Rahul Mehta

@khizii I think there is a mismatch between the output shape of your TFLite model. The model produces 10 values, while you're expecting 40 (10 x 4).

@drewshark TensorFlow Nightly can potentially resolve this issue as the bug fix might be included there.

@marcenacp method works properly and also you can try `import tensorflow_datasets as tfds wider_face_builder = tfds.builder('wider_face') wider_face_builder.download_and_prepare() wider_face_dataset = wider_face_builder.as_dataset()` it worked on my system

@marcenacp, ya I used it earlier in one of my projects. Thanks for the information.

@tomvdw or can we continue using the tfds.builder_from_directory workaround for loading datasets from the specified directory...?

@singhniraj08 you can visit link- https://www.tensorflow.org/datasets/overview#fixing_nonmatchingchecksumerror. For correction and as per my knowledge this issue is not solved yet

@marcenacp, is the dataset hosted on cloud like aws or google cloud storage?? if it is we can directly download it form there and Since you're running into memory limitations...

@rahul-fnu To limit the sampling rate or reduce the amount of information collected by the TensorFlow profiler, you can adjust the sampling_rate parameter in the tensorflow_profiler.experimental.client function. Use- tensorflow_profiler.experimental.client("grpc://localhost:3222", "profiles",...