Your training set and validation set was photographed after the pixels down, probably hundreds of several hundred pixels * this, image sizes, 300 or so in the training set, is very similar between some images, 66 copies in the validation set. Use labellmg calibration label is as follows:
With Faster - R - CNN Inception v2 prototype configuration file modification, only change the corresponding path, the other parameters are the default values,
Run a python program is min_score_thresh modification to the small 0.00000005 is close to 0% to see bounding box.
Vis_util. Visualize_boxes_and_labels_on_image_array (
Image,
Np. Squeeze (boxes),
Np. Squeeze (classes). Astype (np. Int32),
Np. Squeeze (scores),
Category_index,
Use_normalized_coordinates=True,
Line_thickness=8,
Min_score_thresh=0.0000000005)
Can determine the program execution is no problem, but the suspect could be the threshold value or image training set, because this kind of building fences are together, I can only mark label each time, the surrounding is incomplete building fences, pictured above, training the detector to detect, the results are as follows:
Should also not fitting, loss a loss of image:
Could you tell me how to modify the right can be detected from the orange plastic fence?
CodePudding user response:
Problem solving,Prior to the pre - steeped the model code modification is wrong,
Fine_tune_checkpoint: "C:/Users/z5144967/tensorflow1/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model CKPT"
This sentence before be I commented out!!!! Must retain ah!!!!!!
CodePudding user response: