Centernet: Integrating EfficientNet with CenterNet

Created on 4 Dec 2019 · 4Comments · Source: xingyizhou/CenterNet

I am trying to integrate efficientnet from https://github.com/narumiruna/efficientnet-pytorch/blob/master/efficientnet/models/efficientnet.py.

I added efficientnet.py in models/networks folder and added corresponding code for factory, etc.

Then, I tried to train the network using COCO data, the loss, hm_loss and wh_loss are becoming converged at around 3, 2 and 5 respectively after 140 epoch and cannot reduce anymore. It is not enough to get a good AP result. Do you have any hints to improve?

These codes are added after the last channels in efficientnet by following the code in resnet_dcn.py
` last_channels = _round_filters(1280, width_mult)
features += [ConvBNReLU(in_channels, last_channels, 1)]

    self.features = nn.Sequential(*features)
    # self.classifier = nn.Sequential(
    #     nn.Dropout(dropout_rate),
    #     nn.Linear(last_channels, num_classes),
    # )

    # used for deconv layers
    self.inplanes = last_channels
    self.deconv_layers = self._make_deconv_layer(
        3,
        [256, 128, 64],
        [4, 4, 4],
    )

    for head in self.heads:
        classes = self.heads[head]
        # fc = nn.Conv2d(64, classes, 
        #     kernel_size=1, stride=1, 
        #     padding=0, bias=True)
        # fill_fc_weights(fc)
        if head_conv > 0:
            fc = nn.Sequential(
              nn.Conv2d(64, head_conv,
                kernel_size=3, padding=1, bias=True),
              nn.ReLU(inplace=True),
              nn.Conv2d(head_conv, classes, 
                kernel_size=1, stride=1, 
                padding=0, bias=True))
            if 'hm' in head:
                fc[-1].bias.data.fill_(-2.19)
            else:
                fill_fc_weights(fc)
        else:
            fc = nn.Conv2d(64, classes, 
              kernel_size=1, stride=1, 
              padding=0, bias=True)
            if 'hm' in head:
                fc.bias.data.fill_(-2.19)
            else:
                fill_fc_weights(fc)
        self.__setattr__(head, fc)`

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