hu64 / ffavod Goto Github PK
View Code? Open in Web Editor NEWRepository for the paper FFAVOD: Feature Fusion Architecture for Video Object Detection
License: MIT License
Repository for the paper FFAVOD: Feature Fusion Architecture for Video Object Detection
License: MIT License
First of all, thank you for your help, I have solved the previous problem.
However, new problems have emerged. I used centernet as my baseline to train on this data set, using coco indicators, but I found that the indicators change very strangely during training. Loss decreases with training, but the accuracy of the network on the test set gets worse with training, which is far from the results in your paper. I did not use the cneternet you used, but I am sure that there is no problem with my network, and it can get normal results on the coco data set.
I used the ignore.txt file of the dataset to process the data, because I think that unlabeled instances may cause instability in network training. I don't know what kind of treatment you have done?
I currently have no way to get a relatively normal baseline indicator. It makes it difficult for me to continue my work. Do you have any good suggestions?
For small goals, have you made improvements to the official code (it does not seem to be from the paper)?
What index did you use during training, VOC or COCO?
Originally posted by @Bin-ze in #2 (comment)
I downloaded the UAVDT data set and evaluation tool, but I did not find out how to use it to generate test results from the tool. I did a lot of searches, and there are no similar tutorials on the Internet. I searched the original paper and did not find the author. Contact information.
But I really need the same evaluation criteria as the open source papers to evaluate the quality of my network. You published your paper with this data set. I wonder if you can send me the testing tools you use. I want to use this to evaluate your own network.
Hi,
I have a hard time reproducing the results reported in your repo/paper using the provided pre-trained models. On the UAVDT test dataset (using the same split you shared /val-1-on-30.json), I attempted to evaluate the shared per-trained model (spotnet2_vid_uavdt.pth), but I was not able to obtain the same mAP as reported in the paper.
Is there anything else I could do to reproduce the results.
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