Instructions to use adarshcod30/openforensics-ensemble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use adarshcod30/openforensics-ensemble with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://adarshcod30/openforensics-ensemble") - Notebooks
- Google Colab
- Kaggle
File size: 918 Bytes
534b24f 44db0f5 534b24f 44db0f5 534b24f 44db0f5 534b24f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | {
"weights": "model.keras",
"input": {
"size": [
224,
224
],
"channels": 3,
"dtype": "float32",
"range": [
0,
1
],
"note": "Resize to size, divide by 255. Per-backbone normalisation happens inside the model."
},
"output": {
"name": "probability_real",
"range": [
0,
1
],
"note": "P(image is Real). Fake is 1 - p."
},
"params": 45406737,
"decision": {
"threshold": 0.36217108368873596,
"temperature": 0.8760257766124369,
"criterion": "target_recall",
"calibrated": true
},
"tta": {
"recommended": true,
"transform": "horizontal_flip",
"note": "Average P(Real) over the image and its mirror.",
"used_in_eval": true
},
"test_metrics": {
"accuracy": 0.948,
"roc_auc": 0.989896,
"pr_auc": 0.9899874925285668,
"real_called_fake": 25,
"false_accusation_rate": 0.025
}
} |