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:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://adarshcod30/openforensics-ensemble") - Notebooks
- Google Colab
- Kaggle
Download config.json from adarshcod30/openforensics-ensemble: direct link, hf CLI and curl.
- Browser
- Download file 874 Bytes
-
https://huggingface.co/adarshcod30/openforensics-ensemble/resolve/main/config.json
- Command line
-
hf download hf://adarshcod30/openforensics-ensemble/config.json
-
curl -L -o config.json https://huggingface.co/adarshcod30/openforensics-ensemble/resolve/main/config.json
874 Bytes
| { | |
| "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 | |
| } | |
| } |