From 0de07da61287f764cce440986ddf8ff6d4d3a8ff Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 13 Mar 2020 11:03:39 -0700 Subject: [PATCH] updates --- README.md | 2 +- utils/utils.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3119e277..20042ff8 100755 --- a/README.md +++ b/README.md @@ -174,7 +174,7 @@ Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memo Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.649 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.735 -Speed: 6.6/1.6/8.2 ms inference/NMS/total per 608x608 image at batch-size 32 +Speed: 6.6/1.5/8.1 ms inference/NMS/total per 608x608 image at batch-size 32 ``` # Reproduce Our Results diff --git a/utils/utils.py b/utils/utils.py index 5b9d4b43..01f25878 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -325,7 +325,7 @@ def box_iou(boxes1, boxes2): lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] - inter = (rb - lt).clamp(min=0).prod(2) # [N,M] + inter = (rb - lt).clamp(0).prod(2) # [N,M] return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter)