This is the Windows app named YoloV3 Implemented in TensorFlow 2.0 whose latest release can be downloaded as yolov3-tf2sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named YoloV3 Implemented in TensorFlow 2.0 with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS:
YoloV3 Implemented in TensorFlow 2.0
DESCRIPTION:
YoloV3 Implemented in TensorFlow 2.0 is built using TensorFlow 2.0. The project provides a modern deep learning implementation of the popular YOLOv3 algorithm, which is widely used for real-time object detection in images and video streams. YOLOv3 works by dividing an image into grid regions and predicting bounding boxes and class probabilities simultaneously, allowing objects to be detected quickly and efficiently. The repository includes training scripts, inference tools, and configuration files that make it possible to train custom object detection models on user-defined datasets. It also demonstrates how to integrate the model with TensorFlow’s high-level APIs such as Keras for easier experimentation and model development. The project supports both pretrained models and full training pipelines, enabling researchers and developers to adapt YOLOv3 for tasks such as surveillance, robotics, autonomous driving, and image analysis.
Features
- Implementation of the YOLOv3 object detection architecture in TensorFlow 2
- Training pipeline for custom object detection datasets
- Pretrained model weights for rapid inference
- Support for real-time image and video object detection
- Integration with TensorFlow Keras APIs for model development
- Tools for dataset preparation and model evaluation
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/yolov3-implemented-tf.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.