Script for training high-performance SNNs based on back propagation
This is an example of training high-performance SNNs using the braincog. It is able to train high performance SNNs on CIFAR10, DVS-CIFAR10, ImageNet and other datasets, and reach the advanced level.
Install braincog
git clone https://github.com/xxx/Brain-Cog.git
cd braincog
python setup install --user
Examples of training
cd examples/img_cls/bp
python main.py --model dvs_convnet --node-type LIFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun QGate --device 0
Benchmark
We provide a benchmark of SNNs trained with braincog and the corresponding scripts. This provides an open, fair platform for comparison of subsequent SNNs on classification tasks.
Note: The results may vary due to random seeding and software version issues.
CIFAR10
| ID | Dataset | Node-type | Config | Model | Batch Size | Accuracy | Script |
|---|---|---|---|---|---|---|---|
| 1 | CIFAR10 | IF+Atan | - | convnet | 128 | 95.54 | python main.py --model cifar_convnet --node-type IFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR10 | LIF+Atan | - | convnet | 128 | 91.92 | python main.py --model cifar_convnet --node-type LIFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR10 | PLIF+Atan | - | convert | 128 | 93.32 | python main.py --model cifar_convnet --node-type PLIFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR10 | IF+Atan | - | resnet18 | 128 | 89.76/89.80 | python main.py --model resnet18 --node-type IFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR10 | LIF+Atan | - | resnet18 | 128 | 89.93/89.88 | python main.py --model resnet18 --node-type LIFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR10 | PLIF+Atan | - | resnet18 | 128 | 92.64/ 90.65 | python main.py --model resnet18 --node-type PLIFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR10 | IF+QGate | - | dvs_convnet | 128 | 95.73 | python main.py --model cifar_convnet --node-type IFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | CIFAR10 | LIF+QGate | - | dvs_convnet | 128 | 96.04 | python main.py --model cifar_convnet --node-type LIFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | CIFAR10 | PLIF+QGate | - | dvs_convnet | 128 | 96.04/95.84 | python main.py --model cifar_convnet --node-type PLIFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | CIFAR10 | IF+QGate | - | resnet18 | 128 | 89.19 | python main.py --model resnet18 --node-type IFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | CIFAR10 | LIF+QGate | - | resnet18 | 128 | 90.95/90.68 | python main.py --model resnet18 --node-type LIFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | CIFAR10 | PLIF+QGate | - | resnet18 | 128 | 90.97/91.02 | python main.py --model resnet18 --node-type PLIFNode --dataset cifar10 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
CIFAR100
| ID | Dataset | Node-type | Config | Model | Batch Size | Accuracy | Script |
|---|---|---|---|---|---|---|---|
| 1 | CIFAR100 | IF+Atan | - | dvs_convnet | 128 | 76.52 | python main.py --num-classes 100 --model cifar_convnet --node-type IFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR100 | LIF+Atan | - | dvs_convnet | 128 | 71.89 | python main.py --num-classes 100 --model cifar_convnet --node-type LIFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR100 | PLIF+Atan | - | dvs_convnet | 128 | 72.82 | python main.py --num-classes 100 --model cifar_convnet --node-type PLIFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR100 | IF+Atan | - | resnet18 | 128 | 62.47 | python main.py --num-classes 100 --model resnet18 --node-type IFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR100 | LIF+Atan | - | resnet18 | 128 | 62.63 | python main.py --num-classes 100 --model resnet18 --node-type LIFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR100 | PLIF+Atan | - | resnet18 | 128 | 62.71 | python main.py --num-classes 100 --model resnet18 --node-type PLIFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | CIFAR100 | IF+QGate | - | dvs_convnet | 128 | 76.44 | python main.py --num-classes 100 --model cifar_convnet --node-type IFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | CIFAR100 | LIF+QGate | - | dvs_convnet | 128 | 77.73 | python main.py --num-classes 100 --model cifar_convnet --node-type LIFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | CIFAR100 | PLIF+QGate | - | dvs_convnet | 128 | 77.25 | python main.py --num-classes 100 --model cifar_convnet --node-type PLIFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | CIFAR100 | IF+QGate | - | resnet18 | 128 | 60.01 | python main.py --num-classes 100 --model resnet18 --node-type IFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | CIFAR100 | LIF+QGate | - | resnet18 | 128 | 61.33 | python main.py --num-classes 100 --model resnet18 --node-type LIFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | CIFAR100 | PLIF+QGate | - | resnet18 | 128 | 62.32 | python main.py --num-classes 100 --model resnet18 --node-type PLIFNode --dataset cifar100 --step 4 --batch-size 128 --act-fun QGateGrad --device 0 |
DVS-CIFAR10
| ID | Dataset | Node-type | Config | Model | Batch Size | FLOPS | Accuracy | Script |
|---|---|---|---|---|---|---|---|---|
| 1 | DVS-CIFAR10 | IF+Atan | - | dvs_convnet | 128 | 7503 | 65.90 | python main.py --model dvs_convnet --node-type IFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-CIFAR10 | LIF+Atan | - | dvs_convnet | 128 | 7503 | 82.10 | python main.py --model dvs_convnet --node-type LIFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-CIFAR10 | PLIF+Atan | - | dvs_convnet | 128 | 7503 | 81.90 | python main.py --model dvs_convnet --node-type PLIFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-CIFAR10 | IF+Atan | - | resnet18 | 128 | 3149 | 69.10 | python main.py --model resnet18 --node-type IFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-CIFAR10 | LIF+Atan | - | resnet18 | 128 | 3149 | 78.50 | python main.py --model resnet18 --node-type LIFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-CIFAR10 | PLIF+Atan | - | resnet18 | 128 | 3149 | 77.70 | python main.py --model resnet18 --node-type PLIFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-CIFAR10 | IF+QGate | - | dvs_convnet | 128 | 7503 | 68.30 | python main.py --model dvs_convnet --node-type IFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | DVS-CIFAR10 | LIF+QGate | - | dvs_convnet | 128 | 7503 | 82.60/82.90 | python main.py --model dvs_convnet --node-type LIFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | DVS-CIFAR10 | PLIF+QGate | - | dvs_convnet | 128 | 7503 | 83.20 | python main.py --model dvs_convnet --node-type PLIFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | DVS-CIFAR10 | IF+QGate | - | resnet18 | 128 | 3149 | 65.70/66.80 | python main.py --model resnet18 --node-type IFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | DVS-CIFAR10 | LIF+QGate | - | resnet18 | 128 | 3149 | 79.00/79.40 | python main.py --model resnet18 --node-type LIFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | DVS-CIFAR10 | PLIF+QGate | - | resnet18 | 128 | 3149 | 78.10/78.20 | python main.py --model resnet18 --node-type PLIFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
DVS-Gesture
| ID | Dataset | Node-type | Config | Model | Batch Size | Accuracy | Script |
|---|---|---|---|---|---|---|---|
| 1 | DVS-G | IF+Atan | - | dvs_convnet | 128 | 64.77 | python main.py --num-classes 11 --model dvs_convnet --node-type IFNode --dataset dvsg --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-G | LIF+Atan | - | dvs_convnet | 128 | 91.28 | python main.py --num-classes 11 --model dvs_convnet --node-type LIFNode --dataset dvsg --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-G | PLIF+Atan | - | dvs_convnet | 128 | 91.67 | python main.py --num-classes 11 --model dvs_convnet --node-type PLIFNode --dataset dvsg --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-G | IF+Atan | - | resnet18 | 128 | 63.25 | python main.py --num-classes 11 --model resnet18 --node-type IFNode --dataset dvsg --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-G | LIF+Atan | - | resnet18 | 128 | 91.29 | python main.py --num-classes 11 --model resnet18 --node-type LIFNode --dataset dvsg --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-G | PLIF+Atan | - | resnet18 | 128 | 90.15 | python main.py --num-classes 11 --model resnet18 --node-type PLIFNode --dataset dvsg --step 10 --batch-size 128 --act-fun AtanGrad --device 0 |
| 1 | DVS-G | IF+QGate | - | dvs_convnet | 128 | 48.48 | python main.py --num-classes 11 --model dvs_convnet --node-type IFNode --dataset dvsg --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | DVS-G | LIF+QGate | - | dvs_convnet | 128 | 92.05/92.42 | python main.py --num-classes 11 --model dvs_convnet --node-type LIFNode --dataset dvsg --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | DVS-G | PLIF+QGate | - | dvs_convnet | 128 | 91.28 | python main.py --num-classes 11 --model dvs_convnet --node-type PLIFNode --dataset dvsg --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | DVS-G | IF+QGate | - | resnet18 | 128 | 57.95 | python main.py --num-classes 11 --model resnet18 --node-type IFNode --dataset dvsg --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | DVS-G | LIF+QGate | - | resnet18 | 128 | 90.91 | python main.py --num-classes 11 --model resnet18 --node-type LIFNode --dataset dvsg --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | DVS-G | PLIF+QGate | - | resnet18 | 128 | 92.42 | python main.py --num-classes 11 --model resnet18 --node-type PLIFNode --dataset dvsg --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
NCALTECH101
| ID | Dataset | Node-type | Config | Model | Batch Size | Accuracy | Script |
|---|---|---|---|---|---|---|---|
| 1 | NCALTECH101 | IF+QGate | - | dvs_convnet | 128 | 23.09/51.15 | python main.py --num-classes 100 --model dvs_convnet --node-type IFNode --dataset NCALTECH101 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | NCALTECH101 | LIF+QGate | - | dvs_convnet | 128 | 72.78/75.09 | python main.py --num-classes 100 --model dvs_convnet --node-type LIFNode --dataset NCALTECH101 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | NCALTECH101 | PLIF+QGate | - | dvs_convnet | 128 | 74.61/76.79 | python main.py --num-classes 100 --model dvs_convnet --node-type PLIFNode --dataset NCALTECH101 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | NCALTECH101 | IF+QGate | -/mix | resnet18 | 128 | 61.24/60.87 | python main.py --num-classes 100 --model resnet18 --node-type IFNode --dataset NCALTECH101 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | NCALTECH101 | LIF+QGate | -/mix | resnet18 | 128 | 66.22/70.84 | python main.py --num-classes 100 --model resnet18 --node-type LIFNode --dataset NCALTECH101 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
| 1 | NCALTECH101 | PLIF+QGate | -/mix | resnet18 | 128 | 69.62/69.87 | python main.py --num-classes 100 --model resnet18 --node-type PLIFNode --dataset NCALTECH101 --step 10 --batch-size 128 --act-fun QGateGrad --device 0 |
Note:
resnet18 is used here by adding a maximum pooling after the initial convolution layer. However, in the final version of braincog, we remove this pooling layer.
mix refers to the use of EventMix as a data augmentation method.
We will continue to add other results.
Citation
If you find this package helpful, please consider citing it:
@software{name,
author = {xxx},
title = {braincog: xxx},
month = jul,
year = 2022,
note = {{Documentation available under
https://xxx.readthedocs.io}},
publisher = {Zenodo},
version = {xxx},
doi = {xxx},
url = {xxx}
}