Does this model really work? Basically cannot identify non-dataset videos
Tsingtong opened this issue · 1 comments
When I use the model to try to identify the real Obama video, basically all videos are detected with a 95% probability that they are face-changing videos ...
One of the detection videos is as follows：
https://www.youtube.com/watch?v=sHAkDTlv8fA
The clip detection log is as follows：
('path:', '/dlib_model/shape_predictor_68_face_landmarks.dat')
2020-01-19 15:45:07.637407: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2020-01-19 15:45:07.637432: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2020-01-19 15:45:07.637439: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2020-01-19 15:45:07.637445: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2020-01-19 15:45:07.637451: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX512F instructions, but these are available on your machine and could speed up CPU computations.
2020-01-19 15:45:07.637472: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2020-01-19 15:45:07.815133: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce RTX 2080 Ti
major: 7 minor: 5 memoryClockRate (GHz) 1.545
pciBusID 0000:65:00.0
Total memory: 10.73GiB
Free memory: 10.02GiB
2020-01-19 15:45:07.815162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2020-01-19 15:45:07.815166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2020-01-19 15:45:07.815189: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:65:00.0)
Loading checkpoint /home/gky/PycharmProjects/CVPRW2019_Face_Artifacts/ckpt_res50/model
Testing: dataset_test/test/obama_real.mp4
('detecting', 0)
('probs.append:', 0.9868792)
('detecting', 1)
('probs.append:', 0.80856305)
('detecting', 2)
('probs.append:', 0.94188154)
('detecting', 3)
('probs.append:', 0.90303326)
('detecting', 4)
('probs.append:', 0.99767286)
('detecting', 5)
('probs.append:', 0.9614075)
('detecting', 6)
('probs.append:', 0.9595191)
('detecting', 7)
('probs.append:', 0.8166154)
('detecting', 8)
('probs.append:', 0.97614515)
('detecting', 9)
('probs.append:', 0.54254496)
('detecting', 10)
('probs.append:', 0.6386844)
('detecting', 11)
('probs.append:', 0.08990705)
('detecting', 12)
('probs.append:', 0.74948525)
('detecting', 13)
('probs.append:', 0.36588773)
('detecting', 14)
('probs.append:', 0.96228063)
('detecting', 15)
('probs.append:', 0.7279479)
('detecting', 16)
('probs.append:', 0.99728185)
('detecting', 17)
('probs.append:', 0.24376407)
('detecting', 18)
('probs.append:', 0.47520953)
('detecting', 19)
('probs.append:', 0.6742674)
('detecting', 20)
('probs.append:', 0.94513416)
('detecting', 21)
('probs.append:', 0.75601804)
('detecting', 22)
('probs.append:', 0.6526342)
('detecting', 23)
('probs.append:', 0.05435108)
('detecting', 24)
('probs.append:', 0.014932161)
('detecting', 25)
('probs.append:', 0.008214104)
('detecting', 26)
('probs.append:', 0.013158442)
('detecting', 27)
('probs.append:', 0.007968158)
('detecting', 28)
('probs.append:', 0.13961957)
('detecting', 29)
('probs.append:', 0.01184213)
('detecting', 30)
('probs.append:', 0.04152037)
('detecting', 31)
('probs.append:', 0.013649449)
('detecting', 32)
('probs.append:', 0.8725276)
('detecting', 33)
('probs.append:', 0.99986696)
('detecting', 34)
('probs.append:', 0.99989355)
('detecting', 35)
('probs.append:', 0.99965155)
('detecting', 36)
('probs.append:', 0.9992038)
('detecting', 37)
('probs.append:', 0.99040616)
('detecting', 38)
('probs.append:', 0.99737704)
('detecting', 39)
('probs.append:', 0.99870396)
('detecting', 40)
('probs.append:', 0.99959004)
('detecting', 41)
('probs.append:', 0.9452702)
('detecting', 42)
('probs.append:', 0.9321868)
('detecting', 43)
('probs.append:', 0.7133198)
('detecting', 44)
('probs.append:', 0.99842083)
('detecting', 45)
('probs.append:', 0.781696)
('detecting', 46)
('probs.append:', 0.95321435)
('detecting', 47)
('probs.append:', 0.95700437)
('detecting', 48)
('probs.append:', 0.99963474)
('detecting', 49)
('probs.append:', 0.98757046)
('detecting', 50)
('probs.append:', 0.99522686)
('detecting', 51)
('probs.append:', 0.991934)
('detecting', 52)
('probs.append:', 0.99616414)
('detecting', 53)
('probs.append:', 0.99031055)
('detecting', 54)
('probs.append:', 0.98140895)
('detecting', 55)
('probs.append:', 0.9323953)
('detecting', 56)
('probs.append:', 0.9830782)
('detecting', 57)
('probs.append:', 0.9264949)
('detecting', 58)
('probs.append:', 0.971208)
('detecting', 59)
('probs.append:', 0.9561466)
('detecting', 60)
('probs.append:', 0.99475)
('detecting', 61)
('probs.append:', 0.9748002)
('detecting', 62)
('probs.append:', 0.99026966)
('detecting', 63)
('probs.append:', 0.9886033)
('detecting', 64)
('probs.append:', 0.959894)
('detecting', 65)
('probs.append:', 0.9171568)
('detecting', 66)
('probs.append:', 0.9559196)
('detecting', 67)
('probs.append:', 0.9780694)
('detecting', 68)
('probs.append:', 0.9636385)
('detecting', 69)
('probs.append:', 0.965563)
('detecting', 70)
('probs.append:', 0.9263999)
('detecting', 71)
('probs.append:', 0.99452513)
('detecting', 72)
('probs.append:', 0.99809915)
('detecting', 73)
('probs.append:', 0.9980097)
('detecting', 74)
('probs.append:', 0.99830693)
('detecting', 75)
('probs.append:', 0.79650193)
('detecting', 76)
('probs.append:', 0.9734209)
('detecting', 77)
('probs.append:', 0.4492724)
('detecting', 78)
('probs.append:', 0.94296664)
('detecting', 79)
('probs.append:', 0.5423608)
('detecting', 80)
('probs.append:', 0.98778963)
('detecting', 81)
('probs.append:', 0.30337232)
('detecting', 82)
('probs.append:', 0.6011144)
('detecting', 83)
('probs.append:', 0.34220523)
('detecting', 84)
('probs.append:', 0.84168375)
('detecting', 85)
('probs.append:', 0.46755737)
('detecting', 86)
('probs.append:', 0.49161664)
('detecting', 87)
('probs.append:', 0.39203954)
('detecting', 88)
('probs.append:', 0.71996564)
('detecting', 89)
('probs.append:', 0.58421355)
Prob: 0.99316144
Testing: dataset_test/test/obama_real.log
Prob: 0.99316144
Hi sir, but did you solve the problems in this code ?