Awesome

FIERY

This is the PyTorch implementation for inference and training of the future prediction bird's-eye view network as described in:

FIERY: Future Instance Segmentation in Bird's-Eye view from Surround Monocular Cameras

Anthony Hu, Zak Murez, Nikhil Mohan, Sofía Dudas, Jeffrey Hawke, ‪Vijay Badrinarayanan, Roberto Cipolla and Alex Kendall

ICCV 2021 (Oral) Blog post

<img src="https://github.com/wayveai/fiery/releases/download/v1.0/predictions.gif" alt="FIERY future prediction"> Multimodal future predictions by our bird’s-eye view network. Top two rows: RGB camera inputs. The predicted future trajectories and segmentations are projected to the ground plane in the images. Bottom row: future instance prediction in bird’s-eye view in a 100m×100m capture size around the ego-vehicle, which is indicated by a black rectangle in the center.

If you find our work useful, please consider citing:

@inproceedings{fiery2021,
  title     = {{FIERY}: Future Instance Segmentation in Bird's-Eye view from Surround Monocular Cameras},
  author    = {Anthony Hu and Zak Murez and Nikhil Mohan and Sofía Dudas and 
               Jeffrey Hawke and Vijay Badrinarayanan and Roberto Cipolla and Alex Kendall},
  booktitle = {Proceedings of the International Conference on Computer Vision ({ICCV})},
  year = {2021}
}

⚙ Setup

Create the conda environment by running conda env create.

🏄 Prediction

Visualisation

In a colab notebook:

Or locally:

Download pre-trained weights.
Run python visualise.py --checkpoint ${CHECKPOINT_PATH}. This will render predictions from the network and save them to an output_vis folder.

Evaluation

Download the NuScenes dataset. For detailed instructions, see DATASET.md.
Download pre-trained weights.
Run python evaluate.py --checkpoint ${CHECKPOINT_PATH} --dataroot ${NUSCENES_DATAROOT}.

🔥 Pre-trained models

All the configs are in the folder fiery/configs

Config and weights	Dataset	Past context	Future horizon	BEV size	IoU	VPQ
`baseline.yml`	NuScenes	1.0s	2.0s	100mx100m (50cm res.)	36.7	29.9
`lyft/baseline.yml`	Lyft	0.8s	2.0s	100mx100m (50cm res.)	36.3	29.2
`literature/static_pon_setting.yml`	NuScenes	0.0s	0.0s	100mx50m (25cm res.)	37.7	-
`literature/pon_setting.yml`	NuScenes	1.0s	0.0s	100mx50m (25cm res.)	39.9	-
`literature/static_lss_setting.yml`	NuScenes	0.0s	0.0s	100mx100m (50cm res.)	35.8	-
`literature/lift_splat_setting.yml`	NuScenes	1.0s	0.0s	100mx100m (50cm res.)	38.2	-
`literature/fishing_setting.yml`	NuScenes	1.0s	2.0s	32.0mx19.2m (10cm res.)	57.6	-

🏊 Training

To train the model from scratch on NuScenes:

Download the NuScenes dataset. For detailed instructions, see DATASET.md.
Run python train.py --config fiery/configs/baseline.yml DATASET.DATAROOT ${NUSCENES_DATAROOT}.

This will train the model on 4 GPUs, each with a batch of size 3. To train on single GPU add the flag GPUS 1, and to change the batch size use the flag BATCHSIZE ${DESIRED_BATCHSIZE}.

🙌 Credits

Big thanks to Giulio D'Ippolito (@gdippolito) for the technical help on the gpu servers, Piotr Sokólski (@pyetras) for the panoptic metric implementation, and to Hannes Liik (@hannesliik) for the awesome future trajectory visualisation on the ground plane.