Greetings,

I am currently using qdtrack in python 3.8, Cuda 11 based environment for RTX 3090 GPU. While training as well as testing, I am getting this error. All required datasets in required locations have been downloaded and maintained. Any help would be appreciated.

Hi there.

I ran the pre-trained BDD100K model on the tracking validation set and the resulting MOTA IDF1 scores are lower than what QDTrack claim: MOTA: 54.5, IDF1: 66.7 vs your MOTA: 63.5, IDF1 71.5.

Kindly verify if this is the case for you or if there are any missing settings.

I followed the instructions and ran this command: sh ./tools/dist_test.sh ./configs/bdd100k/qdtrack-frcnn_r50_fpn_12e_bdd100k.py ./ckpts/mmdet/qdtrack_frcnn_r50_fpn_12e_bdd100k_13328aed.pth 2 --out exp.pkl --eval track

Hello,

When I train the model with your code for TAO (i.e., pretrain on LVIS and finetune on TAO-train), I get the following final results on TAO-val. which are lower than the scores reported in the original paper.

|mAP0.5 | mAP0.75 | mAP[0.5:0.95] | |---------|---------|---------| |13.8 | 5.5 | 6.5 | | 16.1 | 5.0 | 7.0 |

• above : reproduced // below : original

Are there any issues that I have to consider for getting the original score?

Thanks,

Thanks for the great work!

I am trying to retrain QDTrack on BDD100k, however, it is converging really slowly (at least for the first epochs). Therefore I wanted to ask, whether it is possible to share your diagrams on training loss and acc?

Thanks in advance!

The Readme indicates Detection and Tracking sets, but the site shows 11 options, including: Images, MOT 2020 Labels, MOT 2020 Data, Detection 2020 Labels.

Also, clicking MOT 2020 Data shows many different options. Should they all be downloaded?

when I train the net,Epoch 1 ,200/171305 ,the result as follow: lr: 7.992e-03,loss_rpn_cls: nan, loss_rpn_bbox: nan, loss_cls: nan, acc: 81.8194, loss_bbox: nan, loss_track: nan, loss_track_aux: nan, loss: nan why?

Kindly let me know if customization of backbone as mentioned in mmdet library could be used with qdtrack as well ?

LInk : https://github.com/open-mmlab/mmdetection/blob/master/docs/tutorials/customize_models.md#add-a-new-backbone

This is what you are saying:

On the official download page, the required data and annotations are

detection set images: Images
detection set annotations: Detection 2020 Labels
tracking set images: MOT 2020 Data
tracking set annotations: MOT 2020 Labels

But there is no Images or MOT 2020 Data on the official website for BDD

Hi , thanks for your great work. According to the paper, There is an auliliary loss, I do not really understand the intuition of this loss.

Can you give me some more explanation of this loss? Thanks.

Thank you for your paper and this repo! I would like to test your pretrained model on the BDD100k dataset. Therefore I followed the instructions (https://github.com/SysCV/qdtrack/blob/master/docs/GET_STARTED.md) - downloaded BDD100k, converted annotations as described and stored everything as your folder structure suggests.

I used 'single-gpu testing' in the chapter 'Test a Model' and executed the following command in the terminal: python tools/test.py ${QDTrack}/configs/qdtrack-frcnn_r50_fpn_12e_bdd100k.py ${QDTrack}/pretrained_models/qdtrack-frcnn_r50_fpn_12e_bdd100k-13328aed.pth --out testrun_01.pkl --eval track --show-dir ${QDTrack}/data/results

${QDTrack} = indicates the path on my machine to qdtrack

I get the following error:

Could you please help me solving this issue. Thanks a lot!

Thanks for your great work! I'm now trying to run qdtrack on MOT17. I find the detection part went well during training and reached a reasonable mAP score. But, the loss of the quasi-dense embedding part degraded fastly to zero within 100 iterations, and obtained very low MOTA, MOTP, IDF1, etc., after training. Note that I modified nothing except the code related to dataset, which I've checked carefully thus I believe is not the reason. Should I modify the settings of quasi-dense embedding head to make it work? Do you have any suggestions? Thank you very much!

Hi.

I'm going to freeze the parameters of detector as you say(https://github.com/SysCV/qdtrack/issues/126).

In qdtrack/models/mot/qdtrack.py, I tried to freeze the detector using freeze_detector(freeze_detector = True). But, when freeze_detector = True, self.detector, I got this error.

Traceback (most recent call last): File "tools/train.py", line 169, in main() File "tools/train.py", line 140, in main test_cfg=cfg.get('test_cfg')) File "/workspace/qdtrack/qdtrack/models/builder.py", line 15, in build_model return build(cfg, MODELS, dict(train_cfg=train_cfg, test_cfg=test_cfg)) File "/workspace/mmcv/mmcv/cnn/builder.py", line 27, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/workspace/mmcv/mmcv/utils/registry.py", line 72, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') AttributeError: QDTrack: 'QDTrack' object has no attribute 'backbone'

Here is the config file I used.

I think, be caused by self.detector.

How can I put the backbone and neck, rpn_head, roi_head.bbox_head of the detector config file(/configs/base/faster_rcnn_r50_fpn.py) in self.detector?

Thank you.

Hi.

I trained the detector using mmcv. And, I want to use the detector checkpoint trained using mmcv for the detector of qdtrack without any additional detector learning. In this case, Can I train only tracker of qdtrack?

If I enter trained checkpoint using mmcv in init_cfg=dict(checkpoint='') in detecotr, is it the same as training only the tracker I mentioned?

Thank you.

Hi,

Thanks for open-sourcing the code of your great work! Looks like there are some bugs when running the current tools/inference.py.

When using the configs/bdd100k/qdtrack-frcnn_r50_fpn_12e_bdd100k.py as config and qdtrack-frcnn_r50_fpn_12e_bdd100k-13328aed.pth as the checkpoint (from the google drive link provided in the README file), the model and loaded state dict do not match exactly. Looks like you updated the name of layers but didn't update the definition of the pre-trained model. Manually changing the layer names in the .pth file will work.

The model and loaded state dict do not match exactly

unexpected key in source state_dict: backbone.conv1.weight, backbone.bn1.weight, backbone.bn1.bias, backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.num_batches_tracked, backbone.layer1.0.conv1.weight, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.num_batches_tracked, backbone.layer1.0.conv2.weight, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.num_batches_tracked, backbone.layer1.0.conv3.weight, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.num_batches_tracked, backbone.layer1.0.downsample.0.weight, backbone.layer1.0.downsample.1.weight, backbone.layer1.0.downsample.1.bias, backbone.layer1.0.downsample.1.running_mean, 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When I try to run this command: python -m bdd100k.label.to_coco -m det -i bdd100k/labels/det_20/det_train.json -o data/bdd/labels/det_20/det_train_cocofmt.json I receive the following error:

[2022-09-23 16:25:55,619 to_coco.py:301 main] Mode: det remove-ignore: False ignore-as-class: False [2022-09-23 16:25:55,619 to_coco.py:307 main] Loading annotations... [2022-09-23 16:26:02,429 to_coco.py:318 main] Converting annotations... 10%|████████ | 6879/69863 [00:00<00:08, 7435.14it/s] Traceback (most recent call last): File "/u/m/c/mcdougall/miniconda3/envs/torch310/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/u/m/c/mcdougall/miniconda3/envs/torch310/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/u/m/c/mcdougall/miniconda3/envs/torch310/lib/python3.10/site-packages/bdd100k-1.0.0-py3.10.egg/bdd100k/label/to_coco.py", line 337, in main() File "/u/m/c/mcdougall/miniconda3/envs/torch310/lib/python3.10/site-packages/bdd100k-1.0.0-py3.10.egg/bdd100k/label/to_coco.py", line 322, in main coco = bdd100k2coco_det( File "/u/m/c/mcdougall/miniconda3/envs/torch310/lib/python3.10/site-packages/bdd100k-1.0.0-py3.10.egg/bdd100k/label/to_coco.py", line 145, in bdd100k2coco_det if frame["labels"]: KeyError: 'labels'

This error does not occur when running with ${SET_NAME} equal to val

In the article (QDTrack), the difference between the key image and the reference image is indicated by the image below.

However, when debugging the training code, I saw that the reference image metadata and key image metadata returned by the data loader are exactly the same.

Do I need to change a parameter before starting training or is this an error in the code? I would be glad if you inform me.