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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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Allan BusiJun 1, 20268 min readGlasgowThe Mastering YOLOv8: Complete Training in Deep Learning Object Detection 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 Mastering YOLOv8: Complete Training in Deep Learning Object Detection 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 Mastering YOLOv8: Complete Training in Deep Learning Object Detection 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.
Presentation of neural networks for computer vision. Evolution of YOLO algorithms (from YOLOv1 to YOLOv8). New features introduced by YOLOv8 (architecture, speed, accuracy, modularity). Hands-on with the environment: installing dependencies (Python, CUDA, Torch, Ultralytics framework), GPU/CPU management, introduction to the Ultralytics YOLO command-line interface.
Quick review of annotation formats (label, bounding box, COCO format). Use of annotation tools (Roboflow, LabelImg). Creating a custom dataset. Preparing training configurations (data.yaml, hyp.yaml). Launching training: log management, monitoring performance curves (loss, mAP, precision, recall). Practical exercises: selecting a business use case (e.g., industrial, road, or medical object detection).
Cross-validation and result interpretation. Optimization strategies (advanced data augmentation, backbone fine-tuning, hyperparameter search). Model export (ONNX, TorchScript, CoreML, TensorRT). Integrating YOLOv8 into a Flask/FastAPI API. Deployment on edge devices (Jetson, Raspberry Pi), optimization and latency constraints. Practical cases: real-time detection on video streams. Discussion on limitations, biases, and future perspectives of deep learning object detection.
8 participants max.
$3,630 / per person
3 days
distancielTarget audience
Developers, data scientists, computer vision engineers, and anyone wishing to integrate object detection into their AI projects
Prerequisites
Basic knowledge of Python, machine learning, and convolutional neural networks
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