This is the Fast R-CNN paper. It improves over R-CNN. It is a single-stage
training model (i.e., only one model to train) on the Pascal VOC 2012 dataset.
It is faster than R-CNN by using the RoI pooling layer.
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Girshick et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation
This is the R-CNN paper. It is a multi-stage process to take an image as input and produces bounding boxes and classification softmax vector as output. This is called localization of object within an image. The paper is based on Pascal VOC dataset (20 classes). Most of the technical details...
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Howard et al (2017) MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Convolutional networks such as AlexNet demonstrated the accuracy image
recognition. However, latency as well as model size (i.e., memory) can be a
concern. MobileNet proposed in this paper is to make these adjustable.
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Great Expectations
Great expectations is a Python library for data validation. It tries to profile a sample dataset for what are the attributes, and the statistics of the attributes (e.g., if numeric, what are min, max, mean, variance, etc.). If you read the documentation, it would suggest you to create a data...
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Fixing Keras plot_model bug with expand_nested=True on functional models
Keras has a bug on its function plot_model(). It works fine for simple models but when you have nested models, i.e., a model that depends on some submodels, the plot would be wrong. Specifically, it would be wrong if the submodel is not single-input, single-output (i.e., it must be created...
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