Neural Object Detector vs Cam4DL Usage & Stats

Neural Object Detector was designed to be used by both developers and people who are enthusiastic about Machine Learning, Computer Vision, and Object Detection / Image Classification using the combination of both. Neural Object Detector, by default is bundled with YOLOv3 model, which is a neural network for fast object detection that detects 80 different classes of objects. In addition to that, the app allows the users to import any custom machine learning model designed for object detection or image classification, with a single tap, the downloaded model can be imported via the Files app import window which is available within the app by simply pressing the plus icon on the models view so users do not have to leave the app. The app, based on the model selected, draws a rounded rectangle over the detected objects, the annotated image can be rendered and saved to photos or shared if the user chooses to share it directly from the app. To meet every users needs a handful of settings for computer vision algorithm and camera resolution can be changed. Users have the option to enable CPU only mode, which helps test their models under that specific condition. In addition to Object Detection, Neural also supports Image Classification, by default, Neural is bundled with Resnet50 Image Classification Model. Core ML* models which are a type of "Pipeline" in the format of *.mlmodel is supported by this app for Object Detection. No other model format is supported as of now. Core ML Supported Tools, Services, and Converters: • Turi Create* - https://github.com/apple/turicreate • IBM Watson Services* - https://developer.apple.com/ibm/ • Core ML Tools* - https://pypi.org/project/coremltools/ • Apache MXNet* - https://github.com/apache/incubator-mxnet/tree/master/tools/coreml • TensorFlow* - https://github.com/tf-coreml/tf-coreml • ONNX* - https://github.com/onnx/onnx-coreml * Turi Create, IBM Watson Services, Core ML, Apache MXNet, TensorFlow, ONNX might be registered trademarks of their respected owners / proprietors. Neural Object Detector nor the developer is not affiliated with any of the above services or companies. YOLOv3 Model bundled with the is app is free, open source model. More info: https://github.com/pjreddie/darknet Resnet50 Model bundled with this app for Image Classification is free, and open source model. More info: https://github.com/fchollet/deep-learning-models/blob/master/LICENSE Visit https://hariharanm.com/neural/acknowledgements/ for Acknowledgments. The machine learning aspects are all proceed on device so nothing leaves the device. No cloud services are involved. No private analytics services. If, a user decides to contact app support a handful of data will be complied into a log file and attached to the mail composer, these data include app version, app configuration, device model, battery info, device software version, CPU utilised my the app. If the user decided not share, they can simply delete it and continue with the composing the support mail. No data is that is individually identifiable is collected. https://hariharanm.com/

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Cam4DL

Cam4DL is an app for creating image and video datasets that are typically needed in deep learning projects. RECORD VIDEO CLIPS READY FOR ACTION CLASSIFICATION With Cam4DL you can now record small video clips that you can use for training an action classifier in CreateML. You can choose to record these video clips with the same frame rate and timing options available during CreateML training phase. You can record multiple video clips automatically and hands free. First select a category then tap on the shutter button to start the recording. When a video clip recording ends you have 3 seconds to get ready for the next clip recording. This automatically starts until you stop the recording by tapping again on the shutter button. The video clips are recorded without sound because this is hardly used for action classification. TAKE PHOTOS FOR OBJECT AND FACE ANALYSIS Using Cam4DL you can also take photos organized in categories and optionally generate depth maps and face metadata with ease at the moment of capture. Just create a project of type Face or Object, and choose if you want to include a depth map. Then choose if you want to optionally crop and resize the image at the moment of capture. You can then start taking photos that are automatically cropped, resized and organized in folders along with their metadata. The best part of Cam4DL is that all of the generated data is open and accessible from the Apple Files app, so that transferring this data to your workstation is easier and faster than ever. FACE DATA AND DEPTHMAP AUTOMATICALLY COMPUTED When you take a photo inside a project of type Face, the app automatically computes face features and save them in json files, so that you can later use them for your data science projects. When you select the depth map option during project creation, the app automatically computes the depth map while you take the photo and stores it in a png file. Cam4DL keeps the dataset pictures separate from Apple's Photos app. OPEN ACCESS TO ALL THE GENERATED DATA All the photos you take with Cam4DL and the relative metadata such as the face data are categorized and saved inside the app's document directory so that you can easily share them with your workstation for training. To access the data generated by Cam4DL, just open Apple's Files app and then navigate to "iPhone -> Cam4DL" folder. Made with Love and SwiftUI WARNING: All the datasets are stored inside the app's sandbox. For this reason if you delete the app you also delete the datasets, including all the videos, photos and the associated metadata. If you for any reason need to delete the app REMEMBER TO BACKUP your datasets to another location using the Files app.