This document provides tutorials to train and evaluate CenterNet. Before getting started, make sure you have finished installation and dataset setup.
To evaluate COCO object detection with HarDNet-85 run
python test.py ctdet --exp_id coco_h85 --arch hardnet_85 --load_model centernet_hardnet85_coco.pth
This will give an AP of 44.0 if setup correctly. The input images are resized to 512 x 512. You can add --flip_test and --flip_test --test_scales 0.5,0.75,1,1.25,1.5 to the above commend, for flip test and multi_scale test, respectively.
We have packed all the training scripts in the experiments folder.
python main.py ctdet --exp_id coco_h85 --arch hardnet_85 --batch_size 48 --master_batch 24 --lr 1e-2 --gpus 0,1 --num_workers 16 --num_epochs 300 --lr_step 230,280