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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training YOLOv8: Mastering Object Detection with Deep Learning training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training YOLOv8: Mastering Object Detection with Deep Learning training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training YOLOv8: Mastering Object Detection with Deep Learning training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Overview of object detection techniques, history of YOLO (from v1 to v8), novelties introduced by YOLOv8. Presentation of concrete business use cases. Hands-on with the Python environment, installation of PyTorch, Ultralytics YOLOv8, management of virtual environments.
Dataset structuring, annotation tools (Roboflow, CVAT). Annotation format conversion. Configuration preparation. Launching training on a custom dataset, interpreting logs and results. Overfitting management, hyperparameter tuning, data augmentation.
Model performance evaluation: mAP, recall, precision metrics. Improvement techniques: model adjustment, transfer learning, pruning. Export to ONNX, CoreML, TensorRT. Production deployment: API integration, model monitoring, retraining cycles. Case study: complete business application (real-time object recognition on webcam, video surveillance, or industrial imaging).
Target audience
Developers, Data Scientists, AI engineers wishing to deploy object detection solutions
Prerequisites
Python basics and knowledge of deep learning
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