Instructions to use PredictiveManish/wall-crack-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use PredictiveManish/wall-crack-detection with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://PredictiveManish/wall-crack-detection") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): c514f67
Dataset and Model uploaded
Browse files- README.md +3 -0
- crack_detector.h5 +3 -0
README.md
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# Wall Crack Detection Model
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This model helps in detecting cracks or deforming walls.
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It is created by training CNNs on a large dataset of around 32,000 images (16000-Negative, 32000-Positive) Negative means it had no defect and Positive means it has defects.
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crack_detector.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:da794f29e7f2a65daf2a9f172ba0f533e6273e71d880ca92701842f14cecc3a1
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size 9584112
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