Can't get expected result after runing the code.

[Genius-pig]

The output seems not to have any errors, but the final result is really bad.

2023-08-02 22:20:21.207936: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
 ------ Spotting CASME_sq micro-expression -------

 ------ Croping Images ------
Subject s15
Subject s16
Subject s19
Subject s20
Subject s21
Subject s22
Subject s23
Subject s24
Subject s25
Subject s26
Subject s27
Subject s29
Subject s30
Subject s31
Subject s32
Subject s33
Subject s34
Subject s35
Subject s36
Subject s37
Subject s38
Subject s40

 ------ Loading Images ------
Subject: s15
Subject: s16
Subject: s19
Subject: s20
Subject: s21
Subject: s22
Subject: s23
Subject: s24
Subject: s25
Subject: s26
Subject: s27
Subject: s29
Subject: s30
Subject: s31
Subject: s32
Subject: s33
Subject: s34
Subject: s35
Subject: s36
Subject: s37
Subject: s38
Subject: s40

 ------ Loading Excel ------

 ------ Loading Ground Truth From Excel ------
Total Videos: 33

 ------ Computing k ------
k (Half of average length of expression) = 6

 ------ Feature Extraction & Pre-processing ------
Video 0 Done
Video 1 Done
Video 2 Done
Video 3 Done
Video 4 Done
Video 5 Done
Video 6 Done
Video 7 Done
Video 8 Done
Video 9 Done
Video 10 Done
Video 11 Done
Video 12 Done
Video 13 Done
Video 14 Done
Video 15 Done
Video 16 Done
Video 17 Done
Video 18 Done
Video 19 Done
Video 20 Done
Video 21 Done
Video 22 Done
Video 23 Done
Video 24 Done
Video 25 Done
Video 26 Done
Video 27 Done
Video 28 Done
Video 29 Done
Video 30 Done
Video 31 Done
Video 32 Done
All Done

 ------ Pseudo-Labeling ------
Total frames: 80463

 ------ Leave one Subject Out ------
Frame Index for each subject:-
Subject 0 : 0 -> 3336
Subject 1 : 3336 -> 14273
Subject 2 : 14273 -> 18638
Subject 3 : 18638 -> 19703
Subject 4 : 19703 -> 26673
Subject 5 : 26673 -> 29790
Subject 6 : 29790 -> 44942
Subject 7 : 44942 -> 47211
Subject 8 : 47211 -> 52966
Subject 9 : 52966 -> 61673
Subject 10 : 61673 -> 71111
Subject 11 : 71111 -> 72186
Subject 12 : 72186 -> 77205
Subject 13 : 77205 -> 80463

Total X: 80463 , Total y: 80463

 ------ SOFTNet Training & Testing ------
2023-08-03 13:44:00.084185: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2023-08-03 13:44:00.130458: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:09:00.0 name: NVIDIA GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.725GHz coreCount: 46 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2023-08-03 13:44:00.130586: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2023-08-03 13:44:00.137630: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2023-08-03 13:44:00.137722: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2023-08-03 13:44:00.144323: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2023-08-03 13:44:00.152444: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2023-08-03 13:44:00.256133: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2023-08-03 13:44:00.264745: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2023-08-03 13:44:00.265462: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2023-08-03 13:44:00.265563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2023-08-03 13:44:00.266174: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-03 13:44:00.439964: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x215d9258ab0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2023-08-03 13:44:00.440066: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2023-08-03 13:44:00.440971: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:09:00.0 name: NVIDIA GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.725GHz coreCount: 46 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2023-08-03 13:44:00.441142: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2023-08-03 13:44:00.441473: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2023-08-03 13:44:00.441768: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2023-08-03 13:44:00.442052: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2023-08-03 13:44:00.442336: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2023-08-03 13:44:00.442652: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2023-08-03 13:44:00.443101: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2023-08-03 13:44:00.443408: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2023-08-03 13:44:00.443711: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2023-08-03 13:44:05.095469: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-08-03 13:44:05.095575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0
2023-08-03 13:44:05.096288: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N
2023-08-03 13:44:05.100301: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6573 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 3070, pci bus id: 0000:09:00.0, compute capability: 8.6)
2023-08-03 13:44:05.114755: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x216aeadfba0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-08-03 13:44:05.115189: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 3070, Compute Capability 8.6
Subject : 1
------Initializing SOFTNet-------
D:\Softwares\miniconda3\envs\softnet_spotme\lib\site-packages\tensorflow\python\keras\engine\training.py:1905: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
  warnings.warn('`Model.predict_generator` is deprecated and '
2023-08-03 13:44:06.162629: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2023-08-03 13:44:06.415811: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2023-08-03 13:44:13.414818: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2023-08-03 13:44:13.611201: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2023-08-03 13:44:26.898090: I tensorflow/core/platform/windows/subprocess.cc:308] SubProcess ended with return code: 0

2023-08-03 13:44:27.020066: I tensorflow/core/platform/windows/subprocess.cc:308] SubProcess ended with return code: 0

2023-08-03 13:44:27.508400: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
278/278 [==============================] - 25s 14ms/step
Video: 0
Video: 1
TP: 1 FP: 34 FN: 1
Done Subject 1
Subject : 2
------Initializing SOFTNet-------
911/911 [==============================] - 14s 15ms/step
Video: 2
Video: 3
Video: 4
Video: 5
Video: 6
TP: 2 FP: 134 FN: 8
Done Subject 2
Subject : 3
------Initializing SOFTNet-------
363/363 [==============================] - 4s 12ms/step
Video: 7
TP: 2 FP: 142 FN: 9
Done Subject 3
Subject : 4
------Initializing SOFTNet-------
88/88 [==============================] - 1s 13ms/step
Video: 8
TP: 2 FP: 155 FN: 12
Done Subject 4
Subject : 5
------Initializing SOFTNet-------
580/580 [==============================] - 8s 14ms/step
Video: 9
Video: 10
TP: 3 FP: 207 FN: 19
Done Subject 5
Subject : 6
------Initializing SOFTNet-------
259/259 [==============================] - 4s 14ms/step
Video: 11
Video: 12
TP: 4 FP: 238 FN: 22
Done Subject 6
Subject : 7
------Initializing SOFTNet-------
1262/1262 [==============================] - 19s 15ms/step
Video: 13
Video: 14
Video: 15
Video: 16
Video: 17
Video: 18
TP: 7 FP: 351 FN: 30
Done Subject 7
Subject : 8
------Initializing SOFTNet-------
189/189 [==============================] - 3s 16ms/step
Video: 19
TP: 7 FP: 372 FN: 31
Done Subject 8
Subject : 9
------Initializing SOFTNet-------
479/479 [==============================] - 7s 14ms/step
Video: 20
Video: 21
TP: 8 FP: 416 FN: 32
Done Subject 9
Subject : 10
------Initializing SOFTNet-------
725/725 [==============================] - 12s 16ms/step
Video: 22
Video: 23
Video: 24
TP: 9 FP: 447 FN: 37
Done Subject 10
Subject : 11
------Initializing SOFTNet-------
786/786 [==============================] - 11s 14ms/step
Video: 25
Video: 26
Video: 27
Video: 28
TP: 13 FP: 487 FN: 39
Done Subject 11
Subject : 12
------Initializing SOFTNet-------
89/89 [==============================] - 1s 14ms/step
Video: 29
TP: 13 FP: 494 FN: 41
Done Subject 12
Subject : 13
------Initializing SOFTNet-------
418/418 [==============================] - 6s 14ms/step
Video: 30
Video: 31
TP: 14 FP: 525 FN: 42
Done Subject 13
Subject : 14
------Initializing SOFTNet-------
271/271 [==============================] - 4s 14ms/step
Video: 32
TP: 14 FP: 546 FN: 43
Done Subject 14
TP: 14 FP: 546 FN: 43
Precision =  0.025
Recall =  0.2456
F1-Score =  0.0454
COCO AP@[.5:.95]: 0.0016

[genbing99]

The outputs of using the pre-trained weights should be similar to the results commented in training.py, I suggest you double-check the code.

[ANGLE404]

I get the same output. How do I use the weights

TP: 14 FP: 546 FN: 43 Done Subject 14 TP: 14 FP: 546 FN: 43 Precision = 0.025 Recall = 0.2456 F1-Score = 0.0454 COCO AP@[.5:.95]: 0.0016

[ANGLE404]

I get the same output. How do I use the weights

TP: 14 FP: 546 FN: 43 Done Subject 14 TP: 14 FP: 546 FN: 43 Precision = 0.025 Recall = 0.2456 F1-Score = 0.0454 COCO AP@[.5:.95]: 0.0016

[genbing99]

You can use this setting to utilize the pretrained weights: python main.py --train False

[genbing99]

Closing due to inactivity.