GLEE can be directly tested on classic detection and segmentation datasets locally. For some video datasets, the results need to be submitted to Codalab for evaluation. Additionally, certain datasets such as TAO, BURST, and OmniLabel require evaluation using additional tools. We will continue to update the evaluation tutorials for all datasets reported in the paper here.
GLEE can directly perform evaluations on COCO, Objects365, LVIS, and the RefCOCO series based on Detectron2. Typically, the Stage 2 yaml config file can be used, with manual adjustments made for the dataset to be inferred and the selection of weights to be downloaded from the MODEL_ZOO.md.
To inference on COCO:
# Lite
python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/images/Lite/Stage2_joint_training_CLIPteacher_R50.yaml --num-gpus 8 --eval-only MODEL.WEIGHTS path/to/GLEE_Lite_joint.pth DATASETS.TEST '("coco_2017_val",)'
# Plus
python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/images/Plus/Stage2_joint_training_CLIPteacher_SwinL.yaml --num-gpus 8 --eval-only MODEL.WEIGHTS path/to/GLEE_Plus_joint.pth DATASETS.TEST '("coco_2017_val",)'
# Pro
python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/images/Pro/Stage2_joint_training_CLIPteacher_EVA02L.yaml --num-gpus 8 --eval-only MODEL.WEIGHTS path/to/GLEE_Pro_joint.pth DATASETS.TEST '("coco_2017_val",)'
Replace "path/to/downloaded/weights" with the actual path to the pretrained model weights and use "DATASETS.TEST" to specific the dataset you wish to evaluate on.
'("coco_2017_val",)' can be replace by :
# Lite
python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/images/Lite/Stage2_joint_training_CLIPteacher_R50.yaml --num-gpus 8 --eval-only MODEL.WEIGHTS path/to/GLEE_Lite_joint.pth DATASETS.TEST
'("coco_2017_val",)'
'("lvis_v1_minival",)'
'("lvis_v1_val",)'
'("objects365_v2_val",)'
'("refcoco-unc-val",)'
'("refcoco-unc-testA",)'
'("refcoco-unc-testB",)'
'("refcocoplus-unc-val",)'
'("refcocoplus-unc-testA",)'
'("refcocoplus-unc-testB",)'
'("refcocog-umd-val",)'
'("refcocog-umd-test",)'
# Alternatively, to infer across all tasks at once:
'("coco_2017_val","lvis_v1_minival","lvis_v1_val","objects365_v2_val","refcoco-unc-val","refcoco-unc-testA","refcoco-unc-testB","refcocoplus-unc-val","refcocoplus-unc-testA","refcocoplus-unc-testB","refcocog-umd-val","refcocog-umd-test",)'-
Run the inference scripts:
# YTVIS19 GLEE-Lite python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Lite/ytvis19_base.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Plus_joint.pth # YTVIS19 GLEE-Plus python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Plus/ytvis19_Plus.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Plus_joint.pth # ovis GLEE-Lite python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Lite/ovis_base.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Lite_joint.pth # ovis GLEE-Plus python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Plus/ovis_Plus.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Plus_joint.pth -
Submit the results.zip to online servers.
TAO and BURST share the same video frames.
First, download the validation set zip files (2-TAO_VAL.zip, 2_AVA_HACS_VAL_e49d8f78098a8ffb3769617570a20903.zip) and unzip them from https://motchallenge.net/tao_download.php.
Then, download our preprocessed YTVIS format (COCO-like) annotation files from huggingface:
https://huggingface.co/spaces/Junfeng5/GLEE_demo/tree/main/annotations/TAO
And organize them as below:
${GLEE_ROOT}
-- datasets
-- TAO
--burst_annotations
-- TAO_val_withlabel_ytvisformat.json
-- val
-- all_classes.json
-- ...
--TAO_annotations
-- validation_ytvisfmt.json
-- validation.json
-- frames
-- val
-- ArgoVerse
-- ava
-- ...
-
Run the inference scripts:
python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Lite/TAO_Lite.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Lite_joint.pth python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Plus/TAO_Plus.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Plus_joint.pth -
For TAO, we use teta as our evaluate metric (for more details, please refer to https://github.com/SysCV/tet/blob/main/teta/README.md)
-
Install teta and run evaluation:
git clone https://github.com/SysCV/tet.git cd tet/teta/ pip install -r requirements.txt pip install -e . # eval python3 scripts/run_tao.py --METRICS TETA --TRACKERS_TO_EVAL TETer --GT_FOLDER /path/to/${GLEE_ROOT}/datasets/TAO/TAO_annotations/validation.json --TRACKER_SUB_FOLDER /path/to/${GLEE_ROOT}/GLEE_TAO_Lite_640p/inference/results.json
-
Run the inference scripts:
python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Lite/BURST_Lite.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Lite_joint.pth python3 projects/GLEE/train_net.py --config-file projects/GLEE/configs/video/Plus/BURST_Plus.yaml --eval-only --num-gpus 8 MODEL.WEIGHTS /path/to/GLEE_model_zoo/GLEE_Plus_joint.pth -
Download eval tools from https://github.com/Ali2500/BURST-benchmark and https://github.com/JonathonLuiten/TrackEval:
mkdir burst_tools cd burst_tools git clone https://github.com/Ali2500/BURST-benchmark.git git clone https://github.com/JonathonLuiten/TrackEval.git wget https://huggingface.co/spaces/Junfeng5/GLEE_demo/resolve/main/annotations/convert_ytvis2tao.py -
Run eval codes:
# first convert ytvis format results to TAO/BURST results python3 convert_ytvis2tao.py --results path/to/GLEE_BURST_Lite_720p/inference/results.json --refer /path/to/${GLEE_ROOT}/datasets/TAO/burst_annotations/val/all_classes.json cd BURST-benchmark export TRACKEVAL_DIR=/path/to/burst_tools/TrackEval/ python3 burstapi/eval/run.py --pred ../converted_tao_results.json --gt ../../burst_annotations/val/ --task class_guided