One important element of deep learning and machine learning at large is dataset. A good dataset will contribute to a model with good precision and recall. In the realm of object detection in images or motion pictures, there are some household names commonly used and referenced by researchers and practitioners. The names in the list include Pascal, ImageNet, SUN, and COCO. In this post, we will briefly discuss about COCO dataset, especially on its distinct feature and labeled objects.
When running the test script “relu_op_test.py” to verify Caffe2 installation, you may encounter this error “ImportError: No module name named hypothesis”. Let’s take a look at the content of the script to get some idea about the root cause.
from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from caffe2.python import core from hypothesis import given import hypothesis.strategies as st import caffe2.python.hypothesis_test_util as hu import caffe2.python.mkl_test_util as mu import numpy as np import unittest class TestRelu(hu.HypothesisTestCase): @given(X=hu.tensor(), engine=st.sampled_from(["", "CUDNN"]), **mu.gcs) def test_relu(self, X, gc, dc, engine): op = core.CreateOperator("Relu", ["X"], ["Y"], engine=engine) # go away from the origin point to avoid kink problems X += 0.02 * np.sign(X) X[X == 0.0] += 0.02 self.assertDeviceChecks(dc, op, [X], ) self.assertGradientChecks(gc, op, [X], 0, ) if __name__ == "__main__": unittest.main()
In the previous posts, we have gone through the installation processes for deep learning infrastructure, such as Docker, nvidia-docker, CUDA Toolkit and cuDNN. With the infrastructure setup, we may conveniently start delving into deep learning: building, training, and validating deep neural network models, and applying the models into a certain problem domain. Translating deep learning primitives into low level bytecode execution can be an enormous task, especially for practitioners without interests in the deep learning calculus. Fortunately, there are several deep learning frameworks that provide the high level programming interface to assist in performing deep learning tasks.
In this post, we will go through the installation of Caffe2, one of the major deep learning frameworks. Caffe2 is adopted from Caffe, a deep learning framework developed by the Barkeley Vision and Learning Center (BVLC) of UC Berkeley. Caffe2 was started with the aim to improve Caffe especially to better support large-scale distributed model training, mobile deployment, reduced precision computation, new hardware, and flexibility of porting to multiple platforms. Continue reading