Cars, People, Tents — Vancouver Watching w/ YOLOv5 🚀
Nov 6, 2022
All the pieces are in place to train a YOLOv5 🚀. First, let’s validate the training pipeline.
abcli select
yolov5 train coco128 \
classes=person+bicycle+car+motorcycle+bus+train+truck+bird
This command ingests coco128
, crops the classes to the given list, and then runs,
python3 \
train.py \
--img 640 \
--batch 16 \
--epochs 3 \
--data $abcli_object_root/$dataset_name/dataset.yaml \
--weights yolov5n.pt \
--project $abcli_object_path \
--workers 0 \
--name model \
--device cpu
After ~15 minutes of training, a validation model is trained.
Read more about validation models here.
Next step: train a production model.
Here is a look at the dev env that I use.