hellow，author. I want to train this model in full-data，but I just have a rtx3090.It just has 24GB memory.How to set the hyperparameters to get the best result? Thank you.

Dear author, Should I put the JSON file of the UAV dataset in the same level directory of the uav123 python file? And, where is the the json files of UAV123 datasets .Could you share it? Thanks a lot!

Thanks for your work again. I encountered a problem while using your network . The feature map output by your network neck is 1717, so six 33 convolutional layers can still meet the requirements of the receptive field. If the size of the input picture increases and the feature map increases, and the tracking target itself is relatively large, can it be possible to expand the receptive field by stacking 3*3 convolutional layers? Or is there any better solution?

Hi, author. Thanks for your nice work. I excute offline training for the got10k benchmark. To be specific, I set the number of samples per epoch to 150,000 and the mini-batch size to 15, other settings are same with your code. The whole training phase takes about 1.5 days with one NVIDIA Tesla V100 GPU. But, the evaluation looks bad, Do you meets this problem? or do you have some suggestions.

Dear Author,I have read your paper carefully, but I am a beginner of visual tracking area. I want to test the pre-trained model. But when I run the command

python main/test.py --config experiments/stmtrack/test/got10k/stmtrack-googlenet-got.yaml

I meet

Exception: Dataset /STMTrack/datasets/GOT-10k/test/list.txt not found or corrupted.

But I have download the got-10k dataset of your link https://drive.google.com/file/d/17wQ9lvEa4jLhv72TZatw03EHUx9UgTcA/view?usp=sharing and then it come many files when I unzip it How can I solve this problem?Thanks

Thank you for your excellent work. I read your paper and found an obvious typo. In the last paragraph of section 3.3 ，STM's citation number should be [37] and GraghMemVOS should be [30]. Another question I want to know is whether dividing the features extracted by keys and values will affect the detection of occluded objects. What should I do if I want the bbox to contain the occluded part？

when I test in VOT, it worked fine at first, but failed after some tests.Here are some information for when something goes wrong.

2021-12-12 05:07:47.807 | INFO | videoanalyst.engine.tester.tester_impl.vot:track_single_video:296 - (59) Video: zebrafish1 Time: 2.5s Speed: 151.2fps Lost: 5 2%|█▍ | 1/60 [28:21<27:53:19, 1701.68s/it]2021-12-12 05:07:47.808 | INFO | videoanalyst.engine.tester.tester_impl.vot:run_tracker:147 - Total Lost: 120 2021-12-12 05:07:47.808 | INFO | videoanalyst.engine.tester.tester_impl.vot:run_tracker:148 - Mean Speed: 176.03 FPS loading VOT2018: 0%| | 0/4 [00:00<?, ?it/s, homepage] Traceback (most recent call last): | 0/4 [00:00<?, ?it/s, homepage] File "main/test.py", line 77, in tester.test() File "/home/hkj/Desktop/video_analyst/videoanalyst/engine/tester/tester_impl/vot.py", line 89, in test test_result_dict = self.evaluation() File "/home/hkj/Desktop/video_analyst/videoanalyst/engine/tester/tester_impl/vot.py", line 191, in evaluation dataset = vot_benchmark.VOTDataset( File "/home/hkj/Desktop/video_analyst/videoanalyst/evaluation/vot_benchmark/pysot/datasets/vot.py", line 113, in init video, dataset_root, meta_data[video]['video_dir'], TypeError: string indices must be integers 100%|███████████████████████████████████████████████████████████████████████████████████████| 60/60 [28:21<00:00, 28.36s/it]

Hi, can I add your WeChat to have a deeper talk on this work? Currently, I am extending this tracker into dual-modality version. My wechat is wangxiao5791509. Thanks.

作者你好，我跑了您的代码，发现一些代码中缺少了x_size这一个参数（比如RandomCropTransformer这个类下面的default_hyper_params，DenseboxTarget这个类下同样缺失），导致在train函数中root_cfg.merge_from_file(exp_cfg_path)出现错误，请问这些地方的x_size应该设为多少？

Hi, thanks for sharing your code. I find a bug on visualization: line 361 and line 370. 'frame_idx' -> 'cur_frame_idx'.

    if self._hp_visualization:
        score1 = tensor_to_numpy(score[0])[:, 0]
        vsm.visualize(score1, self._hp_score_size, im_q_crop, self._state['cur_frame_idx'], 'raw_score')

Thanks for your excellent work. I meet a problem when I want to reproduce the results in the paper. When I used GOT alone for training, the iou suppression remained at about 0.38 and there was no improvement. I wonder if there was something wrong with the configuration?

May I ask if you have compared the performance difference between Pixel-wise Correlation and Depth-wise Correlation in the process of the experiment？

And their time-consuming?