Reimplementations of deep learning models.

• ottt_cifar10.py: From Online Training Through Time for Spiking Neural Networks

Usage

$ python main.py --help
...
$ python main.py --batch-size 128 vgg16 cifar100 train /tmp/logs
...
$ python main.py --batch-size 128 vgg16 cifar100 test /path/to/net.pth
...
$ CUDA_VISIBLE_DEVICES=-1 python main.py --batch-size 128 vgg16qcfs cifar100 train /tmp/logs

Observations

• With dropout 0.5 VGG16 does not converge on CIFAR100 with learning rate 0.1 and batch size 32.

Results

Abbreviations:

ABBR	MEANING
ACC	Validation accuracy
AUG	Data augmentation strategy
BS	Batch size
DO	Dropout
LR	Learning rate
VER	Version code
WD	Weight decay
PRG	Training in progress
N	Number of epochs

DATE	DATASET	MODEL	VER	BS	AUG	WD	DO	LR	SEED	N	ACC	PRG
2025-02-19	CIFAR10	AlexNet	std	128/8	std	0.0	0.0	0.1	1000	976	94.3	n
	CIFAR10	DenseNet	std	128	std	0.0					94.9	n
	CIFAR10	DenseNet	std	256	std	0.0					94.6	n
	CIFAR10	DenseNet	std	512	std	0.0					93.8	n
2025-02-11	CIFAR10	DenseNet	std	256/8	aa	0.0	0.0		1003	443	96.4	n
	CIFAR10	VGG16	std	?	std	0.0	0.5				93.6	n
	CIFAR10	VGG16	lin-512	?	std	0.0	0.5				92.1	n
	CIFAR10	VGG16	no-BN	?	std	0.0	0.5				92.6	n
2025-02-11	CIFAR100	DenseNet	std	256/8	aa	0.0	0.0		1007	415	78.4	n
2025-02-15	CIFAR100	EffNet-B0	std	256/8	aa	0.0	0.0		1010	201	75.5	n
2025-02-15	CIFAR100	EffNet-B0	std	512/8	aa	0.0	0.0		1010	516	77.4	n
2025-02-18	CIFAR100	ResNet18	std	128/8	aa	0.0000	0.0	0.10	1000	710	76.8	n
2025-02-11	CIFAR100	ResNet18	std	256/8	aa	0.0	0.0		1004	731	76.7	n
2025-02-14	CIFAR100	ResNet18	std	256/8	aa	0.0004	0.0		1012	908	77.1	n
2025-02-14	CIFAR100	ResNet18	std	256/8	aa	0.0005	0.0		1010	765	77.7	n
2025-02-14	CIFAR100	ResNet18	std	256/8	aa	0.0005	0.0		1011	923	77.7	n
2025-02-14	CIFAR100	ResNet18	std	256/8	aa	0.0010	0.0		1015	637	75.0	n
2025-02-13	CIFAR100	ResNet18	std	512/8	aa	0.0	0.0		1010	636	76.2	n
2025-02-11	CIFAR100	ResNet18	std	512/8	aa	0.0005	0.0		1003	555	77.3	n
2025-02-11	CIFAR100	ResNet18	std	512/8	aa	0.0010	0.0		1010	619	76.8	n
2025-02-12	CIFAR100	ResNet18	std	1024/8	aa	0.0	0.0		1010	572	75.6	n
2025-02-12	CIFAR100	ResNet18	std	1024/8	aa	0.0010	0.0		1010	771	77.2	n
2025-02-12	CIFAR100	ResNet18	std	1024/8	aa	0.0020	0.0		1010	554	74.2	n
	CIFAR100	ResNet50	std	256	aa	0.0					47.8	n
	CIFAR100	ResNet20	std	256	aa	0.0			1001		67.2	n
2024-10-01	CIFAR100	ResNet20	std	512	aa	0.0	0.0		1001		69.1	n
2024-10-01	CIFAR100	ResNet20	std	256	aa	0.0	0.0		1001		69.4	n
2025-02-17	CIFAR100	ResNet20	std	128/8	aa	0.0005	0.0	0.10	1000	953	72.8	n
2025-02-18	CIFAR100	ResNet20-PA	std	128/8	aa	0.0005	0.0	0.05	1000	962	71.6	n
2025-02-18	CIFAR100	ResNet20-PA	std	128/8	aa	0.0005	0.0	0.10	1000	975	71.7	n
2025-02-18	CIFAR100	ResNet20-PA	std	128/8	aa	0.0005	0.0	0.10	1001	739	71.0	n
2024-10-06	CIFAR100	ResNet18QCFS	std	256	aa	0.0005	0.0		1001		79.8	n
2024-10-06	CIFAR100	ResNet18QCFS	std	128	aa	0.0005	0.0		1001		80.3	n
	CIFAR100	VGG16	std	32	aa	0.0	0.0		1001		74.9	n
	CIFAR100	VGG16	std	64	aa	0.0	0.5		1001		69.1	n
	CIFAR100	VGG16	std	64	std	0.0	0.5				71.7	n
	CIFAR100	VGG16	std	128	aa	0.0	0.5		1001		75.4	n
2025-02-01	CIFAR100	VGG16	std	128	aa	0.0005	0.0		1001		77.4	n
	CIFAR100	VGG16	std	256	aa	0.0	0.5				74.7	n
2024-10-02	CIFAR100	VGG16	std	256	aa	0.0005	0.0		1001		77.6	n
2025-02-11	CIFAR100	VGG16	std	256/8	aa	0.0004	0.0		1003	600	74.3	n
	CIFAR100	VGG16	std	256	std	0.0	0.5				70.7	n
	CIFAR100	VGG16	std	512	aa	0.0	0.0		1001		72.9	n
2025-02-10	CIFAR100	VGG16	std	512/8	aa	0.0005	0.0		1001		73.4	n
	CIFAR100	VGG16QCFS	std	128	aa	0.0	0.0		1001		53.9	n
	CIFAR100	VGG16QCFS	std	256	aa	0.0	0.5		1001		72.0	n
2025-03-11	CIFAR100	VGG16QCFS	std	256/8	aa	0.0005	0.0	0.05	999	984	76.1	n
2025-02-17	CIFAR100	VGG16QCFS	std	256/8	aa	0.0005	0.0	0.05	1010	963	76.0	n
2025-02-17	CIFAR100	VGG16QCFS	std	256/8	aa	0.0005	0.0	0.10	1010	966	76.4	n
2025-02-17	CIFAR100	VGG16QCFS	std	256/8	aa	0.0005	0.0	0.20	1010	703	72.6	n