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To perform object recognition using a standard machine learning approach, you start with a collection of images (or video), and select the relevant features in each image. For example, a feature extraction algorithm might extract edge or corner features that can be used to differentiate between classes in your data.
Step 1: Divide the image into a 10×10 grid like this: Step 2: Define the centroids for each patch. Step 3: For each centroid, take three different patches of different heights and aspect ratio:
Step 1. Install TensorFlow-GPU. [01:54] Step 2. Set up Object Detection directory and Anaconda virtual environment. [03:14] Step 3. Gather and label pictures. [15:21] Step 4. Generate training data. [18:35] Step 5. Create label map and configure training. [20:16] Step 6. Train object detector. [23:46] Step 7. Export inference graph. [26:54] Step 8. Try out your object detector!! [27:45]
From the cluster management console, select Workload > Spark > Deep Learning. Select the Datasets tab. Click New. Create a dataset from Images for Object Detection. Provide a dataset name. Specify a Spark instance group. Provide a training folder. Provide the percentage of training images for validation.
Check Point 1: Preparing Dataset: Check Point 2: Labeling the Dataset: Check Point 3: Generating Records for Training: Check Point 4: Configuring Training: Check Point 5: Training the Model: Check Point 6: Exporting Inference Graph:
Object Recognition is responding to the question “What is the object in the image” Whereas, Object detection is answering the question “Where is that object”? Hope someone can illustrate the difference by also generously providing an example for each.
Object detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. The goal of object detection is to replicate this intelligence using a computer.
The motive of object detection is to recognize and locate (localize) all known objects in a scene. Preferably in 3D space, recovering pose of objects in 3D is very important for robotic control systems.
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