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Link of the model zoo--https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md Link of the model zip--http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28.tar.gz

OS : win 10 64 opencv version : 4.1.2, installed by anaconda

Using the opencv dnn module to perform object detection by the tensorflow model. But the results seems weird.

Steps to reproduce

1 : Generate the config file by tf_text_graph_faster_rcnn.py

python tf_text_graph_faster_rcnn.py --input frozen_inference_graph.pb --config pipeline.config --output faster_rcnn_inception_resnet_v2_atrous_oid.pbtxt

2 : detect object by the example codes

import cv2 as cv

cvNet = cv.dnn.readNetFromTensorflow('tensorflow/faster_rcnn_inception_resnet_v2_atrous_oid.pb', 'tensorflow/faster_rcnn_inception_resnet_v2_atrous_oid.pbtxt')

img = cv.imread('traffic_jam.jpg')
rows = img.shape[0]
cols = img.shape[1]
cvNet.setInput(cv.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))
cvOut = cvNet.forward()

for detection in cvOut[0,0,:,:]:
    score = float(detection[2])
    if score > 0.1:
        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows
        cv.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (23, 230, 210), thickness=2)

cv.imshow('img', img)
cv.waitKey()

1. The results

Input image

results

Almost every cars cannot detected by this network, I have tried to change the size of image, but results become worse in this case. How should I fix this issue?Thanks

• Link of the model zoo--https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
  • Link of the model zip(using faster_rcnn_inception_resnet_v2_atrous_oidv2)--http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28.tar.gz
  • OS : win 10 64
  • opencv version : 4.1.2, installed by anaconda

Using the opencv dnn module to perform object detection by the tensorflow model. But the results seems weird.

Steps to reproduce

1 : Generate the config file by tf_text_graph_faster_rcnn.py

python tf_text_graph_faster_rcnn.py --input frozen_inference_graph.pb --config pipeline.config --output faster_rcnn_inception_resnet_v2_atrous_oid.pbtxt

frozen_inference_graph.pb and pipeline.config are the files after unzip

2 : detect object by the example codes

import cv2 as cv

cvNet = cv.dnn.readNetFromTensorflow('tensorflow/faster_rcnn_inception_resnet_v2_atrous_oid.pb', 'tensorflow/faster_rcnn_inception_resnet_v2_atrous_oid.pbtxt')

img = cv.imread('traffic_jam.jpg')
rows = img.shape[0]
cols = img.shape[1]
cvNet.setInput(cv.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))
cvOut = cvNet.forward()

for detection in cvOut[0,0,:,:]:
    score = float(detection[2])
    if score > 0.1:
        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows
        cv.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (23, 230, 210), thickness=2)

cv.imshow('img', img)
cv.waitKey()

1. The results

Input image

results

Almost every cars cannot detected by this network, I have tried to change the size of image, but results become worse in this case. How should I fix this issue?Thanks

• Link of the model zoo--https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
  • Link of the model zip(using faster_rcnn_inception_resnet_v2_atrous_oidv2)--http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28.tar.gz
  • OS : win 10 64
  • opencv version : 4.1.2, installed by anaconda

Using the opencv dnn module to perform object detection by the tensorflow model. But the results seems weird.

Steps to reproduce

1 : Generate the config file by tf_text_graph_faster_rcnn.py

python tf_text_graph_faster_rcnn.py --input frozen_inference_graph.pb --config pipeline.config --output faster_rcnn_inception_resnet_v2_atrous_oid.pbtxt

frozen_inference_graph.pb and pipeline.config are the files after unzip

2 : detect object by the example codes

import cv2 as cv

cvNet = cv.dnn.readNetFromTensorflow('tensorflow/faster_rcnn_inception_resnet_v2_atrous_oid.pb', 'tensorflow/faster_rcnn_inception_resnet_v2_atrous_oid.pbtxt')

img = cv.imread('traffic_jam.jpg')
rows = img.shape[0]
cols = img.shape[1]
cvNet.setInput(cv.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))
cvOut = cvNet.forward()

for detection in cvOut[0,0,:,:]:
    score = float(detection[2])
    if score > 0.1:
        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows
        cv.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (23, 230, 210), thickness=2)

cv.imshow('img', img)
cv.waitKey()

1. The results

Input image

results

Almost every cars cannot detected by this network, I have tried to change the size of image, but results become worse in this case. How should I fix this issue?Thanks

Using the opencv dnn module to perform object detection by the tensorflow model. But the results seems weird.

Steps to reproduce

1 : Generate the config file by tf_text_graph_faster_rcnn.py

python tf_text_graph_faster_rcnn.py --input frozen_inference_graph.pb --config pipeline.config --output faster_rcnn_inception_resnet_v2_atrous_oid.pbtxt

frozen_inference_graph.pb and pipeline.config are the files after unzip

2 : detect object by the example codes

import cv2 as cv

cvNet = cv.dnn.readNetFromTensorflow('tensorflow/faster_rcnn_inception_resnet_v2_atrous_oid.pb', 'tensorflow/faster_rcnn_inception_resnet_v2_atrous_oid.pbtxt')

img = cv.imread('traffic_jam.jpg')
rows = img.shape[0]
cols = img.shape[1]
cvNet.setInput(cv.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))
cvOut = cvNet.forward()

for detection in cvOut[0,0,:,:]:
    score = float(detection[2])
    if score > 0.1:
        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows
        cv.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (23, 230, 210), thickness=2)

cv.imshow('img', img)
cv.waitKey()

1. The results

Input image

results

Almost every cars cannot detected by this network, I have tried to change the size of image, but results become worse in this case. How should I fix this issue?Thanks

Edit : If I use the tensorflow api(1.15) to do the object detection(example on this page)the results seems much better, although there are many vehicles cannot detected.

Link of the model zoo-- - Link of the download model zoo--https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md - Link of the model zip(using faster_rcnn_inception_resnet_v2_atrous_oidv2)--http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28.tar.gz - zip

• OS : win 10 64 - 64
• opencv version : 4.1.2, installed by anaconda

Using the opencv dnn module to perform object detection by the tensorflow model. But the results seems weird.

Steps to reproduce

1 : Generate the config file by tf_text_graph_faster_rcnn.py

python tf_text_graph_faster_rcnn.py --input frozen_inference_graph.pb --config pipeline.config --output faster_rcnn_inception_resnet_v2_atrous_oid.pbtxt

frozen_inference_graph.pb and pipeline.config are the files after unzip

2 : detect object by the example codes

import cv2 as cv

cvNet = cv.dnn.readNetFromTensorflow('tensorflow/faster_rcnn_inception_resnet_v2_atrous_oid.pb', 'tensorflow/faster_rcnn_inception_resnet_v2_atrous_oid.pbtxt')

img = cv.imread('traffic_jam.jpg')
rows = img.shape[0]
cols = img.shape[1]
cvNet.setInput(cv.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))
cvOut = cvNet.forward()

for detection in cvOut[0,0,:,:]:
    score = float(detection[2])
    if score > 0.1:
        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows
        cv.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (23, 230, 210), thickness=2)

cv.imshow('img', img)
cv.waitKey()

1. The results

Input image

results

Almost every cars cannot detected by this network, I have tried to change the size of image, but results become worse in this case. How should I fix this issue?Thanks

Edit : If I use the tensorflow api(1.15) to do the object detection(example on this page)the results seems much better, although there are many vehicles cannot detected.