Finally, experimental results about image classification on the coarse-grained dataset CIFAR-10 (93.41%) and fine-grained dataset CIFAR-100 (70.22%) demonstrate the effectiveness of the framework by comparing with state-of-the-art results. 1.
CIFAR-10: Classify 32x32 colour images into 10 categories. CIFAR-100: Classify 32x32 colour images into 100 categories. STL-10: Image recognition dataset inspired by CIFAR-10. SVHN: Street View House Numbers dataset. PASCAL VOC Object Detection: Visual Object Classes 2012 object detection. PASCAL VOC Object Segmentation
The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. Rodrigo Benenson has been kind enough to collect results on CIFAR-10/100 and other datasets on his website; click here to view.
I received a question asking about CIFAR-10 accuracy, as the demo doesn't reach very high accuracy. Compared to MNIST, CIFAR-10 is a harder dataset and the default network in the demo is tiny. State of the art on the dataset is roughly 90% but these are fairly large models trained for on...
I'm trying to train the mobileNet and VGG16 models with the CIFAR10-dataset but the accuracy can't get above 9,9%. I need it with the completly model (include_top=True) and without the wights from imagenet. P.S.: I have tried increasing/decreasing dropout and learning rate and I changed the...
Configure your MobileNet. In this exercise, we will retrain a MobileNet. MobileNet is a a small efficient convolutional neural network. "Convolutional" just means that the same calculations are performed at each location in the image. The MobileNet is configurable in two ways: Input image resolution: 128,160,192, or 224px.
This section we will take mobilenet_v1 for example, to show how to use RK1808 AI compute stick. Mobilenet_v1 can realize feature extraction of an image and identification of the classification of the image. The mobilenet_v1 demo directory structure and description are as follow: l dataset.txt: a text file containing the test image path.
This section we will take mobilenet_v1 for example, to show how to use RK1808 AI compute stick. Mobilenet_v1 can realize feature extraction of an image and identification of the classification of the image. The mobilenet_v1 demo directory structure and description are as follow: l dataset.txt: a text file containing the test image path.