Open jkpark0825 opened 2 years ago
I can't reproduce the results of hand pose estimation using the provided checkpoint as well. I got even worse results when trained from scratch. Could @stevenlsw please help explain how to reproduce the performance claimed in the paper? Thanks in advance.
Hi, @fuqichen1998, can you provide more training and testing logs as well as result details. Thanks!
Hi @stevenlsw , thanks for your reply! Below is the score output of the results generated using the checkpoint provided:
Collecting open3d-python
Downloading https://files.pythonhosted.org/packages/5f/5c/a86082dc5efc3d22585e8aa22f9840667d9faa5e727b47c43137090caed4/open3d_python-0.7.0.0-cp27-cp27mu-manylinux1_x86_64.whl (3.7MB)
Requirement already satisfied: numpy in /opt/conda/lib/python2.7/site-packages (from open3d-python)
Requirement already satisfied: notebook in /opt/conda/lib/python2.7/site-packages (from open3d-python)
Collecting widgetsnbextension (from open3d-python)
Downloading https://files.pythonhosted.org/packages/d7/31/7c1107fa30c621cd1d36410e9bbab86f6a518dc208aaec01f02ac6d5c2d2/widgetsnbextension-3.5.2-py2.py3-none-any.whl (1.6MB)
Requirement already satisfied: ipywidgets in /opt/conda/lib/python2.7/site-packages (from open3d-python)
Installing collected packages: widgetsnbextension, open3d-python
Successfully installed open3d-python-0.7.0.0 widgetsnbextension-3.5.2
Loading predictions from /tmp/codalab/tmpyXVmia/run/input/res/pred.json
Evaluation 3D KP results:
auc=0.490, mean_kp3d_avg=3.00 cm
Evaluation 3D KP PROCRUSTES ALIGNED results:
auc=0.797, mean_kp3d_avg=1.02 cm
Evaluation 3D KP SCALE-TRANSLATION ALIGNED results:
auc=0.507, mean_kp3d_avg=2.93 cm
Evaluation 3D MESH results:
auc=0.503, mean_kp3d_avg=2.89 cm
Evaluation 3D MESH ALIGNED results:
auc=0.804, mean_kp3d_avg=0.98 cm
F-scores
F@5.0mm = 0.232 F_aligned@5.0mm = 0.529
F@15.0mm = 0.685 F_aligned@15.0mm = 0.950
Scores written to: /tmp/codalab/tmpyXVmia/run/output/scores.txt
Evaluation complete.
and below is the content of the option.txt:
====== Options ======
HO3D_root: /mnt/ssd/qichen/ho3d_v2
blocks: 1
channels: 256
epochs: 60
evaluate: True
host_folder: exp_results
inp_res: 512
lambda_joints2d: 100.0
lambda_objects: 500.0
lr: 0.0001
lr_decay_gamma: 0.7
lr_decay_step: 10
mano_lambda_joints3d: 10000.0
mano_lambda_manopose: 10
mano_lambda_manoshape: 0.1
mano_lambda_regulpose: 1
mano_lambda_regulshape: 100.0
mano_lambda_verts3d: 10000.0
mano_neurons: [1024, 512]
mano_root: assets/mano_models
manual_seed: 0
momentum: 0.9
network: honet_transformer
obj_model_root: assets/object_models
resume: /home/qichen/Semi-Hand-Object/pretrained_models/model.pth.tar
save_results: True
snapshot: 10
stacks: 1
test_batch: 24
test_freq: 10
train_batch: 24
transformer_depth: 1
transformer_head: 1
use_cuda: 1
weight_decay: 0.0005
workers: 16
=====================
launched traineval.py at 2021-12-09 02:21:53.174338
and below is the content of object_results.txt
REP-5
{'021_bleach_cleanser': 0.3959691760521636, '006_mustard_bottle': 0.22134831460674156, '010_potted_meat_can': 0.055685131195335275}
ADD-10
{'021_bleach_cleanser': 0.8847065797273266, '006_mustard_bottle': 0.5696629213483146, '010_potted_meat_can': 0.48892128279883385}
I can also provide the logs about training the model by myself and its score.
Thanks for the info. I am not fully sure the linked model is the right one. I am in travel now. I upload my local saved checkpoints https://drive.google.com/file/d/1Y4fICIY63MA4J1FiY8QfBAF_AKzMEjDn. Could you please @fuqichen1998 help me to take some evaluation. Thanks in advance.
The new checkpoint gives the same results as reported in the paper, thanks @stevenlsw. But after training the model from scratch multiple times, I can't achieve, or even get close to, the performance of the new checkpoint. Is there any difference in the training of the released checkpoint?
Thanks so much for testing. The only difference in the provided checkpoint is that we are incorporating more pseudo labels from Something-v2 dataset as training data.
Got it, thanks for your reply!
Hi @stevenlsw, after several rounds of training, I could not reach the performance you reported training solely on the HO3D dataset. Could you please provide the instruction to reproduce the performance reported in the paper? (Also is this: https://github.com/stevenlsw/Semi-Hand-Object/blob/1aaf6eea5bdcfbacfdc3a04c8958baa2932e4de7/utils/options.py#L36 a typo or there is only 1 head for the contextual reasoning?)
Hi @fuqichen1998. Could you share the evaluation of groundtruth json to me? Thanks in advance.
@dncfjy we don't have it, it's hidden in the backend of the official HO3D challenge evaluation portal.
@fuqichen1998 Now the channel uploaded for the result prediction I do not see on the web page. Can you share a link with me? Thank you very much.
When I run the code as written in the readme.md, the result is different from trained model. Why is it different and how to get trained model?