Comments (2)
Fantastic, this works well. Thanks!
from review_object_detection_metrics.
Hi @matt-sharp ,
In images with many bounding boxes, the class names would be overlapping each other. So, we prefer not to include the names.
But you can easily have this feature by changing one single line in the code.
Change line 120 at details.py, which reads:
img = add_bb_into_image(img, bb, color=(0, 255, 0), thickness=2, label=None)
to
img = add_bb_into_image(img, bb, color=(0, 255, 0), thickness=2, label=bb._class_id)
Best regards,
Rafael
from review_object_detection_metrics.
Related Issues (20)
- Equation (9) and (11) in publication HOT 3
- how to use commond line ? HOT 1
- Do you have the detection results of different detection models in coco json format. HOT 1
- could't open detected the annotation file made by yolov4 darknet HOT 2
- How to calculate the general Precision x recall curve of the detection model? HOT 1
- Coordinates format has repeated option HOT 4
- Can you share commands on how to run the coco and PascalVOC evaluators via terminal to generate the outputs? HOT 3
- No results HOT 4
- Feature request: Docker containerization HOT 3
- move(self, int, int): argument 1 has unexpected type 'float' HOT 5
- ModuleNotFoundError: No module named 'src' HOT 4
- Bug in the converter.py while looking for images folder ? HOT 1
- AttributeError: 'list' object has no attribute 'items' HOT 1
- Please provide command line to evaluate on terminal HOT 1
- Issue during start: TypeError: arguments did not match any overloaded call HOT 3
- np.bool is deprecated HOT 5
- numpy: set_window_title moved to manager HOT 3
- Building the conda environment causes an endless loop HOT 2
- Result is 0 HOT 3
- Getting precision, recall, F1 values HOT 2
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