Image Classification
Keras
LiteRT
TF-Keras
Safetensors
English
efficientnetv2-s
efficientnetv2
fgic
transfer-learning
gem-pooling
focal-loss
swa
grad-cam
calibration
temperature-scaling
computer-vision
tensorflow.js
Eval Results (legacy)
Instructions to use 0xgr3y/Arch-Building-Image-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use 0xgr3y/Arch-Building-Image-Classification with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://0xgr3y/Arch-Building-Image-Classification") - Notebooks
- Google Colab
- Kaggle
Update preprocessor_config.json
Browse files- preprocessor_config.json +1 -1
preprocessor_config.json
CHANGED
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@@ -11,7 +11,7 @@
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"mean": [123.675, 116.28, 103.53],
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"std": [58.395, 57.12, 57.375],
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"scale": 1.0,
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"description": "
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},
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"input_name": "input_1",
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"output_name": "output_1",
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"mean": [123.675, 116.28, 103.53],
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"std": [58.395, 57.12, 57.375],
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"scale": 1.0,
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"description": "Use tf.keras.applications.densenet.preprocess_input for automatic preprocessing. For manual TF-Lite: RGB->BGR, subtract ImageNet mean. Note: preprocessor_config.json reserves 'BGR' for DenseNet ImageNet references, but densenet.preprocess_input() works on RGB input and handles the internal conversion."
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},
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"input_name": "input_1",
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"output_name": "output_1",
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