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
File size: 822 Bytes
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"model_file": "fine_tuning_swa.keras",
"architecture": "EfficientNetV2-S+Conv2D(256)+BN+MaxPool2D+GeMPooling+Dense(256)+BN+Dropout(0.4)+Dense(8,softmax)",
"total_params": 23350633,
"trainable_params": 17810225,
"non_trainable_params": 5540408,
"input_shape": [
320,
320,
3
],
"num_classes": 8,
"model_size_mb": 226.75,
"saved_model_size_mb": 183.29,
"tflite_size_kb": 90483.55,
"tfjs_size_mb": 89.54,
"inference_ms_keras": 358.0,
"inference_ms_tflite": 170.0,
"metrics": {
"train_accuracy": 0.9988,
"val_accuracy": 0.9836,
"test_accuracy": 0.9777,
"test_loss": 0.4262,
"tta_accuracy": 0.9799,
"top1_accuracy": 0.9777,
"top2_accuracy": 0.9926,
"top3_accuracy": 0.997,
"macro_auc": 0.9985,
"ece": 0.1204,
"overfitting_gap": 0.0211
}
} |