I have a question. We used Deformable Conv in classification tasks. We set the training batchsize the same as im2col_step. During the test process, we put different numbers of test samples in test batch (e.g. test the testing dataset by input one sample per time, or test the testing dataset by inputing ten samples per time), and get different classification results. It seems that how many samples we input to the network each time impacts the final classification results. So why is this happening? Will you kindly give me some advice? What's the relationship between testing batchsize and im2col_step? What's the relationship between training batchsize and im2col_step?
I have a question. We used Deformable Conv in classification tasks. We set the training batchsize the same as im2col_step. During the test process, we put different numbers of test samples in test batch (e.g. test the testing dataset by input one sample per time, or test the testing dataset by inputing ten samples per time), and get different classification results. It seems that how many samples we input to the network each time impacts the final classification results. So why is this happening? Will you kindly give me some advice? What's the relationship between testing batchsize and im2col_step? What's the relationship between training batchsize and im2col_step?