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MediaCatch/
yolo11m-redbull-competitors

Name: yolo11m-redbull-competitors
Availability: InStock

Ultralytics/YOLO11

finetune

apache-2.0

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YOLO11m Red Bull competitors

monster
monster_can
celsius
celsius_can
sting
sting_can
alaninu
alaninu_can
white_monster_can

Dataset and training

Link to dataset: https://mediacatch.fra1.digitaloceanspaces.com/logo_recognition/data/redbull-competitors.zip.

Local copy: /datasets/logo/logos/redbull-competitors.

To train on a single RTX 3090:

yolo detect train data=data.yaml model=yolo11m.pt epochs=100 imgsz=1280 batch=8

and with four devices:

yolo detect train data=data.yaml model=yolo11m.pt epochs=100 imgsz=1280 batch=32 device=0,1,2,3

Usage

Using YOLO's own CLI:

yolo predict model=/path/to/best.pt source=/path/to/inputs conf=0.5

Using bifrost detector lib https://github.com/mediacatch/bifrost/tree/development/libs/detector:

uv run detect --conf-threshold 0.5 --imgsz 1280 /path/to/inputs redbull-competitors

Using HF:

python

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

weights = hf_hub_download("MediaCatch/yolo11m-redbull-competitors", "best.pt")
model = YOLO(weights)
results = model("https://ultralytics.com/images/zidane.jpg", conf=0.5, imgsz=1280)

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