Instructions to use chabdullah0566/Omnivision_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use chabdullah0566/Omnivision_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://chabdullah0566/Omnivision_Classifier") - Notebooks
- Google Colab
- Kaggle
Upload model_comparison.csv
Browse files- model_comparison.csv +4 -4
model_comparison.csv
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Model,Accuracy,Precision,Recall,F1
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ConvNeXtTiny,0.8457,0.
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EfficientNetV2B3,0.
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MobileNetV3Large,0.
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ResNet50V2,0.
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Model,Accuracy,Precision,Recall,F1
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ConvNeXtTiny,0.8457,0.8474,0.8457,0.8452
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EfficientNetV2B3,0.8267,0.8281,0.8267,0.8262
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MobileNetV3Large,0.8057,0.8068,0.8057,0.8049
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ResNet50V2,0.758,0.7586,0.758,0.7568
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