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JacobYuan7 / DIN-Group-Activity-Recognition-Benchmark

[ICCV 2021] A new codebase containing various methods for Group Activity Recognition. Paper title: Spatio-Temporal Dynamic Inference Network for Group Activity Recognition.
MIT License
52 stars 15 forks source link

Was the validation set used for training? #8

Closed hongluzhou closed 2 years ago

hongluzhou commented 2 years ago

Thanks for sharing the code! However, according to the code, it seems that the validation set of the volleyball dataset was used as part of the training set.

https://github.com/cvlab-epfl/social-scene-understanding/blob/b26080785f6ef1c1e0d08ab496744e5e8c1c8d4c/volleyball.py#L4 V.S. https://github.com/JacobYuan7/DIN_GAR/blob/7c5a8f54cc3b713ead438cc47dd54c491c837fbf/config.py#L28

May I ask what is the reason or justification for this? Did any prior effort do the same thing?

JacobYuan7 commented 2 years ago

@hongluzhou Hello, honglu. I have some justification for doing this. The earliest effort for doing this can be traced back to PCTDM: https://github.com/ruiyan1995/Group-Activity-Recognition/blob/1018d00e142f2560561551de38b7d02f431aad50/Configs/Dataset_Configs.py#L22-L27

Then the following work ARG: https://github.com/wjchaoGit/Group-Activity-Recognition/blob/c8c1dff953d694db93b2943ba19888be4bd6c24d/config.py#L28-L29

My prior work on AAAI2021 also adopted this setting. Some of previous works are not publicly available, so I can not ensure that they all adopt this setting. However, according to their performances, I assume that they have done the same.

hongluzhou commented 2 years ago

@JacobYuan7 I see! Thank you very much for the reply!

Could you share the checkpoints? I'm hoping to use your code to obtain ROI Aligned person features. I tried training the stage 1 base model on volleyball on my own (I did not change any settings), but the best accuracy I got is "Best group activity accuracy: 88.56% at epoch #36." I found that you mentioned 86.39% accuracy is bit low in this issue, so I'm not sure whether the accuracy that I got is high enough. Hence hoping you could share the checkpoints!

JacobYuan7 commented 2 years ago

@hongluzhou
Sorry about the late reply. I am quite occupied recently. But feel free to ask questions. I checked the codes again and found the reason that may cause the problem of low performances. I accidentally set the _numframes to be 20. Thus, I revised the code and you should produce similar performances. If you fail to reproduce it, just let me know. I will share a link for the checkpoints. BTW, VGG-16 produces more stable results, thus I recommend using it.

P-DX commented 2 years ago

@hongluzhou Sorry about the late reply. I am quite occupied recently. But feel free to ask questions. I checked the codes again and found the reason that may cause the problem of low performances. I accidentally set the _numframes to be 20. Thus, I revised the code and you should produce similar performances. If you fail to reproduce it, just let me know. I will share a link for the checkpoints. BTW, VGG-16 produces more stable results, thus I recommend using it.

Thanks for sharing the code!

Could you share the checkpoints of stage1 and stage2?

JacobYuan7 commented 2 years ago

@P-DX The checkpoints are available at this link

P-DX commented 2 years ago

@P-DX The checkpoints are available at this link

Thanks a lot!

Does "V_VGG16_base_92.45%.pth" mean we can get the 92.45 at stage1? Could you tell us how to get this result?

daniel-richter commented 2 years ago

As a comparision: I trained with vgg16 as stated in https://github.com/JacobYuan7/DIN_GAR/issues/2#issuecomment-932229980 and get the following results.

./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch192_87.73%.pth
./[SACRF Volleyball_stage2_stage2]<2021-10-01_14-57-51>/stage2_epoch24_92.00%.pth
./[Dynamic Volleyball_stage2_res18_litedim128_reproduce_1_stage2]<2021-11-05_17-00-56>/stage2_epoch16_92.30%.pth
./[ARG Volleyball_stage2_stage2]<2021-10-01_15-16-02>/stage2_epoch23_91.70%.pth
./[AT Volleyball_stage2_stage2]<2021-10-01_13-05-05>/stage2_epoch29_90.43%.pth
./[HiGCIN Volleyball_stage2_stage2]<2021-10-01_15-00-47>/stage2_epoch28_83.62%.pth
./[PCTDM Volleyball_stage2_stage2]<2021-11-05_17-08-22>/stage2_epoch28_91.10%.pth
Script Accuracy
Stage 1: VGG16 87.73%
Stage 2: SACRF 92.00%
Stage 2: Dynamic 92.30%
Stage 2: ARG 91.70%
Stage 2: AT 90.43%
Stage 2: HiGCIN 83.62%
Stage 2: PCTDM 91.10%
JacobYuan7 commented 2 years ago

@P-DX The checkpoints are available at this link

Thanks a lot!

Does "V_VGG16_base_92.45%.pth" mean we can get the 92.45 at stage1? Could you tell us how to get this result?

Simply by running the codes. Would you please tell me the result you get and more details about how you reproduce it? Thus, I can analyze the reasons accordingly.

JacobYuan7 commented 2 years ago

As a comparision: I trained with vgg16 as stated in #2 (comment) and get the following results.

./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch192_87.73%.pth
./[SACRF Volleyball_stage2_stage2]<2021-10-01_14-57-51>/stage2_epoch24_92.00%.pth
./[Dynamic Volleyball_stage2_res18_litedim128_reproduce_1_stage2]<2021-11-05_17-00-56>/stage2_epoch16_92.30%.pth
./[ARG Volleyball_stage2_stage2]<2021-10-01_15-16-02>/stage2_epoch23_91.70%.pth
./[AT Volleyball_stage2_stage2]<2021-10-01_13-05-05>/stage2_epoch29_90.43%.pth
./[HiGCIN Volleyball_stage2_stage2]<2021-10-01_15-00-47>/stage2_epoch28_83.62%.pth
./[PCTDM Volleyball_stage2_stage2]<2021-11-05_17-08-22>/stage2_epoch28_91.10%.pth

Script Accuracy Stage 1: VGG16 87.73% Stage 2: SACRF 92.00% Stage 2: Dynamic 92.30% Stage 2: ARG 91.70% Stage 2: AT 90.43% Stage 2: HiGCIN 83.62% Stage 2: PCTDM 91.10%

Can you give me more information about the script that VGG base model trained with? I never get such low base model performance.

daniel-richter commented 2 years ago

Can you give me more information about the script that VGG base model trained with? I never get such low base model performance.

I started a Docker container and executed python scripts/train_volleyball_stage1.py - without any modifications, as stated in the README.

Ubuntu 16.04.6 LTS
Torch 1.2.0
CUDA 10.0.130
cuDNN 7.6.03
Python 3.6.9

scikit-image 0.17.2
Pillow 6.2.0
thop 0.0.31.post2005241907
fvcore 0.1.2.post20210128
opencv-python 4.5.3.56

To copy the best model I use the following command:

cp $(find ./result -name "*stage1*.pth" -printf '%f %p\n' | sort -t_ -k3 -Vr | cut -f 2- -d ' ' | head -1) ./result/basemodel_VD_vgg16.pth --verbose

The raw saved model file names are:

./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch192_87.73%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch191_87.36%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch154_87.21%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch151_87.06%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch142_86.91%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch119_86.84%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch101_86.84%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch86_86.76%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch78_86.76%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch192_86.39%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch55_86.24%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch110_86.24%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch49_86.16%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch34_86.01%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch31_85.79%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch27_85.49%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch23_85.49%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch38_85.42%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch20_85.34%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch21_85.19%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch20_85.19%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch12_85.12%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch16_84.82%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch16_83.99%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch11_83.77%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch12_83.62%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch8_82.95%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch12_82.72%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch8_81.53%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch9_81.53%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch5_81.45%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch7_81.23%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch7_80.70%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch5_80.55%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch5_80.55%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch4_79.51%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch3_79.43%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch4_77.26%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch3_76.66%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch4_76.66%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch3_76.44%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch2_70.91%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch2_69.33%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch2_69.18%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch1_66.57%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch1_59.76%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch1_58.79%.pth
JacobYuan7 commented 2 years ago

Can you give me more information about the script that VGG base model trained with? I never get such low base model performance.

I started a Docker container and executed python scripts/train_volleyball_stage1.py - without any modifications, as stated in the README.

Ubuntu 16.04.6 LTS
Torch 1.2.0
CUDA 10.0.130
cuDNN 7.6.03
Python 3.6.9

scikit-image 0.17.2
Pillow 6.2.0
thop 0.0.31.post2005241907
fvcore 0.1.2.post20210128
opencv-python 4.5.3.56

To copy the best model I use the following command:

cp $(find ./result -name "*stage1*.pth" -printf '%f %p\n' | sort -t_ -k3 -Vr | cut -f 2- -d ' ' | head -1) ./result/basemodel_VD_vgg16.pth --verbose

The raw saved model file names are:

./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch192_87.73%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch191_87.36%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch154_87.21%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch151_87.06%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch142_86.91%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch119_86.84%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch101_86.84%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch86_86.76%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch78_86.76%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch192_86.39%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch55_86.24%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch110_86.24%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch49_86.16%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch34_86.01%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch31_85.79%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch27_85.49%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch23_85.49%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch38_85.42%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch20_85.34%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch21_85.19%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch20_85.19%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch12_85.12%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch16_84.82%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch16_83.99%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch11_83.77%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch12_83.62%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch8_82.95%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch12_82.72%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch8_81.53%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch9_81.53%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch5_81.45%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch7_81.23%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch7_80.70%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch5_80.55%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch5_80.55%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch4_79.51%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch3_79.43%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch4_77.26%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch3_76.66%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch4_76.66%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch3_76.44%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch2_70.91%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch2_69.33%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch2_69.18%.pth
./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch1_66.57%.pth
./[Volleyball_stage1_stage1]<2021-09-15_19-39-19>/stage1_epoch1_59.76%.pth
./[Volleyball_stage1_stage1]<2021-09-23_12-46-16>/stage1_epoch1_58.79%.pth

What about the training scripts? (including hyper-parameters)

daniel-richter commented 2 years ago

What about the training scripts? (including hyper-parameters)

What did you mean by that? I cloned this repo and run the scripts without any modifications.

The output of python scripts/train_volleyball_stage1.py that created ./[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/stage1_epoch192_87.73%.pth was:

Nov 02 15:09:08 =====================Config=====================
Nov 02 15:09:08 image_size :  (720, 1280)
Nov 02 15:09:08 batch_size :  8
Nov 02 15:09:08 test_batch_size :  1
Nov 02 15:09:08 num_boxes :  12
Nov 02 15:09:08 use_gpu :  True
Nov 02 15:09:08 use_multi_gpu :  False
Nov 02 15:09:08 device_list :  0
Nov 02 15:09:08 dataset_name :  volleyball
Nov 02 15:09:08 data_path :  data/volleyball/videos
Nov 02 15:09:08 train_seqs :  [1, 3, 6, 7, 10, 13, 15, 16, 18, 22, 23, 31, 32, 36, 38, 39, 40, 41, 42, 48, 50, 52, 53, 54, 0, 2, 8, 12, 17, 19, 24, 26, 27, 28, 30, 33, 46, 49, 51]
Nov 02 15:09:08 test_seqs :  [4, 5, 9, 11, 14, 20, 21, 25, 29, 34, 35, 37, 43, 44, 45, 47]
Nov 02 15:09:08 backbone :  vgg16
Nov 02 15:09:08 crop_size :  (5, 5)
Nov 02 15:09:08 train_backbone :  True
Nov 02 15:09:08 out_size :  (22, 40)
Nov 02 15:09:08 emb_features :  512
Nov 02 15:09:08 num_actions :  9
Nov 02 15:09:08 num_activities :  8
Nov 02 15:09:08 actions_loss_weight :  1.0
Nov 02 15:09:08 actions_weights :  [[1.0, 1.0, 2.0, 3.0, 1.0, 2.0, 2.0, 0.2, 1.0]]
Nov 02 15:09:08 num_frames :  1
Nov 02 15:09:08 num_before :  5
Nov 02 15:09:08 num_after :  4
Nov 02 15:09:08 num_features_boxes :  1024
Nov 02 15:09:08 num_features_relation :  256
Nov 02 15:09:08 num_graph :  16
Nov 02 15:09:08 num_features_gcn :  1024
Nov 02 15:09:08 gcn_layers :  1
Nov 02 15:09:08 tau_sqrt :  False
Nov 02 15:09:08 pos_threshold :  0.2
Nov 02 15:09:08 train_random_seed :  0
Nov 02 15:09:08 train_learning_rate :  1e-05
Nov 02 15:09:08 lr_plan :  {}
Nov 02 15:09:08 train_dropout_prob :  0.3
Nov 02 15:09:08 weight_decay :  0
Nov 02 15:09:08 max_epoch :  200
Nov 02 15:09:08 test_interval_epoch :  1
Nov 02 15:09:08 training_stage :  1
Nov 02 15:09:08 stage1_model_path :
Nov 02 15:09:08 test_before_train :  True
Nov 02 15:09:08 exp_note :  Volleyball_stage1
Nov 02 15:09:08 exp_name :  [Volleyball_stage1_stage1]<2021-11-02_15-09-00>
Nov 02 15:09:08 set_bn_eval :  False
Nov 02 15:09:08 inference_module_name :  dynamic_volleyball
Nov 02 15:09:08 stride :  1
Nov 02 15:09:08 ST_kernel_size :  3
Nov 02 15:09:08 dynamic_sampling :  True
Nov 02 15:09:08 sampling_ratio :  [1, 3]
Nov 02 15:09:08 group :  1
Nov 02 15:09:08 scale_factor :  True
Nov 02 15:09:08 beta_factor :  True
Nov 02 15:09:08 load_backbone_stage2 :  False
Nov 02 15:09:08 parallel_inference :  False
Nov 02 15:09:08 hierarchical_inference :  False
Nov 02 15:09:08 lite_dim :  None
Nov 02 15:09:08 num_DIM :  1
Nov 02 15:09:08 load_stage2model :  False
Nov 02 15:09:08 stage2model :  None
Nov 02 15:09:08 temporal_pooled_first :  False
Nov 02 15:09:08 halting_penalty :  0.0001
Nov 02 15:09:08 result_path :  result/[Volleyball_stage1_stage1]<2021-11-02_15-09-00>
Nov 02 15:09:08 log_path :  result/[Volleyball_stage1_stage1]<2021-11-02_15-09-00>/log.txt
Nov 02 15:09:08 ======================End=======================
Nov 02 15:09:08 Reading dataset finished...
Nov 02 15:09:08 3493 train samples
Nov 02 15:09:08 1337 test samples
Nov 02 15:19:15 {'time': 606.4387230873108, 'epoch': 0, 'loss': 7.11802880676629, 'activities_acc': 12.789827973074047, 'actions_acc': 5.204437796060833}
Nov 02 15:27:05
Nov 02 15:27:05 Train at epoch #1
Nov 02 15:27:05 Group Activity Accuracy: 38.56%, Loss: 3.47269, Using 470.8 seconds

...

Nov 05 02:07:34 Train at epoch #200
Nov 05 02:07:34 Group Activity Accuracy: 99.74%, Loss: 0.04666, Using 469.6 seconds
Nov 05 02:07:34
Nov 05 02:07:34
Nov 05 02:17:23
Nov 05 02:17:23 ====> Test at epoch #200
Nov 05 02:17:23 Group Activity Accuracy: 84.14%, Loss: 3.01014, Using 588.4 seconds
Nov 05 02:17:23
Nov 05 02:17:23
Nov 05 02:17:23 Best group activity accuracy: 87.73% at epoch #192.
hongluzhou commented 2 years ago

link

@JacobYuan7 Thank you so much for replying and sharing the checkpoints! Could you share the link again? The link seems to be expired and I can't download the checkpoints anymore. (Also sorry about my late reply! )

Best, Honglu

JacobYuan7 commented 2 years ago

@hongluzhou Hello Honglu, it's available now.

JacobYuan7 commented 2 years ago

@daniel-richter I updated the training script. It should produce better results for the base model.

hongluzhou commented 2 years ago

@hongluzhou Hello Honglu, it's available now.

Thank you so much! I'm gonna close this issue. Thanks again!!

Best, Honglu

daniel-richter commented 2 years ago

@daniel-richter I updated the training script. It should produce better results for the base model.

Thanks. Now it is Best group activity accuracy: 91.92% at epoch #111.