Image Classification
Keras
LiteRT
plant-disease
efficientnetv2
tensorflow
plantvillage
computer-vision
transfer-learning
Instructions to use animeshakr/plant-disease-efficientnetv2s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use animeshakr/plant-disease-efficientnetv2s with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://animeshakr/plant-disease-efficientnetv2s") - Notebooks
- Google Colab
- Kaggle
Upload 2 files
Browse files- class_indices.json +40 -0
- model_float16_quant.tflite +3 -0
class_indices.json
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{
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"0": "Apple___Apple_scab",
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"1": "Apple___Black_rot",
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"2": "Apple___Cedar_apple_rust",
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"3": "Apple___healthy",
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"4": "Blueberry___healthy",
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"5": "Cherry___Powdery_mildew",
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"6": "Cherry___healthy",
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"7": "Corn___Cercospora_leaf_spot Gray_leaf_spot",
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"8": "Corn___Common_rust",
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"9": "Corn___Northern_Leaf_Blight",
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"10": "Corn___healthy",
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"11": "Grape___Black_rot",
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"12": "Grape___Esca_(Black_Measles)",
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"13": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
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"14": "Grape___healthy",
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"15": "Orange___Haunglongbing_(Citrus_greening)",
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"16": "Peach___Bacterial_spot",
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"17": "Peach___healthy",
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"18": "Pepper,_bell___Bacterial_spot",
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"19": "Pepper,_bell___healthy",
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"20": "Potato___Early_blight",
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"21": "Potato___Late_blight",
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"22": "Potato___healthy",
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"23": "Raspberry___healthy",
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"24": "Soybean___healthy",
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"25": "Squash___Powdery_mildew",
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"26": "Strawberry___Leaf_scorch",
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"27": "Strawberry___healthy",
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"28": "Tomato___Bacterial_spot",
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"29": "Tomato___Early_blight",
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"30": "Tomato___Late_blight",
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"31": "Tomato___Leaf_Mold",
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"32": "Tomato___Septoria_leaf_spot",
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"33": "Tomato___Spider_mites Two-spotted_spider_mite",
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"34": "Tomato___Target_Spot",
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"35": "Tomato___Tomato_Yellow_Leaf_Curl_Virus",
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"36": "Tomato___Tomato_mosaic_virus",
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"37": "Tomato___healthy"
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}
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model_float16_quant.tflite
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version https://git-lfs.github.com/spec/v1
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oid sha256:c94c4b1dd60d8d5c3b2e621105cf790b95584b49b7820e127ef68bc942f0f6f6
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size 41791472
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