:Modify the configuration files located in ./experiments/dff_rfcn/cfgs . Use a standard setup like resnet_v1_101_flownet_imagenet_vid_rfcn_end2end_ohem.yaml for high-performance detection.
): The model runs a full forward pass through the feature network ( Nfeatcap N sub f e a t end-sub ) to get feature maps A lightweight FlowNet ( Nflowcap N sub f l o w end-sub ) calculates the displacement field ( Mi→kcap M sub i right arrow k end-sub ) between the current frame and the last keyframe. 0guogcfcb4q156ug2eqlg_source.mp4
:Clone the repository and install dependencies including MXNet. Ensure you have the ResNet-101 and FlowNet pretrained models. :Modify the configuration files located in
Does this video belong to a specific like ImageNet VID, or are you looking to implement this on a custom real-time stream ? python demo
python demo.py --cfg experiments/dff_rfcn/cfgs/resnet_v1_101_flownet_imagenet_vid_rfcn_end2end_ohem.yaml --video 0guogcfcb4q156ug2eqlg_source.mp4 Use code with caution. Copied to clipboard Feature Extraction Logic Keyframes ( Ikcap I sub k