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Having trouble in reproducing the result in inference.ipynb #9
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I changed the link for the pretrained model. Could you it download and try again? Also be aware that the notebook uses the groundtruth detections to obtain these results. |
Got same result. I did use the groundtruth detections to obtain these results. |
the result i got for mot16-02 is |
@iamZe did you solve this issue? am stuck here as well |
I pushed a fix for the problem with the However, I cannot reproduce the problem you are having. I get the same scores as in the notebook. Could you maybe check that you are using the same pytorch (1.3.0) and torch-geomeric ( 1.3.2) versions? |
Yes am using the same version
…On Wed, 8 Sep, 2021, 12:26 am Sven, ***@***.***> wrote:
I pushed a fix for the problem with the inference.py script.
However, I cannot reproduce the problem you are having. I get the same
scores as in the notebook.
Could you maybe check that you are using the same pytorch (1.3.0) and
torch-geomeric ( 1.3.2) versions?
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Also am new to the domain of deep learning. Am learning. Can I ask the order of execution of codes. So first using Mot16 train sequences I do following:
2.I execute python src/gnn_tracker/train.py --dataset_path /data/preprocessed. a. What is the purpose of doing this training on preprocessed data? b. And what is the use of log_dir ? Finally I should execute inference on this training sequence like using command: a. Here this preprocessed folder is from directory generated by preprocessing code above, right? |
Sure! So the steps you are doing seem correct to me. Regarding 2a) 2b) 2c)
where For batch size, you usually want to use as large as possible. Just try different values. It should not affect results too much. I know the implementation is not most memory efficient but 4 should be possible with 6GB memory.
3a) The difference to training is that you do not have access to the groundtruth bounding boxes. So what you do instead is to use a object detection model (here a pre-trained FasterRCNN) to get the detections. And with these detections you then run your trained model. So the setup is: Run the object detection model to get the bounding boxes:
this generates a files with the detections at You can then put these file as Now, we run the pre-processing on the test data (note
And then you can apply your trained model to the test data by using 3b) You do not train the FasterRCNN doing the detections and only (optionally) the ReID CNN providing the visual features but this does not seem to improve results. What you train is the model associating the individual detections over frames into tracks. Maybe it also helps to have a look at Figure 1 in the paper for an overview. Hope this helps! Let me know if you have some other questions. |
i went ahead with python -m src.data_utils.run_obj_detect --model_path /home/rajkumar/repo/GraphNN-Multi-Object-Tracking/faster_rcnn_fpn.model --dataset_path data/MOT16/test --out_path /home/rajkumar/repo/GraphNN-Multi-Object-Tracking/out/detections/MOT16/test --device cuda |
Yeah, my bad. I typed the wrong command. Now what is left is to move the individual .txt files into the respective sequence folder, e.g. |
I tried to reproduce the result of MOT16-02 in inference.ipynb by using the provided pretrained weight. But got this result.
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