BICLab / SpikeYOLO

Offical implementation of "Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection" (ECCV2024 Best Paper Candidate / Oral)
https://arxiv.org/abs/2407.20708
GNU Affero General Public License v3.0
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Time window for coco dataset training and inference and time window and dataloader use for GEN1 #19

Open gwgknudayanga opened 1 week ago

gwgknudayanga commented 1 week ago
XinhaoLuo666 commented 1 week ago

1,3. We re-uploaded coco.yaml,you can try it again

  1. you can change the time window in cfg/model/snn_yolov8.yaml [MS_GetT]
  2. Time window of Gen1 is same as the paper reported. Gen1 dataset will upload a few weeks later
  3. There is no function in the code to convert an integer into a pulse train, as this does not provide inference acceleration or power consumption on the GPU. But it's easy to implement, for example, you just need to turn a 3 into 3 1s
gwgknudayanga commented 1 week ago

@XinhaoLuo666, Regarding the GEN1 dataset evaluation, I organize each sample of the dataset to 5 time steps. each time step has 2 channels. And then i feed these to the network and trained your network . But the maximum mAP@50 that i could obtained in validation set is 36.1%. (I used horizontal,vertical as data augmentations. ). Don't know why i am getting these low values compared to the reported values in the paper.

XinhaoLuo666 commented 6 days ago

For different T's, do you guarantee that their data-enhanced images are the same? Assuming that the image remains as it is in the first frame and the third image is flipped left and right, the model's ability to model the timing task will be significantly damaged

gwgknudayanga commented 6 days ago

@XinhaoLuo666 In GEN1 dataset there are no images and it's only the events, isn't it? I feed the direct events of a sample organizing them into a tensor of dimension (5,16,2,640,640) which are (T,N,C,H,W). So any augmentation happens to the whole tensor at once and hence similar to all 5 time steps in similar manner. I don't why the mAP@50 is 36.1% even after 200 iterations. It is better you can upload the code for GEN1 dataloading with guidelines/READme and also with sample weights file so that it is easy to reproduce the results and proced on top of it.

Thank you.

feekyzz commented 3 days ago

GEN1数据集如何使用呀,有没有哥们教一教我!