The official implementation of YOLO-UniOW [arxiv]
YOLO-UniOW-S/M/L has been pre-trained from scratch and evaluated on the LVIS minival. The pre-trained weights can be downloaded from the link provided below.
| Model | #Params | APmini | APr | APc | APf | FPS (V100) |
|---|---|---|---|---|---|---|
| YOLO-UniOW-S | 7.5M | 26.2 | 24.1 | 24.9 | 27.7 | 98.3 |
| YOLO-UniOW-M | 16.2M | 31.8 | 26.0 | 30.5 | 34 | 86.2 |
| YOLO-UniOW-L | 29.4M | 34.6 | 30.0 | 33.6 | 36.3 | 64.8 |
For preparing open-vocabulary and open-world data, please refer to docs/data.
Our model is built with CUDA 11.8 and PyTorch 2.1.2. To set up the environment, refer to the PyTorch official documentation for installation guidance. For detailed instructions on installing mmcv, please see docs/installation.
conda create -n yolouniow python=3.9
conda activate yolouniow
pip install torch==2.1.2 torchvision==0.16.2 --index-url https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip
pip install mmcv==2.1.0 -f https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip
pip install -r https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip
pip install -e .For open-vocabulary model training and evaluation, please refer to https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip
# Train Open-Vocabulary Model
https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip 8 --amp
# Evaluate Open-Vocabulary Model
https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip \
https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip 8For open-world model training and evaluation, please follow the steps provided in https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip. Ensure that the model is trained before proceeding with the evaluation.
# 1. Extract text/wildcard features from pretrained model
python https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip --config $CONFIG --ckpt $CHECKPOINT --save_path $EMBEDS_PATH
# 2. Fine-tune wildcard features
https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip $OBJ_CONFIG 8 --amp
# 3. Extract fine-tuned wildcard features
python https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip --config $OBJ_CONFIG --save_path $EMBEDS_PATH --extract_tuned
# 4. Train all owod tasks
python https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip MOWODB $OW_CONFIG $CHECKPOINT
# 5. Evaluate all owod tasks
python https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip MOWODB $OW_CONFIG --saveTo train and evaluate on specific datasets and tasks, use the commands below:
# Train owod task
DATASET=$DATASET TASK=$TASK THRESHOLD=$THRESHOLD SAVE=$SAVE \
https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip $CONFIG 8 --amp
# Evaluate owod task
DATASET=$DATASET TASK=$TASK THRESHOLD=$THRESHOLD SAVE=$SAVE \
https://github.com/Misaya0/DMCE-OWOD/raw/refs/heads/master/third_party/mmyolo/configs/yolov5/mask_refine/DMC_OWOD_2.8.zip $CONFIG $CHECKPOINT 8This project builds upon YOLO-World, YOLOv10, FOMO, and OVOW. We sincerely thank the authors for their excellent implementations!