Comments (1)
Hi @merlinarer , welcome back.
We did not look into two stage detectors such as Faster RCNN. However, it shouldn't be too hard to adapt to faster RCNN.
For generating images, you will have to modify this script https://github.com/NVlabs/DIODE/blob/yolo/deepinversion_yolo.py. The model will have to be changed to a Faster-RCNN and the task_loss
will change to the detection loss for a faster-rcnn model. All other losses such as image prior losses and deep feature loss will remain the same.
For distillation, here is a paper that performs knowledge distillation for Faster RCNN models: https://arxiv.org/abs/1906.03609
from diode.
Related Issues (17)
- Bad results of generating images of KITTI dataset HOT 8
- [GCS] Access to directory DIODE_data HOT 3
- [GCS]need access to data HOT 3
- Does this method used for a two-stage object detector? HOT 2
- [GCS] access to data HOT 2
- Image variance collapses to zero HOT 1
- About L2 & TV loss, mean value HOT 1
- Question about the pretrained model. HOT 1
- Training time consumption HOT 1
- ERROR occurs when doing distillation!! help!
- [GCS]Access to data
- Confusion about the one box dataset HOT 4
- Same content in label files on bdd100k dataset HOT 2
- Regarding training using BDD100K dataset HOT 9
- Re: Self-similarity hook HOT 3
- [IMPORTANT][GCS] Access to Google's cloud storage bucket
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from diode.