(batch_size, num_queries, d_model), with num_queries typically set to 100 and initialized with zeros.
These input embeddings are learnt positional encodings that the authors refer to as object queries, and similarly to
the encoder, they are added to the input of each attention layer. Each object query will look for a particular object
in the image. The decoder updates these embeddings through multiple self-attention and encoder-decoder attention layers
to output decoder_hidden_states of the same shape: (batch_size, num_queries, d_model). Next, two heads
are added on top for object detection: a linear layer for classifying each object query into one of the objects or "no
object", and a MLP to predict bounding boxes for each query.
The model is trained using a bipartite matching loss: so what we actually do is compare the predicted classes +
