GETTING_STARTED.md

Getting started with VMFormer

Data Preparation

Foreground Dataset

• VideoMatte240K
• ImageMatte

Background Dataset

• BG20k
• DVM

Composited Testing Set

• Low-resolution Google Drive
• High-resolution Google Drive

The way the data are composited following RVM practice.

Training

We conducted all experiments on 8xA6000 GPUs. To train VMFormer with 8 GPUs, run:

GPUS_PER_NODE=8 ./tools/run_dist_launch.sh 8 ./configs/mv3_vmformer.sh

Inference & Evaluation

Evaluating VMFormer on the low-resoution composited testing set:

CUDA_VISIBLE_DEVICES=0 python inference_vm.py --model_path path/to/model_weights --masks --num_frames 20 --img_path path/to/vmformer_512x288_public --query_temporal weight_sum --fpn_temporal

Evaluating VMFormer on the high-resoution composited testing set:

CUDA_VISIBLE_DEVICES=0 python inference_vm.py --model_path path/to/model_weights --masks --num_frames 5 --img_path path/to/vmformer_1920x1080_public --query_temporal weight_sum --fpn_temporal

Evaluating VMFormer on the RVM low-resoution testing set:

CUDA_VISIBLE_DEVICES=0 python inference_rvm.py --model_path path/to/model_weights --masks --num_frames 20 --img_path path/to/rvm_512x288_public --query_temporal weight_sum --fpn_temporal

Evaluating VMFormer on the RVM high-resoution testing set:

CUDA_VISIBLE_DEVICES=0 python inference_rvm.py --model_path path/to/model_weights --masks --num_frames 20 --img_path path/to/rvm_1920x1080_public --query_temporal weight_sum --fpn_temporal