GIou_loss_caffe
Caffe version Generalized Iou loss and Complete Iou loss Implementation for Faster RCNN/FPN bbox regression. (https://arxiv.org/abs/1902.09630) (https://github.com/generalized-iou) (https://arxiv.org/abs/1911.08287) (https://github.com/Zzh-tju/DIoU)
Usage
caffe.proto
optional GIouLossParameter giou_loss_param = 1490;
message GIouLossParameter {
optional float x_std = 1 [default = 0.1]; // BBOX_NORMALIZE_STDS
optional float y_std = 2 [default = 0.1];
optional float w_std = 3 [default = 0.2];
optional float h_std = 4 [default = 0.2];
optional float clip_bound = 5 [default = 4.1352]; // log(1000/ 16)
optional bool clip = 6 [default = true]; // Clip bounding box transformation predictions to prevent exp() from overflowing
}
train.prototxt
Simply replace
layer {
bottom: "bbox_pred"
bottom: "proposal_targets"
bottom: "box_inside_weights"
bottom: "box_outside_weights"
top: "loss_bbox"
name: "loss_bbox"
type: "SmoothL1Loss"
loss_weight: 1
propagate_down: 1
propagate_down: 0
propagate_down: 0
propagate_down: 0
}
By
layer {
bottom: "bbox_pred"
bottom: "proposal_targets"
bottom: "box_inside_weights"
bottom: "box_outside_weights"
top: "loss_bbox"
name: "loss_bbox"
type: "GIouLoss"
giou_loss_param{
clip: true # or false
x_std: 0.1 # __C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2) in py-faster-rcnn
y_std: 0.1
w_std: 0.2
h_std: 0.2
}
loss_weight: 10
propagate_down: 1
propagate_down: 0
propagate_down: 0
propagate_down: 0
}
COCO2017 validation Results
Baseline model (SmoothL1Loss)
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.290
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.458
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.311
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.320
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.271
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.388
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.394
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.202
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.437
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.540
Current model (GIouLoss)
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.292
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.460
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.308
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.126
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.328
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.275
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.401
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.408
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.462
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.553
more experiments
TODO