is it possible to use your trained model just for detection purpose ?

[ifzhang]

is it possible to use your trained model just for detection purpose ?

[pedromoraesh]

Of course, you can use it. The detection part is the base for good tracking performance. In this case, they aim to use almost every detection box to improve the tracking step. With this in mind, you can use the model just for detection without sending the boxes to the tracker.

[ssbilakeri]

Thank you for your response. I have used your trained model on mix dataset as a detector with the deepsort tracker. Though it is not related to ByteTracker . would you find any reason for the below traceback. please help me to fix it Traceback (most recent call last): File "/content/drive/MyDrive/working_code_on_backbone_yolox/evaluation_singlcam.py", line 293, in <module> det.detect() File "/content/drive/MyDrive/working_code_on_backbone_yolox/evaluation_singlcam.py", line 183, in detect outputs = self.deepsort.update(bbox_xywh, scores, img0) File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep_sort.py", line 30, in update features = self._get_features(bbox_xywh, ori_img) File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep_sort.py", line 111, in _get_features features = self.extractor(im_crops) File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep/feature_extractor.py", line 44, in __call__ im_batch = self._preprocess(im_crops) File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep/feature_extractor.py", line 38, in _preprocess im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float() File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep/feature_extractor.py", line 38, in <listcomp> im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float() File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep/feature_extractor.py", line 36, in _resize return cv2.resize(im.astype(np.float32)/255., size) cv2.error: OpenCV(4.1.2) /io/opencv/modules/imgproc/src/resize.cpp:3720: error: (-215:Assertion failed) !ssize.empty() in function 'resize'

…

On Thu, Nov 11, 2021 at 2:30 AM Pedro H. de Moraes ***@***.***> wrote: Of course, you can use it. The detection part is the base for good tracking performance. In this case, they aim to use almost every detection box to improve the tracking step. With this in mind, you can use the model just for detection without sending the boxes to the tracker. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#61 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AOOYJBIVZCRFHFF3KAXSYJTULLMPBANCNFSM5HUI6HOQ> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

[ssbilakeri]

for imagePath in imagePaths: # load the image, pre-process it, and store it in the data list print(imagePath) #here I have printed the imagename image = cv2.imread(imagePath) image = cv2.resize(image, (28, 28)) # 28, 28 image = img_to_array(image) data.append(image) if(type(image) == type(None)): passelse: image = cv2.resize(image, (h, w), interpolation=cv2.INTER_AREA) import cv2 image = cv2.imread('noexist.jpg')try: resize = cv2.resize(image, (64,64))except cv2.error as e: print('Invalid frame!') cv2.waitKey() On Thu, Nov 11, 2021 at 11:06 AM Shavantrevva Bilakeri ***@***.***> wrote:

…

Thank you for your response. I have used your trained model on mix dataset as a detector with the deepsort tracker. Though it is not related to ByteTracker . would you find any reason for the below traceback. please help me to fix it Traceback (most recent call last): File "/content/drive/MyDrive/working_code_on_backbone_yolox/evaluation_singlcam.py", line 293, in <module> det.detect() File "/content/drive/MyDrive/working_code_on_backbone_yolox/evaluation_singlcam.py", line 183, in detect outputs = self.deepsort.update(bbox_xywh, scores, img0) File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep_sort.py", line 30, in update features = self._get_features(bbox_xywh, ori_img) File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep_sort.py", line 111, in _get_features features = self.extractor(im_crops) File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep/feature_extractor.py", line 44, in __call__ im_batch = self._preprocess(im_crops) File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep/feature_extractor.py", line 38, in _preprocess im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float() File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep/feature_extractor.py", line 38, in <listcomp> im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float() File "/content/drive/MyDrive/working_code_on_backbone_yolox/deep_sort/deep_sort/deep/feature_extractor.py", line 36, in _resize return cv2.resize(im.astype(np.float32)/255., size) cv2.error: OpenCV(4.1.2) /io/opencv/modules/imgproc/src/resize.cpp:3720: error: (-215:Assertion failed) !ssize.empty() in function 'resize' On Thu, Nov 11, 2021 at 2:30 AM Pedro H. de Moraes < ***@***.***> wrote: > Of course, you can use it. The detection part is the base for good > tracking performance. In this case, they aim to use almost every detection > box to improve the tracking step. With this in mind, you can use the model > just for detection without sending the boxes to the tracker. > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <#61 (comment)>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AOOYJBIVZCRFHFF3KAXSYJTULLMPBANCNFSM5HUI6HOQ> > . > Triage notifications on the go with GitHub Mobile for iOS > <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> > or Android > <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. > >

[zxchang-1]

I had the same problem，I have used his trained model on mix dataset as a detector with the
deepsort tracker too. I wonder if you have solved your problem

Traceback (most recent call last):
File "C:/Users/zxc/Desktop/ByteTrack-main/tools/demo_track.py", line 558, in
main(exp, args)
File "C:/Users/zxc/Desktop/ByteTrack-main/tools/demo_track.py", line 549, in main
image_demo_deepsort2(predictor, vis_folder, current_time, args)
File "C:/Users/zxc/Desktop/ByteTrack-main/tools/demo_track.py", line 378, in image_demo_deepsort2
outputs = deepsort.update((torch.Tensor(bbox_xywh)), (torch.Tensor(confs)), img_info['raw_img'])
File "C:\Users\zxc\Desktop\ByteTrack-main\deep_sort\deep_sort.py", line 29, in update
features = self._get_features(bbox_xywh, ori_img)
File "C:\Users\zxc\Desktop\ByteTrack-main\deep_sort\deep_sort.py", line 99, in _get_features
features = self.extractor(im_crops)
File "C:\Users\zxc\Desktop\ByteTrack-main\deep_sort\deep\feature_extractor.py", line 45, in call
im_batch = self._preprocess(im_crops)
File "C:\Users\zxc\Desktop\ByteTrack-main\deep_sort\deep\feature_extractor.py", line 40, in _preprocess
im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float()
File "C:\Users\zxc\Desktop\ByteTrack-main\deep_sort\deep\feature_extractor.py", line 40, in
im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float()
File "C:\Users\zxc\Desktop\ByteTrack-main\deep_sort\deep\feature_extractor.py", line 37, in _resize
a=cv2.resize(im.astype(np.float32)/255., size)
cv2.error: OpenCV(4.6.0) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\resize.cpp:4052: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize'