Face detection about vedadet HOT 6 CLOSED

Hi,
Actually, you only need to change the "weights", the "filepath" where you save your trained model. And others like "num_classes" and "class_names" are based on your dataset. Besides, some post process parameters like "score_thr", "max_per_img" and "iou_thr" in "infer_engine" are based on your needs.

from vedadet.

Hi,
Actually, you only need to change the "weights", the "filepath" where you save your trained model. And others like "num_classes" and "class_names" are based on your dataset. Besides, some post process parameters like "score_thr", "max_per_img" and "iou_thr" in "infer_engine" are based on your needs.

thank you for your reply,
however，When I change ‘configs/infer/retinanet/retinanet.py’ to ‘configs/infer/tinaface/tinaface.py’, I can run infer.py successfully. Does this command need to be adjusted？【CUDA_VISIBLE_DEVICES="0" python tools/infer.py configs/infer/retinanet/retinanet.py image_path】 thank you

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You mean that you will get error when running "CUDA_VISIBLE_DEVICES="0" python tools/infer.py configs/infer/retinanet/retinanet.py image_path". Please provide the error info.

from vedadet.

You mean that you will get error when running "CUDA_VISIBLE_DEVICES="0" python tools/infer.py configs/infer/retinanet/retinanet.py image_path". Please provide the error info.

Some of them are as follows：
The model and loaded state dict do not match exactly

size mismatch for bbox_head.retina_cls.weight: copying a param with shape torch.Size([3, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([9, 256, 3, 3]).
size mismatch for bbox_head.retina_cls.bias: copying a param with shape torch.Size([3]) from checkpoint, the shape in current model is torch.Size([9]).
size mismatch for bbox_head.retina_reg.weight: copying a param with shape torch.Size([12, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([36, 256, 3, 3]).
size mismatch for bbox_head.retina_reg.bias: copying a param with shape torch.Size([12]) from checkpoint, the shape in current model is torch.Size([36]).
unexpected key in source state_dict: backbone.gn1.weight, backbone.gn1.bias, backbone.layer1.0.gn1.weight, backbone.layer1.0.gn1.bias, backbone.layer1.0.gn2.weight, backbone.layer1.0.gn2.bias, backbone.layer1.0.gn3.weight, backbone.layer1.0.gn3.bias, backbone.layer1.1.gn1.weight, backbone.layer1.1.gn1.bias, backbone.layer1.1.gn2.weight, backbone.layer1.1.gn2.bias, backbone.layer1.1.gn3.weight, backbone.layer1.1.gn3.bias, backbone.layer1.2.gn1.weight, backbone.layer1.2.gn1.bias, backbone.layer1.2.gn2.weight, backbone.layer1.2.gn2.bias, backbone.layer1.2.gn3.weight, backbone.layer1.2.gn3.bias, backbone.layer2.0.gn1.weight, backbone.layer2.0.gn1.bias, backbone.layer2.0.gn2.weight, backbone.layer2.0.gn2.bias, backbone.layer2.0.gn3.weight, backbone.layer2.0.gn3.bias, backbone.layer2.1.gn1.weight, backbone.layer2.1.gn1.bias, backbone.layer2.1.gn2.weight, backbone.layer2.1.gn2.bias, backbone.layer2.1.gn3.weight, backbone.layer2.1.gn3.bias, backbone.layer2.2.gn1.weight, backbone.layer2.2.gn1.bias, backbone.layer2.2.gn2.weight, backbone.layer2.2.gn2.bias, backbone.layer2.2.gn3.weight, backbone.layer2.2.gn3.bias, backbone.layer2.3.gn1.weight, backbone.layer2.3.gn1.bias, backbone.layer2.3.gn2.weight, backbone.layer2.3.gn2.bias, backbone.layer2.3.gn3.weight, backbone.layer2.3.gn3.bias, backbone.layer3.0.gn1.weight, backbone.layer3.0.gn1.bias, backbone.layer3.0.gn2.weight, backbone.layer3.0.gn2.bias, backbone.layer3.0.gn3.weight, backbone.layer3.0.gn3.bias, backbone.layer3.0.conv2.conv_offset.weight, backbone.layer3.0.conv2.conv_offset.bias, backbone.layer3.1.gn1.weight, backbone.layer3.1.gn1.bias, backbone.layer3.1.gn2.weight, backbone.layer3.1.gn2.bias, backbone.layer3.1.gn3.weight, backbone.layer3.1.gn3.bias, backbone.layer3.1.conv2.conv_offset.weight, backbone.layer3.1.conv2.conv_offset.bias, backbone.layer3.2.gn1.weight, backbone.layer3.2.gn1.bias, backbone.layer3.2.gn2.weight, backbone.layer3.2.gn2.bias, backbone.layer3.2.gn3.weight, backbone.layer3.2.gn3.bias, backbone.layer3.2.conv2.conv_offset.weight, backbone.layer3.2.conv2.conv_offset.bias, backbone.layer3.3.gn1.weight, backbone.layer3.3.gn1.bias, backbone.layer3.3.gn2.weight, backbone.layer3.3.gn2.bias, backbone.layer3.3.gn3.weight, backbone.layer3.3.gn3.bias, backbone.layer3.3.conv2.conv_offset.weight, backbone.layer3.3.conv2.conv_offset.bias, backbone.layer3.4.gn1.weight, backbone.layer3.4.gn1.bias, backbone.layer3.4.gn2.weight, backbone.layer3.4.gn2.bias, backbone.layer3.4.gn3.weight, backbone.layer3.4.gn3.bias, backbone.layer3.4.conv2.conv_offset.weight, backbone.layer3.4.conv2.conv_offset.bias, backbone.layer3.5.gn1.weight, backbone.layer3.5.gn1.bias, backbone.layer3.5.gn2.weight, backbone.layer3.5.gn2.bias, backbone.layer3.5.gn3.weight, backbone.layer3.5.gn3.bias, backbone.layer3.5.conv2.conv_offset.weight, backbone.layer3.5.conv2.conv_offset.bias, backbone.layer4.0.gn1.weight, backbone.layer4.0.gn1.bias, backbone.layer4.0.gn2.weight, backbone.layer4.0.gn2.bias, backbone.layer4.0.gn3.weight, backbone.layer4.0.gn3.bias, backbone.layer4.0.conv2.conv_offset.weight, backbone.layer4.0.conv2.conv_offset.bias, backbone.layer4.1.gn1.weight, backbone.layer4.1.gn1.bias, backbone.layer4.1.gn2.weight, backbone.layer4.1.gn2.bias, backbone.layer4.1.gn3.weight, backbone.layer4.1.gn3.bias, backbone.layer4.1.conv2.conv_offset.weight, backbone.layer4.1.conv2.conv_offset.bias, backbone.layer4.2.gn1.weight, backbone.layer4.2.gn1.bias, backbone.layer4.2.gn2.weight, backbone.layer4.2.gn2.bias, backbone.layer4.2.gn3.weight, backbone.layer4.2.gn3.bias, backbone.layer4.2.conv2.conv_offset.weight, backbone.layer4.2.conv2.conv_offset.bias, neck.0.lateral_convs.0.conv.weight, neck.0.lateral_convs.0.gn.weight, neck.0.lateral_convs.0.gn.bias, neck.0.lateral_convs.1.conv.weight, neck.0.lateral_convs.1.gn.weight, neck.0.lateral_convs.1.gn.bias, neck.0.lateral_convs.2.conv.weight, neck.0.lateral_convs.2.gn.weight, neck.0.lateral_convs.2.gn.bias, neck.0.lateral_convs.3.conv.weight, neck.0.lateral_convs.3.gn.weight, neck.0.lateral_convs.3.gn.bias, neck.0.fpn_convs.0.conv.weight, neck.0.fpn_convs.0.gn.weight, neck.0.fpn_convs.0.gn.bias, neck.0.fpn_convs.1.conv.weight, neck.0.fpn_convs.1.gn.weight, neck.0.fpn_convs.1.gn.bias, neck.0.fpn_convs.2.conv.weight, neck.0.fpn_convs.2.gn.weight, neck.0.fpn_convs.2.gn.bias, neck.0.fpn_convs.3.conv.weight, neck.0.fpn_convs.3.gn.weight, neck.0.fpn_convs.3.gn.bias, neck.0.fpn_convs.4.conv.weight, neck.0.fpn_convs.4.gn.weight, neck.0.fpn_convs.4.gn.bias, neck.0.fpn_convs.5.conv.weight, neck.0.fpn_convs.5.gn.weight, neck.0.fpn_convs.5.gn.bias, neck.1.level_convs.0.0.conv.weight, neck.1.level_convs.0.0.gn.weight, neck.1.level_convs.0.0.gn.bias, neck.1.level_convs.0.1.conv.weight, neck.1.level_convs.0.1.gn.weight, neck.1.level_convs.0.1.gn.bias, neck.1.level_convs.0.2.conv.weight, neck.1.level_convs.0.2.gn.weight, neck.1.level_convs.0.2.gn.bias, neck.1.level_convs.0.3.conv.weight, neck.1.level_convs.0.3.gn.weight, neck.1.level_convs.0.3.gn.bias, neck.1.level_convs.0.4.conv.weight, neck.1.level_convs.0.4.gn.weight, neck.1.level_convs.0.4.gn.bias, bbox_head.retina_iou.weight, bbox_head.retina_iou.bias, bbox_head.cls_convs.0.gn.weight, bbox_head.cls_convs.0.gn.bias, bbox_head.cls_convs.1.gn.weight, bbox_head.cls_convs.1.gn.bias, bbox_head.cls_convs.2.gn.weight, bbox_head.cls_convs.2.gn.bias, bbox_head.cls_convs.3.gn.weight, bbox_head.cls_convs.3.gn.bias, bbox_head.reg_convs.0.gn.weight, bbox_head.reg_convs.0.gn.bias, bbox_head.reg_convs.1.gn.weight, bbox_head.reg_convs.1.gn.bias, bbox_head.reg_convs.2.gn.weight, bbox_head.reg_convs.2.gn.bias, bbox_head.reg_convs.3.gn.weight, bbox_head.reg_convs.3.gn.bias

from vedadet.

Trained TinaFace model can not be loaded into RetinaNet, cause they are different models. If you want to use some models to infer, you should first train a corresponding model.

from vedadet.

Trained TinaFace model can not be loaded into RetinaNet, cause they are different models. If you want to use some models to infer, you should first train a corresponding model.

ok , i get it, Is there any solution to the false face detection？

from vedadet.