You are right, batchsize is important and we should make that more clear.
The numbers in the benchmark are taken from our deep-learning-benchmarks[1] which we are still in the process of building up. It might actually make sense to test the same model with different batch sizes. The current benchmarks are based on the convnet-benchmarks[2] where the Alexnet model has a batch size of 128. (Alexnet was chosen because out of the benchmarks that's the one I am most familiar with, since it small enough that I can work with it on my Laptop)
In some informal tests Leaf was generally faster than other frameworks in smaller batch sizes, but no benchmarks that we could publish with confidence yet.
The numbers in the benchmark are taken from our deep-learning-benchmarks[1] which we are still in the process of building up. It might actually make sense to test the same model with different batch sizes. The current benchmarks are based on the convnet-benchmarks[2] where the Alexnet model has a batch size of 128. (Alexnet was chosen because out of the benchmarks that's the one I am most familiar with, since it small enough that I can work with it on my Laptop)
In some informal tests Leaf was generally faster than other frameworks in smaller batch sizes, but no benchmarks that we could publish with confidence yet.
[1]: https://github.com/autumnai/deep-learning-benchmarks [2]: https://github.com/soumith/convnet-benchmarks