Comments (2)
AlvaroCavalcante
commented on June 7, 2024
Hello @tullydwyer, glad to know that you liked the tool! In theory, it's pretty easy to set the confidence level which you want to detect the labels, you just need to pass the "min_score_thresh" parameter to the "visualize_boxes_and_labels_on_image_array" method. This parameter is set by default in 0.3 or 0.5 depending on the TensorFlow version.
You can find this parameter in line 39 of the file "detection_img_tf2.py". Just change the value for whatever you want between 0 and 1.
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tullydwyer
commented on June 7, 2024
Thanks @AlvaroCavalcante! How did that not click. I am very new to TensorFlow.
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Related Issues (20)
- readme.md
- bug
- documentation
- duplicate
- enhancement
- Error parsing message HOT 5
- KeyError: "The name 'image_tensor:0' refers to a Tensor which does not exist. The operation, 'image_tensor', does not exist in the graph." HOT 4
- How to get pretrained model and what is it trained on? HOT 2
- XML result path dynamically HOT 2
- Running detection_img_tf2.py [...] resulting in AttributeError: ... 'cv2' has no attribute HOT 3
- visualize_boxes_and_labels_on_image_array() got an unexpected keyword argument 'file_name' HOT 4
- tensorflow.python.framework.errors_impl.NotFoundError: /path-label-map.pbtxt; No such file or directory HOT 3
- How to set the dimensions for the bounding box? HOT 6
- Version issue within the setup.py HOT 19
- Create issue template HOT 4
- Question: I am trying to auto_annotate images using google collab but getting this error. HOT 2
- using yolov5 model HOT 2
- Could not find matching concrete function to call loaded from the SavedModel HOT 2
- Annotations are not generated HOT 3
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