Beta: Evaluation

Applies to v2.0.0-beta.2. For the stable v1.1.1 procedure, see the User Guides.

Procedure

Run the YORU’s Evaluation sub-module.
Load a project config.yaml file and a model.
Changed in Beta 2: the model is in the exp_<model>/ folder, not train/.
- YOLO / RT-DETR: <project>/exp_yolo11s/weights/best.pt
- torchvision: <project>/exp_fasterrcnn/fasterrcnn_best.pt
Repeat training runs go to exp_yolo11s2, exp_yolo11s3, … so make sure you pick the run you mean.
Extract frames for labeling using Grab GUI.

I. Select a video in the Video file path in the Grab GUI.

Ⅱ. Select Save directory. (Basically, all_label_images in the project folder is a good choice.)

Ⅲ. Decide the grabbed frame name.

IV. Cut out the screenshot.

i. Play video with Streaming movie.

ii. Arrow keys to go forward and back.

iii. Grab Current Frame or Alt key to save frame.

Images that are not used for creating a model are better.

Changed in Beta 2: Left / Right / Alt only fire when the YORU window has focus, and grabbing before an output folder has been chosen no longer crashes.
Run LabelImg and label the frames.

The detailed documents are accessible in LabelImg.

Save format is done in YOLO.

It is easier to do so if Auto Save mode is turned on in the View tab.
Push the “Prediction” button.
Push the “Calculate APs” button.

YORU calculates APs and IOUs.

A divide-by-zero in the evaluation IoU was fixed.
Quit no longer raises in the Evaluation and Create-Labels windows.
A SciPy function that was removed in modern versions is no longer used, so evaluation works on current SciPy.
yoru.libs.file_operation_evaluation was removed — it was a duplicate of yoru.libs.file_operation_create_label. This only matters if you import YORU from your own scripts.
Detection thresholds are uniform across backends (confidence 0.25, IoU 0.45). AP and IoU numbers from Beta 2 are therefore not directly comparable with numbers produced under Beta 1, especially for torchvision models.