This repository contains material to train the YOLO v8 neural network architectures from Ultralytics on the LARD dataset, for detection, segmentation and pose estimation tasks.
Example of installation:
conda create -p ./.conda python=3.10
pip install -r requirements.txt
NOTE: If necessary, you can override environment variables located in .env
file by creating a .env.local
one. It will automatically be loaded if existing.
All the neural networds are available under the nn
directory tree, with ONNX exports and training associated files.
task | mult-adds (GFLops) |
weights | mAP50 BBOX |
mAP50:95 BBOX |
mAP50 POSE |
mAP50:95 POSE |
---|---|---|---|---|---|---|
pose | 42.66 | pretrained | 0.99 | 0.9 | 0.98 | 0.95 |
pose | 42.66 | scratch | 0.98 | 0.85 | 0.97 | 0.91 |
task | mult-adds (GFLops) |
weights | mAP50 BBOX |
mAP50:95 BBOX |
---|---|---|---|---|
detect | 41.39 | pretrained | 0.99 | 0.91 |
detect | 41.39 | scratch | 0.99 | 0.87 |
task | mult-adds (GFLops) |
weights | mAP50 BBOX |
mAP50:95 BBOX |
mAP50 MASK |
mAP50:95 MASK |
---|---|---|---|---|---|---|
segment | 61.31 | pretrained | 0.98 | 0.87 | 0.97 | 0.76 |
segment | 61.31 | scratch | 0.99 | 0.87 | 0.97 | 0.73 |