Comments (6)
Referenced and explained in #12
Short answer: it does support training with images with empty negative samples, but it very likely will not increase accuracy.
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Referenced and explained in #12
Short answer: it does support training with images with empty negative samples, but it very likely will not increase accuracy.
So I should copy target object to empty background image,that be changed to positive sample?
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I think what you're trying to describe is called Synthetic data - and yes, that could be good approach to increase the accuracy.
Have a look at this article:
https://www.hackster.io/dmitrywat/synthetic-data-generation-for-object-detection-e0f023
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Now, the two class target objects is cxtstence , one is a positive sample, but another object is negative samples, They don't exist in the same image,But I just train and detect only one class object.
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Sorry, I'm not following -
Word "sample" refers to images. You can say you have 1000 samples, which would mean you have 1000 images. The images(samples) can contain multiple instances of the same class or multiple instances of different classes.
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emm, I mean motorbike and bike 2 classes objects, but I only annotate label bike for single class object to train(positive sample)。
I don't want annotate label these voc-xml of motorbike for train(negative samples), but these motorbike and bike look is very similar!
Usually these bikes and motorbikes are not in the same image of dataset!
So, I want just add these no annotation label voc-xml of motorbike to train(object element is not exist), that can improve recall rate or AP ?
voc-xml of motorbike example(a negative sample):<annotation> <folder>VOC2007</folder> <filename>fyb004595.jpg</filename> <source> <database>Unknown</database> </source> <size> <width>600</width> <height>775</height> <depth>3</depth> </size> <segmented>0</segmented> </annotation>
The VOC API have supported the format of XML.
Yes, it is possible to train with empty samples (not containing any instances of object of interest). According to at least one evidence #12 that does not increase accuracy.
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Related Issues (20)
- 是否支持把没有目标的空背景和标签输入训练? HOT 2
- Can the aXeleRate support the format? HOT 1
- k210 segnet HOT 1
- json.decoder.JSONDecodeError: Expecting ',' delimiter: line 35 column 2 (char 1325) HOT 5
- Run in Sipeed Maix Bit HOT 6
- Cannot convert tf to onnx in object detection HOT 2
- Training person detector with pascal_20_detection dataset error HOT 9
- tensorflow 2.5 error! HOT 1
- Support for custom input_size no work in kmodel HOT 4
- [unstable branch] validation frequency adding to config.json HOT 1
- [unstable branch] yolo training quantize or not can be switched by config.json HOT 1
- [unstable branch] yolo k210 converter failed while nncase compile and YOLOv3 convert incorrectly HOT 1
- Threads Error HOT 1
- The loss is not converge when training detector on VOC 2012 HOT 2
- When class names more than one, the mAP is false? HOT 5
- How calculate anchors by kmeans? HOT 2
- Multi Object Detect Is Wrong! HOT 2
- Loading kmodel on MaixPy crashing device HOT 25
- ValueError: Invalid value for argument filters. Expected a strictly positive value. Received filters=0. HOT 2
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