Please can you train ghostnet.
(i don't have the imagenet dataset)

https://github.com/google/automl/blob/3614751749a21ca2fcb299b60238c6651ff51125/efficientdet/efficientdet_arch.py#L402

When survival_prob=0.0, in both class_net and box_net, neither dropout or residual connection is applied. While in MBConvBlock, dropout is not applied but residual connection is still present. Did I get it right?

In file binary segmentation (camvid).ipynb, block 5, there is:

# Lets look at data we have
dataset = Dataset(x_train_dir, y_train_dir, classes=['car', 'pedestrian'])
image, mask = dataset[5] # get some sample
visualize(
    image=image, 
    cars_mask=mask[..., 0].squeeze(),
    sky_mask=mask[..., 1].squeeze(),
    background_mask=mask[..., 2].squeeze(),
)

here, sky_mask

The 2x down-sampling is one of the important operations in reference models. But, a convolution or a pooling with stride=2, padding='SAME' may result in different outputs over different deep learning libraries (e.g., TensorFlow, CNTK, Theano, Caffe, Torch, ...) due to their different padding behaviors.

For example (TensorNets syntax; but can be regarded as pseudo codes for other libraries),

pip insatll tensorflow==2.0.0 or pip install tensorflow==2.0.0-beta1，tensorflow2.0.0or tensorflow2.0.0-beta1,but when I run train.py, the error of "ValueError: This converter can only convert a single ConcreteFunction. Converting multiple functions is under development" appears,why?How to do?

Hi,

I'm also a UFSC Araranguá student from course of Information Tecnology and Comunication.

I've experience working with python, data structures, algorithms, database etc.

I'm looking for a completion course work and I'm interested in this problem, how can i contribute to this project?

my email is arthurfn.webdev@gmail.com