Classification Model
This is a Convolutional Neural Network (CNN) model built using TensorFlow/Keras to classify potato leaf diseases into three categories:
- Early Blight (
Potato___Early_blight) - Late Blight (
Potato___Late_blight) - Healthy (
Potato___healthy)
Model Metrics
- Training Accuracy: 89.12%
- Validation Accuracy: 88.54%
- Test Accuracy (Unseen Data): 87.50%
- Epochs trained: 5 epochs (using Keras 3 / TensorFlow 2.18 on CPU)
Dataset
The model is trained on a dataset containing 2,152 images of potato leaves belonging to the three categories mentioned above.
How to Load and Use the Model
Keras 3 / TensorFlow 2.16+
To load the native Keras format model:
import tensorflow as tf
model = tf.keras.models.load_model("1.keras")
Legacy SavedModel Format
You can also use the SavedModel folder files to load the model:
import tensorflow as tf
# Load as an inference-only layer in Keras 3
model_layer = tf.keras.layers.TFSMLayer("./1", call_endpoint='serving_default')
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