@@ -46,6 +48,10 @@ You can turn on it by setting `flip=True` in the `test_pipeline`.
Due to the scale and measurements of depth is different from those of other regression targets, we first train the model with depth weight equal to 0.2 for a more stable training procedure. For a stronger detector with better performance, please finetune the model with depth weight changed to 1.0 as shown in the [config](./fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune.py). Note that the path of `load_from` needs to be changed to yours accordingly.
### Visualizing prediction results
We also provide visualization functions to show the monocular 3D detection results. Simply follow the [documentation](https://mmdetection3d.readthedocs.io/en/latest/1_exist_data_model.html#test-existing-models-on-standard-datasets) and use the `single-gpu testing` command. You only need to add the `--show` flag and specify `--show-dir` to store the visualization results.