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Thank you for providing your implementations. After getting this repo to run a few error occured.
Both the pretrained baseline model faster_rcnn_1_10_3960.pth and a model I trained on UAVDT yields mAP=0 for bird view. The overall mAP also is wrong (for almost all the categories). Could you shortly tell me, how I have to set
as there are different version that are commented out. If I just take the uncommented rows it yields an key error. (f.ex. should I choose them as so: self._angle_to_ind = {'front-side-view': 0, 'front-view': 1, 'side-view': 0, 'bird-view': 2} ?)
Furthermore, you said that you were gonna "discard the foggy class". Does that mean you don't train and test on images labeled as foggy? If I include the foggy class and assign it to "day", the mAP is considerably worse.
Thank you!
The text was updated successfully, but these errors were encountered:
Hi Ben, I think your evaluation is wrong. Before you quantitively evaluate the model, you can run a qualitative evaluation on the testing images by visualizing the bounding boxes.
Also, we didn't discard the foggy videos. We relabel them as day or night.
Thank you very much for your reply. I managed to get it working.
Just a few follow-up question if you don't mind:
-did you use the original image size (i.e. 1024 x 520) or did you use the configuration large_scale (effectively upscaling the image to aprox. 1300x800)? And is there a change in configuration for training and testing, respectively?
-did you use any data augmentation (such as horizontally flipping)?
Thank you for providing your implementations. After getting this repo to run a few error occured.
Both the pretrained baseline model faster_rcnn_1_10_3960.pth and a model I trained on UAVDT yields mAP=0 for bird view. The overall mAP also is wrong (for almost all the categories). Could you shortly tell me, how I have to set
self._angles,
self._altitudes,
self._weathers
and
self._weather_to_ind,
self._altitude_to_ind,
self._angle_to_ind
as there are different version that are commented out. If I just take the uncommented rows it yields an key error. (f.ex. should I choose them as so: self._angle_to_ind = {'front-side-view': 0, 'front-view': 1, 'side-view': 0, 'bird-view': 2} ?)
Furthermore, you said that you were gonna "discard the foggy class". Does that mean you don't train and test on images labeled as foggy? If I include the foggy class and assign it to "day", the mAP is considerably worse.
Thank you!
The text was updated successfully, but these errors were encountered: