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Let me get some hints up

• As pointed out by @berak, two views in a single cascade --> forget it, simply does not work and not what cascade classifiers in OpenCV following the Viola and Jones paper is meant for. You will need at least 2 separate models.
• Naotoshi Seo's createsamples script --> your second mistake. Its better to use your true data, without artificial data augmentation, but thats purely from experience.
• -w and -h are the dimension of your window (for negative sample grabbing) and model size. These can be the training image dimensions but do not have to be. Everything is rescaled to these parameters before training starts.
• Again don't use the script, then you do not need to merge vectors :D
• -minHitRate 0.999 that is very strict for your weak classifiers, might wanna try putting that on 0.995. Its an immense difference!
• Required leaf false alarm rate achieved. Branch training terminated. --> your data is optimally seperated given the set parameters. You need more data to obtain a better detector.

Let me get some hints up

• As pointed out by @berak, two views in a single cascade --> forget it, simply does not work and not what cascade classifiers in OpenCV following the Viola and Jones paper is meant for. You will need at least 2 separate models.
• Naotoshi Seo's createsamples script --> your second mistake. Its better to use your true data, without artificial data augmentation, but thats purely from experience.
• -w and -h are the dimension of your window (for negative sample grabbing) and model size. These can be the training image dimensions but do not have to be. Everything is rescaled to these parameters before training starts.
• Again don't use the script, then you do not need to merge vectors :D
• -minHitRate 0.999 that is very strict for your weak classifiers, might wanna try putting that on 0.995. Its an immense difference!
• Required leaf false alarm rate achieved. Branch training terminated. --> your data is optimally seperated given the set parameters. You need more data to obtain a better detector.

Let me get some hints up

• As pointed out by @berak, two views in a single cascade --> forget it, simply does not work and not what cascade classifiers in OpenCV following the Viola and Jones paper is meant for. You will need at least 2 separate models.
• Naotoshi Seo's createsamples script --> your second mistake. Its better to use your true data, without artificial data augmentation, but thats purely from experience.
• -w and -h are the dimension of your window (for negative sample grabbing) and model size. These can be the training image dimensions but do not have to be. Everything is rescaled to these parameters before training starts.
• Again don't use the script, then you do not need to merge vectors :D
• -minHitRate 0.999 that is very strict for your weak classifiers, might wanna try putting that on 0.995. Its an immense difference!
• Required leaf false alarm rate achieved. Branch training terminated. --> your data is optimally seperated given the set parameters. You need more data to obtain a better detector.

Oh and BTW, people should understand the difference between a notice/warning and an actual error. This does not mean your training breaks up, it just finished because you have what you asked for.