how to prepare correct dataset of imglab for train_object_detector.cpp help

[Jesse Stone]

I want to do cat or dog train_object_detector

1. https://stackoverflow.com/questions/30326560/object-detection-with-dlib-library
   the object occupy 90% of image to train
   seems fail

2. https://its401.com/article/wyd1520/81585293
   using image include many object and crop object to train
   seems work ok

////////////////////
https://www.kaggle.com/competitions/dogs-vs-cats/data?select=train.zip
If I get this dataset that the object occupy 90% of image, does it mean failed detect like method 1. Do I need use method 2

[Jesse Stone]

After trace into dlib source

virtual void separation_oracle
{
//goto void detect_from_fhog_pyramid
//Do det(rect) predict , then score sorting, the first is biggest socre
scanner.detect(current_solution, dets, thresh-loss_per_false_alarm)

//find match det(rect)
const std::pair<double,unsigned int> truth = find_best_match(truth_object_detections[idx], dets[i].second);
final_dets.push_back(dets[i].second);
...
...
...
else if (!overlaps_ignore_box(idx,dets[i].second)) //no hit then add loss
{
    // didn't hit anything
    final_dets.push_back(dets[i].second);

#ifdef ENABLE_ASSERTS
total_score += dets[i].first;
#endif
loss += loss_per_false_alarm;
}
}

this loss function looks like Hinge loss, Rects using fhog predict.
so I guess the object occupy can not bigger than 50% of image to train
Am I right?

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