Official repository for:

1. Please follow official repository for ACDNet to prepare the audio datasets for these experiments
2. For AudioEvent dataset, the scripts are in this repository audio/resources/ae_

Example: python audio/trainer.py --model_path 'path/to/mini/or/micro/acdnet' --dataset 'esc10 or esc50 or us8k' --data '/path/to/dataset/' --xnor 0 --nClasses 50 --model_name 'file_name_tosave_the_trained_model'

1. ACDNet on ESC-10: python audio/trainer.py --model_path '' --dataset 'esc10' --data '/path/to/dataset/' --xnor 0 --nClasses 10 --model_name 'model_name'
2. ACDNet on us8k: python audio/trainer.py --model_path '' --dataset 'us8k' --data '/path/to/dataset/' --xnor 0 --nClasses 10 --model_name 'model_name'
3. ACDNet on AudioEvent (20 class): python audio/trainer.py --model_path '' --dataset 'audioevent' --data '/path/to/dataset/' --xnor 0 --nClasses 20 --model_name 'model_name'
4. Mini-ACDNet on ESC-50: python audio/trainer.py --model_path 'audio/models/mini_acdnet.pt' --dataset 'esc50' --data '/path/to/dataset/' --xnor 0 --nClasses 50 --model_name 'model_name'
5. Micro-ACDNet on ESC-10: python audio/trainer.py --model_path 'audio/models/micro_acdnet.pt' --dataset 'esc10' --data '/path/to/dataset/' --xnor 0 --nClasses 10 --model_name 'model_name'

1. XACDNet on ESC-50: python audio/trainer.py --model_path '' --dataset 'esc50' --data '/path/to/dataset/' --xnor 1 --nClasses 50 --model_name 'xacdnet_esc50'
2. XMicroACDnet on ESC-40: python audio/trainer.py --model_path 'audio/models/micro_acdnet.pt' --dataset 'esc40' --data '/path/to/dataset/' --xnor 1 --nClasses 40 --model_name 'xmicro_acdnet_esc40'
3. XMiniAcdnet on US8k: python audio/trainer.py --model_path 'audio/models/mini_acdnet.pt' --dataset 'us8k' --data '/path/to/dataset/' --xnor 1 --nClasses 10 --folds_to_train '[1,2]' --model_name 'xmini_acdnet_us8k'

1. Update settings opt.dataset, opt.data, opt.model_path, opt.model_name, opt.split in audio/quantization.py
2. Run: python audio/quantization.py

1. Create a folder to store the datasets and copy the path
2. Update args.datasetpath with the copied path in image/resources/prepare_dataset.py
3. Update args.datasetpath with the copied path in image/resources/prepare_dataset_cifar100.py
4. Update args.dataset_path in image/resources/settings.py
5. To prepare CIFAR-10, run in terminal: python image/resources/prepare_dataset.py
6. To prepare CIFAR-100, run in terminal: python image/resources/prepare_dataset_cifar100.py

1. CIFAR-10: python resnet/trainer.py --dataset 'cifar10' --icl 0 --classes 10
2. CIFAR-100: python resnet/trainer.py --dataset 'cifar100' --icl 0 --classes 100
3. For incremental training (e.g. CIFAR-30): python resnet/trainer.py --dataset 'cifar100' --icl 1 --classes 30

1. CIFAR-10: python resnet/bin_trainer.py --dataset 'cifar10' --icl 0 --classes 10
2. CIFAR-100: python resnet/bin_trainer.py --dataset 'cifar100' --icl 0 --classes 100
3. For incremental training (e.g. CIFAR-30): python resnet/bin_trainer.py --dataset 'cifar100' --icl 1 --classes 30