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  1. ======================================================
  2. [{'name': 'input_tensor', 'index': 0, 'shape': array([ 1, 320, 320, 3]), 'shape_signature': array([ 1, 320, 320, 3]), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]
  3. ======================================================
  4. {'name': 'Identity', 'index': 1366, 'shape': array([1, 1]), 'shape_signature': array([ 1, -1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}
  5.  
  6. {'name': 'Identity_1', 'index': 1419, 'shape': array([1, 1, 1]), 'shape_signature': array([ 1, -1, -1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}
  7.  
  8. {'name': 'Identity_2', 'index': 1401, 'shape': array([1, 1]), 'shape_signature': array([ 1, -1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}
  9.  
  10. {'name': 'Identity_3', 'index': 1384, 'shape': array([1, 1, 1]), 'shape_signature': array([ 1, -1, -1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}
  11.  
  12. {'name': 'Identity_4', 'index': 1348, 'shape': array([1, 1]), 'shape_signature': array([ 1, -1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}
  13.  
  14. {'name': 'Identity_5', 'index': 1331, 'shape': array([1]), 'shape_signature': array([1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}
  15.  
  16. {'name': 'Identity_6', 'index': 465, 'shape': array([ 1, 12804, 4]), 'shape_signature': array([ 1, 12804, 4]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}
  17.  
  18. {'name': 'Identity_7', 'index': 470, 'shape': array([ 1, 12804, 44]), 'shape_signature': array([ 1, 12804, 44]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}
  19.  
  20. Detection anchor indices: [[ 4978. 4000. 3952. 5500. 4192. 4732. 5740. 4978. 5212. 4240.
  21. 3712. 4198. 3958. 4432. 5494. 3946. 526. 5260. 4978. 4438.
  22. 4186. 5734. 4708. 4126. 3706. 3718. 8952. 3886. 5830. 5458.
  23. 4132. 4294. 4366. 4948. 3892. 3994. 532. 8958. 3472. 4372.
  24. 4120. 3880. 3226. 5224. 4534. 4000. 8958. 2992. 4612. 4300.
  25. 3466. 5206. 4000. 3232. 4702. 4984. 4150. 8952. 3478. 4552.
  26. 4954. 4192. 5254. 3646. 5836. 4606. 5980. 3754. 634. 4360.
  27. 4480. 12702. 5482. 3652. 3952. 2998. 1966. 4732. 3640. 3976.
  28. 4138. 12702. 2950. 4972. 4144. 5446. 4966. 4390. 2902. 4462.
  29. 4852. 4468. 5476. 4498. 3874. 4492. 5212. 4648. 4054. 3520.]]
  30. Boxes: [[[0.46521667 0.7184442 0.5352696 0.7609366 ]
  31. [0.3698166 0.64502877 0.4369641 0.6859012 ]
  32. [0.3771396 0.44950515 0.4411768 0.4879378 ]
  33. [0.52391064 0.8931645 0.59093535 0.933632 ]
  34. [0.40040353 0.44837245 0.46194777 0.48687658]
  35. [0.45594728 0.70393634 0.52386844 0.7433249 ]
  36. [0.5392424 0.89180535 0.60620546 0.9347603 ]
  37. [0.46521667 0.7184442 0.5352696 0.7609366 ]
  38. [0.47787902 0.69498634 0.5449509 0.73925436]
  39. [0.38521504 0.6427934 0.4529072 0.68617487]
  40. [0.3559907 0.44970033 0.42110103 0.4884849 ]
  41. [0.40249798 0.46590108 0.46146283 0.5041858 ]
  42. [0.37854415 0.46628696 0.44070184 0.50444067]
  43. [0.42789432 0.45001328 0.48658958 0.48744977]
  44. [0.5238015 0.8747581 0.59204465 0.91485226]
  45. [0.37833345 0.42538098 0.44506449 0.4639658 ]
  46. [0.00731527 0.16187054 0.08831388 0.21444863]
  47. [0.5048381 0.89382976 0.5722631 0.93287057]
  48. [0.46521667 0.7184442 0.5352696 0.7609366 ]
  49. [0.43147686 0.46747032 0.48852536 0.50438863]
  50. [0.40250152 0.42443395 0.46610117 0.46267486]
  51. [0.5406477 0.8721804 0.6092774 0.9137464 ]
  52. [0.45390004 0.5936034 0.5147254 0.6313201 ]
  53. [0.39841393 0.1678877 0.46612063 0.20895632]
  54. [0.3530013 0.42384472 0.41944414 0.46341452]
  55. [0.35688686 0.46665204 0.4214816 0.5059255 ]
  56. [0.86568636 0.26024356 0.9999564 0.36324182]
  57. [0.3723985 0.16815095 0.44088602 0.20971365]
  58. [0.5850878 0.27880904 0.65501106 0.31675783]
  59. [0.51298255 0.7189672 0.57497865 0.7600292 ]
  60. [0.3972843 0.19216534 0.46536598 0.23400235]
  61. [0.39880612 0.8715372 0.4644076 0.91094327]
  62. [0.426306 0.16899702 0.49181527 0.20889351]
  63. [0.47442412 0.59417844 0.53658795 0.6322731 ]
  64. [0.37193838 0.19253491 0.4408823 0.23508339]
  65. [0.37140998 0.6253109 0.43787846 0.66565084]
  66. [0.01349203 0.18768138 0.08998884 0.23548377]
  67. [0.85640126 0.28172833 0.99543446 0.38695478]
  68. [0.3213453 0.4446925 0.38998654 0.4878527 ]
  69. [0.42562377 0.19329855 0.49036884 0.23361227]
  70. [0.40077233 0.14282386 0.46674222 0.18253751]
  71. [0.37383395 0.14290167 0.4411708 0.18336119]
  72. [0.27894968 0.41473222 0.3610469 0.46815044]
  73. [0.49520287 0.73661053 0.5574928 0.77713525]
  74. [0.42510155 0.8720028 0.48967084 0.9105076 ]
  75. [0.3698166 0.64502877 0.4369641 0.6859012 ]
  76. [0.85640126 0.28172833 0.99543446 0.38695478]
  77. [0.24720025 0.4371552 0.3453089 0.49899107]
  78. [0.45307285 0.19495916 0.5171634 0.23387253]
  79. [0.4000609 0.89273536 0.46734184 0.9331298 ]
  80. [0.31671315 0.419139 0.38722324 0.46421978]
  81. [0.49725547 0.66841584 0.56107986 0.7087793 ]
  82. [0.3698166 0.64502877 0.4369641 0.6859012 ]
  83. [0.2745984 0.43644968 0.3601134 0.49072602]
  84. [0.45229918 0.57291526 0.5166492 0.61107594]
  85. [0.479708 0.7348699 0.5431593 0.7719036 ]
  86. [0.4029255 0.26697415 0.467291 0.30608433]
  87. [0.86568636 0.26024356 0.9999564 0.36324182]
  88. [0.32563704 0.46492147 0.39296877 0.50767916]
  89. [0.41521955 0.9564591 0.4858253 0.9975187 ]
  90. [0.4792748 0.6167067 0.5390107 0.65308845]
  91. [0.40040353 0.44837245 0.46194777 0.48687658]
  92. [0.5022755 0.87540317 0.5705778 0.91440475]
  93. [0.35047185 0.16883163 0.41778594 0.20973347]
  94. [0.59484273 0.29136807 0.6578434 0.3273986 ]
  95. [0.4525983 0.17068478 0.517935 0.20948535]
  96. [0.56176394 0.89408356 0.62903947 0.93628067]
  97. [0.35495564 0.62697834 0.4207224 0.6663075 ]
  98. [0.00799625 0.61690074 0.07971115 0.6614447 ]
  99. [0.42719063 0.14267132 0.4916493 0.1817317 ]
  100. [0.4207591 0.6433173 0.4860094 0.6835323 ]
  101. [0.44195437 0.29667258 1. 0.7147149 ]
  102. [0.5319426 0.8176237 0.5971459 0.8553708 ]
  103. [0.35139406 0.19302231 0.41842765 0.23451477]
  104. [0.3771396 0.44950515 0.4411768 0.4879378 ]
  105. [0.24361771 0.4534095 0.34045476 0.5126367 ]
  106. [0.17403248 0.16587362 0.23941043 0.20733887]
  107. [0.45594728 0.70393634 0.52386844 0.7433249 ]
  108. [0.3495251 0.14261152 0.4168768 0.18295671]
  109. [0.37613466 0.5434967 0.44101956 0.58579206]
  110. [0.40034625 0.2164496 0.46724078 0.2575662 ]
  111. [0.44195437 0.29667258 1. 0.7147149 ]
  112. [0.25619757 0.2646564 0.35432893 0.32540533]
  113. [0.46332732 0.7008713 0.5342548 0.74338406]
  114. [0.40258244 0.24263026 0.4676021 0.28221935]
  115. [0.52790004 0.66789746 0.5900094 0.70659065]
  116. [0.47682285 0.6726058 0.543771 0.7120889 ]
  117. [0.43011317 0.2675123 0.4904643 0.30527744]
  118. [0.276359 0.06863823 0.344027 0.10934045]
  119. [0.4309367 0.572825 0.4948737 0.61129576]
  120. [0.47782737 0.19551212 0.54341024 0.23405686]
  121. [0.43292248 0.5912916 0.49322504 0.62945896]
  122. [0.52640915 0.7956557 0.59133136 0.83434355]
  123. [0.42200935 0.715626 0.5038775 0.76394165]
  124. [0.37724227 0.11695611 0.44106132 0.15588552]
  125. [0.429419 0.6981435 0.4992037 0.74046195]
  126. [0.47787902 0.69498634 0.5449509 0.73925436]
  127. [0.46588072 0.34829122 0.52215433 0.382881 ]
  128. [0.37325716 0.8705448 0.44151568 0.91168964]
  129. [0.3345155 0.64440185 0.40674347 0.68637365]]]
  130. Classes: [[ 2. 2. 2. 2. 2. 2. 2. 10. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.
  131. 3. 2. 2. 2. 2. 2. 2. 2. 6. 2. 2. 2. 2. 2. 2. 2. 2. 2.
  132. 2. 6. 2. 2. 2. 2. 2. 2. 2. 3. 5. 2. 2. 2. 2. 2. 10. 2.
  133. 2. 2. 2. 5. 2. 2. 2. 10. 2. 2. 2. 2. 2. 2. 2. 2. 2. 11.
  134. 2. 2. 10. 2. 2. 10. 2. 2. 2. 6. 2. 3. 2. 2. 2. 2. 2. 2.
  135. 2. 2. 2. 2. 2. 2. 10. 2. 2. 2.]]
  136. Multiclass scores: [[[0.00265756 0.06033984 0.52877843 ... 0.04179654 0.02609545 0.0266228 ]
  137. [0.00270933 0.06398529 0.47833073 ... 0.03520814 0.02723366 0.02942285]
  138. [0.00212896 0.05545843 0.45421752 ... 0.02625731 0.02475893 0.0275937 ]
  139. ...
  140. [0.00279969 0.02631518 0.25531405 ... 0.01215678 0.01838645 0.01389092]
  141. [0.00200635 0.03241014 0.2543683 ... 0.01472256 0.01702851 0.0143626 ]
  142. [0.00419801 0.03217068 0.2540201 ... 0.02139649 0.02824858 0.01730928]]]
  143. Scores: [[0.52877843 0.47833073 0.45421752 0.44444585 0.43971634 0.43414393
  144. 0.41329736 0.40996432 0.3953153 0.3918856 0.38400343 0.36077625
  145. 0.36038506 0.35767642 0.3541311 0.34693164 0.34524345 0.3410777
  146. 0.33804938 0.33647424 0.33464932 0.33109495 0.32497406 0.3208604
  147. 0.31964833 0.31768495 0.31572184 0.3131352 0.31179065 0.30814803
  148. 0.3074053 0.30623427 0.30533302 0.30336043 0.3026716 0.30063337
  149. 0.30047178 0.30025342 0.29857737 0.29821536 0.2951373 0.29473096
  150. 0.2928502 0.29211685 0.29098886 0.2882353 0.28698665 0.2867356
  151. 0.28673443 0.28668007 0.28645274 0.2862987 0.28549862 0.28142917
  152. 0.2803246 0.27975333 0.27926558 0.2792431 0.27813697 0.2778862
  153. 0.27778935 0.2773381 0.27665251 0.275999 0.27497664 0.2744791
  154. 0.2743043 0.27286056 0.27239698 0.27124265 0.27123857 0.26925558
  155. 0.26884001 0.26701343 0.26687104 0.26649028 0.26641458 0.26619977
  156. 0.2651347 0.2650686 0.26401442 0.26350468 0.26336846 0.26292622
  157. 0.26253214 0.2618887 0.26169014 0.26084355 0.26027182 0.26011246
  158. 0.25974655 0.25891995 0.25852582 0.2577828 0.25727612 0.2558649
  159. 0.25540823 0.25531405 0.2543683 0.2540201 ]]
  160. Num detections: [100.]
  161. Raw boxes: [[[-0.02057116 -0.00990371 0.06491157 0.05281114]
  162. [-0.05401782 -0.03605912 0.09056567 0.07188669]
  163. [-0.01497794 -0.00848343 0.04582704 0.06633476]
  164. ...
  165. [ 0.05013597 -0.98994803 1.5187693 2.4219792 ]
  166. [ 0.0419147 0.45522875 1.6943116 1.2758071 ]
  167. [-0.4039808 0.11618823 2.0189106 1.5406828 ]]]
  168. 149 230 171 243
  169. 118 206 140 219
  170. 121 144 141 156
  171. 168 286 189 299
  172. 128 143 148 156
  173. 146 225 168 238
  174. 173 285 194 299
  175. 149 230 171 243
  176. 153 222 174 237
  177. 123 206 145 220
  178.  
  179. Process finished with exit code 0
  180.  
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