Results Modeldetect Image Verbose1. Say you are training a CV model to recognize features in cars. Mask_RCNNmodelpy in unmold_detectionsself detections mrcnn_mask image_shape window 2275 for i in rangeN.
Now divide the total images into two sets training and validation and the ratio of training and validation should be around 8020. Chung ta co the thay djuoc mask cua cac djoi tuong lam sao. Passed one image to.
75 of them are used for training and 25 of them are used for validation.
Run detection results modeldetectimage verbose1 Visualize results r results0 visualizedisplay_instancesimage rrois rmasks rclass_ids class_names rscores That la tuyet voi mo hinh cua ta dja lam viec rat tot. Confidence scores for each box class_names. Chung ta co the thay djuoc mask cua cac djoi tuong lam sao. Now we test the model on some images.