/Solo

Primary LanguagePython

SOLO: Segmenting Objects by Locations

Implementation of SOLO Networks for Instance Segmentation based on torchvision.

SOLO: Segmenting Objects by Locations, Xinlong Wang, Tao Kong, Chunhua Shen, Yuning Jiang, Lei Li In: Proc. European Conference on Computer Vision (ECCV), 2020 arXiv preprint (arXiv 1912.04488)



TODO

  • Solo Version1 Implement.
  • Training on Coco dataset.
  • Decoupled Solo Version1 Implement.
  • Demo Code implementation



⚙ Getting Start

Directory structure

├─data
│    ├─cocodataset
│         ├─train2017
│         ├─val2017
│         ├─test2017
│         └─annotations
└─workspace
    ├─configs
    ├─scripts
    └─src
        ├─datasets
        ├─eval
        ├─models
        ├─modules
        │    ├─backbone
        │    ├─head
        │    ├─neck
        │    ├─utils
        │    └─solo_v1.py
        └─utils



Config

  • You can set various experimental environments in configs/experiments/*.yaml and configs/models/*.yaml
experiment:
  name: 'Solo_r50_fpn_3x'
  group: "v1"
  log_model: true
  wandb: true
  log_step: true

model: configs/models/solo_v1.yaml

general:
  seed: 822
  gpus: [0]
  epoch: 36
  precision: 32
  data_root: ''
  trn_img_size:
    height: 512
    width: 512
  val_img_size:
    height: 512
    width: 512

  mean: [0.485, 0.456, 0.406]
  std:  [0.229, 0.224, 0.225]

  save_top_k: 5
  save_interval: 1

optim:
  name: 'SGD'
  args:
    lr: 0.01
    momentum: 0.9
    weight_decay: 0.0001


grad_clip:
  use: true
  max_norm: 35
  norm_type: 2


sched:
  name: 'MultiStepLR'
  args:
    milestones: [27, 33]
    gamma: 0.1


train_loader:
  image_path: 'cocodataset/train2017'
  annFile: 'cocodataset/annotations/instances_train2017.json'
  batch_size: 8
  num_workers: 8
  shuffle: trues


valid_loader:
  image_path: 'cocodataset/val2017'
  annFile: 'cocodataset/annotations/instances_val2017.json'
  batch_size: 2
  num_workers: 8
  shuffle: false



Reference