Open uzumaki-noharu opened 1 year ago
I got the same problem that Prec@1 0.07676% Prec@5 0.19502%. Do you figured it out?
I solved this problem that I replace the --pretrain_path to --resume_path, and it works. Honestly, I see this solution in another issue, but I have not solved this problem at the first time. I download the code again and use the parameters like these: --root_path F:/RealTimeGD/Real-time-GesRec-master --video_path nvGesture --annotation_path annotation_nvGesture/nvall_but_None.json --result_path results --modality Depth --pretrain_modality Depth --dataset nvgesture --n_classes 25 --n_finetune_classes 25 --sample_duration 32 --downsample 1 --resume_path nv_resnext_101_Depth_32.pth --test_subset test --model resnext --model_depth 101 --downsample 1
I got this: Overall Prec@1 0.83402% Prec@5 0.98133%
Hi @uzumaki-noharu, Have you tried with Egogesture dataset? did that work? I'm having an issue to run the main.py.
Hi, @ahmetgunduz I downloaded your pre-training model and used offline_ The test.py file tests its accuracy on the nvGesture data set, but the results show that its accuracy is very low, with errors such as
UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples Use
zero_ division
parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))The following is the result after filtering the warning:
[1/8] Time 4.59765 (4.59765) prec@1 0.71875 prec@5 0.98438 precision 0.40512 (0.40512) recall 0.38369 (0.38369) [2/8] Time 3.16794 (3.88279) prec@1 0.72656 prec@5 0.99219 precision 0.31801 (0.36157) recall 0.25735 (0.32052) [3/8] Time 3.16407 (3.64322) prec@1 0.76042 prec@5 0.98958 precision 0.41215 (0.37843) recall 0.36193 (0.33433) [4/8] Time 3.31298 (3.56066) prec@1 0.81250 prec@5 0.98438 precision 0.83333 (0.49216) recall 0.81458 (0.45439) [5/8] Time 3.10335 (3.46920) prec@1 0.82500 prec@5 0.98438 precision 0.60476 (0.51468) recall 0.54767 (0.47305) [6/8] Time 3.20778 (3.42563) prec@1 0.82292 prec@5 0.97917 precision 0.53571 (0.51818) recall 0.46732 (0.47209) [7/8] Time 3.16362 (3.38820) prec@1 0.83036 prec@5 0.98214 precision 0.55556 (0.52352) recall 0.50741 (0.47714) [8/8] Time 1.68080 (3.17477) prec@1 0.83402 prec@5 0.98133 precision 0.33333 (0.51011) recall 0.29630 (0.46438) -----Evaluation is finished------ Overall Prec@1 0.83402% Prec@5 0.98133%
At the beginning of reading the nvgesture dataset, because the dataset does not have a
sk_color_all
folder, the code cannot read the data, so I processed the video into pictures and created this folder.It contains every frame of video.But when I finished processing and successfully ran the code, the result was very unsatisfactory. I don't know whether my parameters are set incorrectly or my dataset is not processed properly. Can you help me to have a look?Thank you very much!
Here are my parameter settings:
python offline_test.py \ --root_path ./ \ --pretrain_path models/models/nv_resnext_101_Depth_32.pth \ --resume_path models/models/nv_resnext_101_Depth_32.pth \ --video_path ../../dataset/nvgesture/ \ --annotation_path annotation_nvGesture/nvall_but_None.json \ --result_path results \ --dataset nvgesture \ --downsample 1 \ --sample_duration 32 \ --learning_rate 0.01 \ --resnet_shortcut B \ --model resnext \ --pretrain_modality Depth \ --model_depth 101 \ --batch_size 64 \ --n_classes 25 \ --n_finetune_classes 25 \ --modality Depth \ --n_threads 0 \ --train_crop random \ --checkpoint 1 \ --train_crop corner \ --n_val_samples 1 \ --test_subset val \ --n_epochs 100 \
so, could you please tell me what you put in sk_color_all folder?
Hi, @ahmetgunduz I downloaded your pre-training model and used offline_ The test.py file tests its accuracy on the nvGesture data set, but the results show that its accuracy is very low, with errors such as
The following is the result after filtering the warning:
At the beginning of reading the nvgesture dataset, because the dataset does not have a
sk_color_all
folder, the code cannot read the data, so I processed the video into pictures and created this folder.It contains every frame of video.But when I finished processing and successfully ran the code, the result was very unsatisfactory. I don't know whether my parameters are set incorrectly or my dataset is not processed properly. Can you help me to have a look?Thank you very much!
Here are my parameter settings: