This document provides tutorials to train and evaluate CenterNet. Before getting started, make sure you have finished installation and dataset setup.
First, download the models you want to evaluate from our model zoo and put them in CenterNet_ROOT/models/.
To evaluate COCO object detection with DLA run
python test.py ctdet --exp_id coco_dla --keep_res --load_model ../models/ctdet_coco_dla_2x.pth
This will give an AP of 37.4 if setup correctly. --keep_res is for keep the original image resolution. Without --keep_res it will resize the images 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. The expected APs are 39.2 and 41.7, respectively.
To test with hourglass net, run
python test.py ctdet --exp_id coco_hg --arch hourglass --fix_res --load_model ../models/ctdet_coco_hg.pth
Similarly, to evaluate human pose estimation, run the following command for dla
python test.py multi_pose --exp_id dla --keep_res --load_model ../models/multi_pose_dla_3x.pth --flip_test
and the following for hourglass
python test.py multi_pose --exp_id hg --arch hourglass --keep_res --load_model ../models/multi_pose_dla_3x.pth --flip_test
The expected results can be found in the model zoo.
To evaluate object detection on Pascal VOC (test2007), run
python test.py ctdet --exp_id dla --dataset pascal --load_model ../models/ctdet_pascal_dla.pth --flip_test
Note that we fix the resolution during testing.
And you can change to other network architectures and resolutions by specifying --arch and --input_res 512.
To evaluate the kitti dataset, first compile the evaluation tool (from here):
cd CenterNet_ROOT/src/tools/kitti_eval
g++ -o evaluate_object_3d_offline evaluate_object_3d_offline.cpp -O3
Then run the evaluation with pretrained model:
python test.py ddd --exp_id 3dop --dataset kitti --kitti_split 3dop --load_model ../models/ddd_3dop.pth
to evaluate the 3DOP split. For the subcnn split, change --kitti_split to subcnn and load the corresponding models.
Note that test time augmentation is not trivially applicable for 3D orientation.
We have packed all the training scripts in the experiments folder. The experiment names are correspond to the model name in the model zoo. The number of GPUs for each experiments can be found in the scripts and the model zoo. In the case that you don't have 8 GPUs, you can follow the linear learning rate rule to scale the learning rate as batch size. For example, to train COCO object detection with dla on 2 GPUs, run
python main.py ctdet --exp_id coco_dla --batch_size 32 --master_batch 15 --lr 1.25e-4 --gpus 0,1
The default learning rate is 1.25e-4 for batch size 32 (on 2 GPUs).
By default, pytorch evenly splits the total batch size to each GPUs.
--master_batch allows using different batchsize for the master GPU, which usually costs more memory than other GPUs.
If it encounters GPU memory out, using slightly less batch size (e.g., 112 of 128) with the same learning is fine.
If the training is terminated before finishing, you can use the same commond with --resume to resume training. It will found the lastest model with the same exp_id.
Our HourglassNet model is finetuned from the pretrained ExtremeNet model (from the ExtremeNet repo).
You will need to download the model, run python convert_hourglass_weight.py to convert the model format, and load the model for training (see the script).