This is an unofficial pytorch implementation of MaskRCNN instance aware segmentation as described in Mask R-CNN by Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick
tqdm
pyyaml
numpy
opencv-python
pycocotools
torch >= 1.5
torchvision >=0.6.0
we trained this repo on 4 GPUs with batch size 16(4 image per node).the total epoch is 24(about 180k iter),Adam with cosine lr decay is used for optimizing. finally, this repo achieves 39.0 mAP(box) 33.7mAP(seg) at 736px(max thresh) resolution with resnet50 backbone.(about 23.64)
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.390
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.598
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.419
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.214
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.436
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.322
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.508
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.534
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.330
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.585
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.705Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.337
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.557
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.353
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.128
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.371
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.288
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.443
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.462
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.516
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.651for now we only support coco data.
- modify main.py (modify config file path)
from solver.ddp_mix_solver import DDPMixSolver
if __name__ == '__main__':
processor = DDPMixSolver(cfg_path="config/maskrcnn.yaml")
processor.run()- custom some parameters in config.yaml
model_name: mask_rcnn
data:
train_annotation_path: data/coco/annotations/instances_train2017.json
# train_annotation_path: data/coco/annotations/instances_val2017.json
val_annotation_path: data/coco/annotations/instances_val2017.json
train_img_root: data/coco/train2017
# train_img_root: data/coco/val2017
val_img_root: data/coco/val2017
max_thresh: 768
use_crowd: False
batch_size: 4
num_workers: 2
debug: False
remove_blank: Ture
model:
num_cls: 80
backbone: resnet50
pretrained: True
reduction: False
fpn_channel: 256
fpn_bias: True
anchor_sizes: [32.0, 64.0, 128.0, 256.0, 512.0]
anchor_scales: [1.0, ]
anchor_ratios: [0.5, 1.0, 2.0]
strides: [4.0, 8.0, 16.0, 32.0, 64.0]
box_score_thresh: 0.05
box_nms_thresh: 0.5
box_detections_per_img: 100
optim:
optimizer: Adam
lr: 0.0001
milestones: [24,]
warm_up_epoch: 0
weight_decay: 0.0001
epochs: 24
sync_bn: True
amp: True
val:
interval: 1
weight_path: weights
gpus: 0,1,2,3detailed settings reference to nets.mask_rcnn.default_cfg
- run train scripts
nohup python -m torch.distributed.launch --nproc_per_node=4 main.py >>train.log 2>&1 &- Color Jitter
- Perspective Transform
- Mosaic Augment
- MixUp Augment
- IOU GIOU DIOU CIOU
- Warming UP
- Cosine Lr Decay
- EMA(Exponential Moving Average)
- Mixed Precision Training (supported by apex)
- Sync Batch Normalize
- PANet(neck)
- BiFPN(EfficientDet neck)
- VOC data train\test scripts
- custom data train\test scripts
- MobileNet Backbone support