[OpenR8 solution] Image-Object-Detection-VGG16-SSD300-Keras-Face (Image analysis using SSD 512 algorithm and Keras library for face recognition)
This Image_Face_SSD_Keras is through Keras library, using the SSD (Single Shot Multibox detector) method for face recognition.
[OpenR8 solution] Image-Object-Detection-VGG16-SSD300-Caffe-Age (Using the SSD 512 algorithm for image analysis and Caffe library for age prediction)
This Image_Age_SSD_Caffe is in the framework of deep learning Caffe.
[OpenR8 solution] Image-Classification-AlexNet-Caffe (Using AlexNet algorithm and Caffe framework for object classification)
This Image_Classify_AlexNet_Caffe is using the Caffe framework.
[OpenR8 solution] Image-Classification-VGG16-Caffe (Using VGG16 algorithm and Caffe framework for object classification)
This Image_Classify_VGG16_Caffe is using the Caffe framework, VGG16 network architecture is used to train the model and to classify the images through trained models.
[OpenR8 solution] Image-Classification-ResNet50-Caffe (Using ResNet50 algorithm and Caffe framework for object classification)
This Image_Classify_ ResNet50_Caffe is using the Caffe framework.
[OpenR8 solution] Image-Segmentation-MaskRCNN-Keras (Using MaskRCNN and Keras framework for Pill)
Mask R-CNN is an extended application of Faster R-CNN, adding a branch more than Faster r-cnn. The target pixels are segmented while the target is being detected.
[OpenR8 solution] Sound-Input (Record and play sound files)
This function is used for recording.
[OpenR8 solution] Image-Classification-Full-Connection-Caffe-XOR (Learn XOR rules with Caffe)
XOR_Caffe_FC is to use the Caffe framework to determine the XOR value of two numbers.
[OpenR8 solution] Http-Server-Object-Detection-VGG16-SSD300-Keras-PCB (Python TensorFlow Keras Deep Learning Server for PCB Inspection)
The purpose of this solution : The user selects the image through the Web page and obtains the frame that the result message of the object detects.
[OpenR8 solution] For-Loop (for loop)
ForLoop refers to the for loop in Loop. If there is some basicity for the program, it should be familiar to the loop.
[OpenR8 solution] Http-Server-VGG16-SSD300-Caffe-VOC (OpenR8 Caffe deep learning server)
The function of HttpServer_DeepLearning is to read the already trained caffemodel file.
[OpenR8 solution] Http-Server-Object-Detection-VGG16-SSD300-Keras-VOC (Python TensorFlow Keras deep learning server)
The purpose of this solution is to allow users to select images through the web page and obtain a frame of the resulting for object detection.
[OpenR8 solution] Http-Server (Web Server)
The function of HttpServer is to transmit the file to be received, and display the content of the received file through the webpage.
[OpenR8 solution] Data-Analysis (Data Analysis)
Data_analysis uses different methods to model learning and inference of data.
[OpenR8 solution] Get-Computer-ID (Get the MAC address)
The computer Media Address Control Address (MAC address) can be obtained automatically by executing the R8 solution.
[OpenR8 solution] Image-Move-Mouse-Then-Screenshot (Mouse control and image screenshot analysis)
This section describes how users can load an existing solution through the R8 software to automatically click on the next image.
[OpenR8 solution] Image-Object-Detection-VGG16-SSD512-Caffe-Face-Recognition-Celebrity (Face recognition of famous people)
Using the Caffe framework, the human face in the image is detected and identified through the SSD (Single Shot Multibox detector) method.
[OpenR8 solution] Image-Find-Blob (Find objects in the image)
The purpose of this solution is to find objects in the image.
[OpenR8 solution] Image-Cap (Detect if the cap is tight)
The Image_Cap is to detect whether the cap is covered and tight.
[OpenR8 solution] Image-Get-Sharpness (Obtain image sharpness to determine if the image is blurry)
By running the R8 solution, you can automatically obtain the sharpness of the image to determine if the image is blurry.