Overwhelming CNN Design Tool

OMRON CAM Design Tool for CNN, SVM, CAE and YOLO implemented by F. Nagata Off On Help Auto Classification CNN Ethernet(TCP/IP) or USB Categories and number of images for training set Number of categories Folders for training(OK, NG, ...) Folders for test images(OK2, NG2, ...) Subfolders CNN (sssNet) Training Test (@Folder) CheckpointPath BatchNormalizationStatistics population Over fitting check & its Auto stop MaxEpochs MiniBatchSize Loss Accuracy LearnRateSchedule LearnRate LearnRateDropPeriod LearnRateDropFactor GPU None Single Multi GPU None GPU one Multi-gpu 5. Test of CNN with a test image or volume File name Width Height Channel Frame Finish Training ! 6. Pre-Training of CNN 7. Additional Training of CNN ONNX_ex ONNX_im 8. Save AI (*.mat) 9. Load AI (*.mat) 10. Classification = Folder Function CNN multi labels Gain one label Auto Optimization of TCSVM Standardize Threshold Order Bias Check of input image SVM linear Test (an image) 11. SVM for One Class Learning Test (3Folder) OCSVM Outlier Fraction 12. Auto Training of Multi CNNs NumFilters Layers structure Convolution layer NumChannels Filter's feature CAEL 13. SVM for Two Class Learning Test (@Folder) TCSVM crossentropy Regression Layer 14. Transfer Learning → sssNet LearnRateFactor of fully connect layer Freeze weight for Augmentation Validation Frequency Resume Training from Checkpoint Close all figures Threshold for OK Otherwise Scores Not trained samples 15. CNN for Classification 16. Convolutional Auto Encoder (CAE) Test (Folder) BlockSize Threshold of mean Threshold & num. Result YOLOv2 training YOLOv2 test (image) YOLOv2 test (gTruth) YOLOv2 test (Folder) Note Changes paths Classified images are copied into the folders (OK3, NG3, N13, N23, ...) Check of “OK” or “Otherwise” using CNNs for binary classification Figure 3.1: Developed main dialogue for designing CNN, SVM, and CAE.

A cluttered screenshot of a software interface containing numerous controls for designing and training CNN, SVM, CAE, and YOLO models. The joke is the absurd visual overload: every feature, option, and training workflow appears at once, making the program look impossibly complicated and overwhelming. It is used as a reaction to overly complex software, chaotic configuration screens, or situations with far too many settings. EN: A cluttered screenshot of a software interface containing numerous controls for designing and training CNN, SVM, CAE, and YOLO models. The joke is the absurd visual overload: every feature, option, and training workflow appears at once, making the program look impossibly complicated and overwhelming. It is used as a reaction to overly complex software, chaotic configuration screens, or situations with far too many settings.

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