感谢你非常棒的工作《LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search》，但是网上好像现在找不到这篇论文，可以给个论文的下载链接吗?另外，可以尽快公布代码吗？

The paper is similar to siamfc++, except that the network layer is changed. Why is it so robust that it is better than online updates, such as dimp. How does this offline tracking tracker distinguish distractor?

downloaded from https://www.votchallenge.net/vot2019/dataset.html

VOT2019 dataset preview

but did not have list.txt

FileNotFoundError: [Errno 2] No such file or directory: 'LightTrack/lib/utils/../../dataset/VOT2019/list.txt'

what exactly is this path? path_name=back_04502514044521042540+cls_211000022+reg_100000111_ops_32

On running: bash tracking/reproduce_vot2019.sh

===> init Siamese <==== load pretrained model from snapshot/LightTrackM/LightTrackM.pth Traceback (most recent call last): File "tracking/test_lighttrack.py", line 181, in main() File "tracking/test_lighttrack.py", line 165, in main siam_net = load_pretrain(siam_net, args.resume) File "/home/nishat/adasi/tracking/LightTrack/tracking/../lib/utils/utils.py", line 702, in load_pretrain ckpt = torch.load(ckpt_path, map_location=lambda storage, loc: storage(device))
NameError: name 'ckpt_path' is not defined VOT2019 loading VOT2019: 100%|██████████████████████████████████| 60/60 [00:00<00:00, 61.31it/s, zebrafish1] eval ar: 100%|████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15.58it/s] eval eao: 0%| | 0/1 [00:00<?, ?it/s] multiprocessing.pool.RemoteTraceback: """ Traceback (most recent call last): File "/home/nishat/miniconda3/envs/lighttrack/lib/python3.6/multiprocessing/pool.py", line 119, in worker result = (True, func(*args, **kwds)) File "/home/nishat/adasi/tracking/LightTrack/lib/eval_toolkit/bin/../../eval_toolkit/pysot/evaluation/eao_benchmark.py", line 47, in eval eao = self._calculate_eao(tracker_name, self.tags) File "/home/nishat/adasi/tracking/LightTrack/lib/eval_toolkit/bin/../../eval_toolkit/pysot/evaluation/eao_benchmark.py", line 110, in _calculate_eao max_len = max([len(x) for x in all_overlaps]) ValueError: max() arg is an empty sequence """

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "lib/eval_toolkit/bin/eval.py", line 148, in trackers), desc='eval eao', total=len(trackers), ncols=100): File "/home/nishat/miniconda3/envs/lighttrack/lib/python3.6/site-packages/tqdm/std.py", line 1178, in iter for obj in iterable: File "/home/nishat/miniconda3/envs/lighttrack/lib/python3.6/multiprocessing/pool.py", line 735, in next raise value ValueError: max() arg is an empty sequence

Hello ,your work is very wonderful and testing in my video achieved a good performance. One frame inference time is about 13ms and ocean is about 15ms in 2080Ti. How to raise the fps? Thanks for your open-source codes and methods and look forward your prompt reply.

I noticed that the method of cross-correlation is different from the general siam tracker. In the code, the matrix multiplication method is used as the pixel cross-correlation, and the channel separable convolution method is generally used as the channel cross-correlation. What are the advantages of using pixel correlation? Looking forward to your reply, thank you very much!

After testing your work on UAV123 dataset and evaluating the result with OPE benchmark the result is have very low accuracy Is there any wrong in your code or paper .....

Hi, i read the paper again, in section 4.3, it confuse me that how can you make sure that the best backbone architecture(path) can be sampled in the pre-training stage and the sampled path is different from the previous paths? Since there are about 78 billion possible backbones(paths), and i think it's impossible to pre-train them all and maybe the actual best backbone(path) was not been sampled, so what if this situation was really happend during your expirement? Thanks, looking for your reply.

Hi, it's a very interesting and excellent work, and I am a beginner in NAS, I'm very interesting in the pre-training of backbone supernet, but I didn't find this part in your released code, did you not release this part or it's just my fault? Thanks, looking for your reply.