Comments (1)
Hi @amirhmk, out of the box, I don't think the repo comes with that ability. However, you can provide empty dummy ground truth with your custom data and run inference. This would potentially enable you to do what you wanted without fiddling around much with the detectron framework. Also, I think the repo saves prediction's visualization as images in compressed .npz format (compresses the image array). You can potentially reconstruct the images from the .npz array and visualize them with opencv or PIL
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Related Issues (20)
- Interesting HOT 1
- question about allocentric_to_egocentric HOT 3
- How to calc 'MEAN_DEPTH_PER_LEVEL' and 'STD_DEPTH_PER_LEVEL' HOT 4
- RuntimeError: cannot perform reduction function argmax on a tensor with no elements because the operation does not have an identity HOT 1
- when load pretrained model of VoVNet99, missing key in state_dict HOT 1
- model trained on NuScenes HOT 1
- Box pose transfomation from camera 2 to velodyne may not be correct.
- where to change the train class num 5 to 3
- Guidance on Pre training
- Multi-node training
- Generating Validation Folder HOT 1
- Please generate a precompiled docker image
- Can release DD3DV2 code?
- why dd3d_kitti_omninets.yaml feature_extractors use DLA34
- Omninets weights Some model parameters or buffers are not found HOT 2
- omninets train result is very low HOT 5
- OmniML trained weights link not working HOT 1
- I can't reproduce the result of Kitti Dataset.
- AttributeError: 'DD3D' object has no attribute 'export'
- Unable to get visualize_dataloader.py to run due to kitti access
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