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Time window for coco dataset training and inference and time window and dataloader use for GEN1 #19
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1,3. We re-uploaded coco.yaml,you can try it again |
@XinhaoLuo666, |
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 |
@XinhaoLuo666 Thank you. |
GEN1数据集如何使用呀,有没有哥们教一教我! |
Hi @XinhaoLuo666, Is the class for preprocessing and loading of GEN1 event data to SpikeYOLO available to download now? |
We have uploaded a new folder "SpikeYOLO_for_Gen1" that can be used for training and inference directly on the GEN1 dataset |
We have uploaded a new folder "SpikeYOLO_for_Gen1" that can be used for training and inference directly on the GEN1 dataset |
Thank you. I just had a look. It seems even for the GEN1 data , the same augmentations for image-based data are used. |
No, we didn't use augmentation when working with neuromorphic data, he did execute the "RandomPerspective tranformation" function, but this function has been internally altered to remove the augmentation part |
coco.yaml is missing
I just run the code for coco dataset. it seems the time window is always 1. According to your mem_update logic in this case it doesn't experience any voltage membrane decay and it just send the value of the received data clamp to 4 as maximum. So what is the time window that you have used to make it behave like spiking neurons?
Even with that running i am getting following error when running.
File "/home/atiye/SpikeYOLO/ultralytics/models/yolo/detect/val.py", line 146, in get_stats
self.nt_per_class = np.bincount(stats[-1].astype(int), minlength=self.nc) # number of targets per class
IndexError: list index out of range
Another question is, how many time bins/time window you have used to organize the GEN1 dvs data? Can you please provide the code dataloader code for GEN1 as then the spiking neurons shows the membrane decay based real spiking neuron behavior?
Also since mem_update is used for both train and validation it cannot see how binary spikes (1,0) are available during inference as mentioned in paper
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