This library is based on the great TorchCV library and combines it with the MobileNetV2 architecture which aims to greatly reduce the model parameters.
Get the voc07 train and test set. Copy or symlik the VOC datasets to mobilessd/datasets/voc/VOCdevkit (Scipts must be able to find training and test images in mobilessd/datasets/voc/VOCdevkit/VOC2007/JPEGImages).
This done, you can use train.py, test.py and eval.py from the scripts folder.
The mAP of the created model currenlty tops for VOC07 dataset at 53%.
{'ap': array([0.60172077, 0.66596245, 0.38399861, 0.42450882, 0.14971891, 0.66367717, 0.75209124, 0.54852008, 0.30904903, 0.50163143, 0.58313164, 0.49539706, 0.70116495, 0.69394266, 0.6345324 , 0.25968499, 0.50607345, 0.58362693, 0.67304679, 0.56539054]), 'map': 0.5348434957870685}
Especially for classes "bottles" and "potted plants" the algorithm still has problems. They have in common that bottles and plants often appear in smaller size and grouped on a single image, therefore next step would be to check small anchors in the bottom layers of the fpn.