search

dvlab-research / PanopticFCN

Fully Convolutional Networks for Panoptic Segmentation (CVPR2021 Oral)
Apache License 2.0
391 stars 53 forks source link

About R101 corresponding configs and trained models #24

Closed ShihuaHuang95 closed 3 years ago

ShihuaHuang95 commented 3 years ago

Many thanks for this great work. I am wondering if you have any plan to release the R101 corresponding configs and trained models.

ShihuaHuang95 commented 3 years ago

I have tried out the following config and just got PQ 45.6.

MODEL: META_ARCHITECTURE: "PanopticFCN" WEIGHTS: detectron2://ImageNetPretrained/MSRA/R-101.pkl MASK_ON: True PIXEL_MEAN: [123.675, 116.28, 103.53] PIXEL_STD: [1.0, 1.0, 1.0] RESNETS: DEPTH: 101 OUT_FEATURES: ["res2", "res3", "res4", "res5"] FPN: IN_FEATURES: ["res2", "res3", "res4", "res5"] DATASETS: TRAIN: ("coco_2017_train_panoptic_separated",) TEST: ("coco_2017_val_panoptic_separated",) DATALOADER: FILTER_EMPTY_ANNOTATIONS: True SOLVER: BASE_LR: 0.01 WEIGHT_DECAY: 1e-4 LR_SCHEDULER_NAME: "WarmupPolyLR" POLY_LR_POWER: 0.9 WARMUP_ITERS: 1000 WARMUP_FACTOR: 0.001 WARMUP_METHOD: "linear" CLIP_GRADIENTS: ENABLED: True CLIP_VALUE: 35.0 IMS_PER_BATCH: 16 MAX_ITER: 270000 CHECKPOINT_PERIOD: 10000 INPUT: MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) MIN_SIZE_TRAIN_SAMPLING: "choice" MIN_SIZE_TEST: 800 MAX_SIZE_TRAIN: 1333 MAX_SIZE_TEST: 1333 MASK_FORMAT: "bitmask" VERSION: 2

OUTPUT_DIR: "./PanopticFCN_r101_3x_FAM"

yanwei-li commented 3 years ago

Hi, Thanks for your interest. The config and performance seem right and normal.