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
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"name": "v2",
"out_dir": "/Users/adarsh/Desktop/Projects/OpenForensics/runs",
"data": {
"base_dir": "/Users/adarsh/Desktop/Projects/OpenForensics/Dataset",
"train_per_class": 10000,
"val_per_class": 3000,
"test_per_class": 1000,
"batch_size": 32,
"seed": 12345,
"img_size": [
224,
224
],
"corruption_prob": 0.5,
"max_corruptions": 2
},
"model": {
"backbones": [
"resnet50",
"vgg16",
"efficientnetv2b0"
],
"head_units": 256,
"dropout_branch": 0.4,
"dropout_merge": 0.4,
"dropout_final": 0.3
},
"train": {
"epochs": 20,
"lr": 0.0002,
"finetune_epochs": 10,
"finetune_lr": 1e-05,
"unfreeze_last": 50,
"freeze_batchnorm": true,
"monitor": "val_auc",
"monitor_mode": "max",
"early_stop_patience": 7,
"reduce_lr_patience": 3
}
} |