CUSTOM OBJECT DETECTION USING YOLOV8
Descrição da oferta de emprego
Task Requirements.
The task is to fine-tune a pretrained Object Detection model (YOLOv8) to detect 3 custom object classes.
Note.
The object classes must not belong to the COCO dataset.
Expected Steps to Complete the Assignment.
Data Collection and Annotation.
Images should be collected and annotated using tools such as Roboflow.
The dataset must be well-prepared for training.
Model Training and Evaluation.
The YOLOv8 model must be trained using the annotated dataset.
Model performance should be evaluated based on mean average precision (mAP).
Experimentation and Fine-tuning.
All experiments are to be logged using tools like Weights & Biases (wandb).
Model performance should be improved by fine-tuning the dataset, adjusting hyperparameters, applying data augmentation, or employing other optimization techniques.
Results from different training iterations should be compared.
Model Export and Deployment.
The trained model must be exported to a high-performance format suitable for the chosen platform (CPU or GPU) for fast inference.
Inference should be demonstrated on.
One test image One test video (~1 minute duration) The best-performing model should be deployed to HuggingFace Space.
Expected Submission.
A detailed discussion of the data collection and annotation process, including a link to the dataset/annotations.
An explanation of the fine-tuning process with a comparison of model performances.
Screenshots of experimental logs on Weights & Biases.
A link to the deployed model on HuggingFace Space.
Outputs from the best model.
One test image with bounding boxes.
One test video (.
p4) with bounding boxes.
This approach ensures clear documentation and demonstration of the model's performance and deployment capabilities.
YOLO Python Inteligência Artificial Hugging Face Object Detection ID do Projeto.
# Sobre o projeto 5 propostas Aberto para ofertas Projeto remoto Ativo em 18 minutos atrás
Detalhes da oferta
- Indeterminado
- Em todo Portugal
- Indeterminado - Indeterminado
- 20/12/2024
- 20/03/2025
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