From 590722294c4a806e2c3a439af37937a11e1795d9 Mon Sep 17 00:00:00 2001 From: TE-KrishnaWadhwani <50579838+TE-KrishnaWadhwani@users.noreply.github.com> Date: Tue, 14 May 2019 12:52:24 +0900 Subject: [PATCH] Update tutorial_training.md --- object-detection/yolov2/tutorial/tutorial_training.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/object-detection/yolov2/tutorial/tutorial_training.md b/object-detection/yolov2/tutorial/tutorial_training.md index 49bd7ddb..b0c174e1 100644 --- a/object-detection/yolov2/tutorial/tutorial_training.md +++ b/object-detection/yolov2/tutorial/tutorial_training.md @@ -10,7 +10,7 @@ This tutorial will cover the following four topics: - `train.py` will be mainly used for this purpose. 3. Run image object detection **using the trained parameters**. - `yolov2_detection.py` will be mainly used for this purpose. - - This is mostly the same as [Quick Start: Image Object Detection with YOLO-v2-NNabla](./quickstart.md), until the final step, where the trained network weights are used instead of the pretrained network weights. + - This is mostly the same as [Quick Start: Image Object Detection with YOLO-v2-NNabla](../quickstart.md), until the final step, where the trained network weights are used instead of the pretrained network weights. 4. **Evaluate the network's mAP** (Mean Average Precision). - Mean Average Precision is the score used for evaluating the image object detection performance in [the original YOLO v2 paper][1]. - `valid.py` and `scripts/voc_eval.py` will be mainly used for this purpose.