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
Upload model_benchmark.json with huggingface_hub
Browse files- model_benchmark.json +13 -13
model_benchmark.json
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"saved_model_size_mb": 183.29,
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"tflite_size_kb": 90483.55,
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"tfjs_size_mb": 89.54,
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"inference_ms_keras":
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"inference_ms_tflite":
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"metrics": {
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"train_accuracy": 0.
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"tta_accuracy": 0.
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"ece": 0.
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"overfitting_gap": 0.
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}
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"saved_model_size_mb": 183.29,
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"tflite_size_kb": 90483.55,
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"tfjs_size_mb": 89.54,
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"inference_ms_keras": 437.5,
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"inference_ms_tflite": 197.8,
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"metrics": {
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"train_accuracy": 0.9997,
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"val_accuracy": 0.9851,
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"test_accuracy": 0.9792,
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"test_loss": 0.3928,
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"tta_accuracy": 0.9814,
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"top1_accuracy": 0.9792,
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"top2_accuracy": 0.9918,
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"top3_accuracy": 0.9955,
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"macro_auc": 0.9975,
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"ece": 0.1813,
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"overfitting_gap": 0.0206
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}
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}
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