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[Udemy] Automatic Number Plate Recognition, OCR Web App in Python ()

  • 收录时间:2022-02-15 02:06:33
  • 文件大小:2GB
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  • 最近下载:2022-02-15 02:06:33
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文件列表

  1. 1. Introduction/2.1 Project_Files.zip 473MB
  2. 8. Number Plate Web App/6. Integrate Deep Learning Object Detection Model.mp4 142MB
  3. 3. Data Processing/3. Data Preprocessing.mp4 83MB
  4. 2. Labeling/5. XML to CSV.mp4 82MB
  5. 8. Number Plate Web App/8. Display Output in HTML Page.mp4 78MB
  6. 5. Pipeline Object Detection Model/1. Make Predictions.mp4 75MB
  7. 8. Number Plate Web App/9. Display Output in HTML Page part 2.mp4 71MB
  8. 6. Optical Character Recognition (OCR)/3. Exrtract Number Plate text from Image.mp4 67MB
  9. 8. Number Plate Web App/7. Integrate Number Plate Detection and OCR to Flask App.mp4 67MB
  10. 3. Data Processing/1. Read Data.mp4 61MB
  11. 8. Number Plate Web App/5. HTTP Method Upload File in Flask.mp4 57MB
  12. 5. Pipeline Object Detection Model/5. Create Pipeline.mp4 55MB
  13. 3. Data Processing/2. Verify Labeled Data.mp4 49MB
  14. 6. Optical Character Recognition (OCR)/1. Install Tesseract.mp4 48MB
  15. 7. Flask App/3. Render HTML Template.mp4 48MB
  16. 4. Deep Learning for Object Detection/2. InceptionResnet V2 model building.mp4 45MB
  17. 2. Labeling/3. Install Dependencies.mp4 40MB
  18. 5. Pipeline Object Detection Model/4. Bounding Box.mp4 39MB
  19. 7. Flask App/1. Install Visual Studio Code.mp4 39MB
  20. 7. Flask App/2. First Flask App.mp4 38MB
  21. 2. Labeling/4. Label Images.mp4 32MB
  22. 5. Pipeline Object Detection Model/3. De-normalize the Output.mp4 31MB
  23. 5. Pipeline Object Detection Model/2. Make Predictions part2.mp4 30MB
  24. 4. Deep Learning for Object Detection/8. Tensorboard.mp4 28MB
  25. 3. Data Processing/4. Split train and test set.mp4 27MB
  26. 8. Number Plate Web App/1. Create Web App.mp4 26MB
  27. 7. Flask App/4. Import Boostrap.mp4 26MB
  28. 4. Deep Learning for Object Detection/6. InceptionResnet V2 Training - Part 2.mp4 25MB
  29. 4. Deep Learning for Object Detection/7. Save Deep Learning Model.mp4 24MB
  30. 4. Deep Learning for Object Detection/4. Compiling Model.mp4 24MB
  31. 8. Number Plate Web App/4. Upload Form in HTML.mp4 23MB
  32. 2. Labeling/2. Download Image Annotation Tool.mp4 23MB
  33. 8. Number Plate Web App/3. Template Inheritance.mp4 22MB
  34. 4. Deep Learning for Object Detection/5. InceptionResnet V2 Training.mp4 21MB
  35. 2. Labeling/1. Get the Data.mp4 19MB
  36. 4. Deep Learning for Object Detection/1. Get Transfer Learning from TensorFlow 2.x.mp4 17MB
  37. 4. Deep Learning for Object Detection/3. Defining Inputs and Outputs.mp4 14MB
  38. 6. Optical Character Recognition (OCR)/2. Install Pytesseract.mp4 13MB
  39. 8. Number Plate Web App/2. Footer.mp4 13MB
  40. 1. Introduction/1. Project Architecture.mp4 12MB
  41. 2. Labeling/2.1 labelImg-master.zip 6MB
  42. 8. Number Plate Web App/6. Integrate Deep Learning Object Detection Model.srt 15KB
  43. 5. Pipeline Object Detection Model/1. Make Predictions.srt 11KB
  44. 3. Data Processing/3. Data Preprocessing.srt 11KB
  45. 8. Number Plate Web App/8. Display Output in HTML Page.srt 9KB
  46. 8. Number Plate Web App/5. HTTP Method Upload File in Flask.srt 9KB
  47. 3. Data Processing/1. Read Data.srt 8KB
  48. 7. Flask App/3. Render HTML Template.srt 8KB
  49. 8. Number Plate Web App/9. Display Output in HTML Page part 2.srt 7KB
  50. 4. Deep Learning for Object Detection/2. InceptionResnet V2 model building.srt 7KB
  51. 6. Optical Character Recognition (OCR)/3. Exrtract Number Plate text from Image.srt 7KB
  52. 3. Data Processing/2. Verify Labeled Data.srt 7KB
  53. 2. Labeling/5. XML to CSV.srt 7KB
  54. 7. Flask App/2. First Flask App.srt 6KB
  55. 8. Number Plate Web App/7. Integrate Number Plate Detection and OCR to Flask App.srt 6KB
  56. 5. Pipeline Object Detection Model/5. Create Pipeline.srt 6KB
  57. 5. Pipeline Object Detection Model/4. Bounding Box.srt 5KB
  58. 6. Optical Character Recognition (OCR)/1. Install Tesseract.srt 5KB
  59. 5. Pipeline Object Detection Model/2. Make Predictions part2.srt 5KB
  60. 4. Deep Learning for Object Detection/8. Tensorboard.srt 5KB
  61. 7. Flask App/1. Install Visual Studio Code.srt 5KB
  62. 5. Pipeline Object Detection Model/3. De-normalize the Output.srt 4KB
  63. 3. Data Processing/4. Split train and test set.srt 4KB
  64. 8. Number Plate Web App/4. Upload Form in HTML.srt 4KB
  65. 4. Deep Learning for Object Detection/5. InceptionResnet V2 Training.srt 4KB
  66. 8. Number Plate Web App/1. Create Web App.srt 4KB
  67. 1. Introduction/1. Project Architecture.srt 3KB
  68. 8. Number Plate Web App/3. Template Inheritance.srt 3KB
  69. 7. Flask App/4. Import Boostrap.srt 3KB
  70. 4. Deep Learning for Object Detection/1. Get Transfer Learning from TensorFlow 2.x.srt 3KB
  71. 4. Deep Learning for Object Detection/7. Save Deep Learning Model.srt 3KB
  72. 4. Deep Learning for Object Detection/4. Compiling Model.srt 3KB
  73. 4. Deep Learning for Object Detection/6. InceptionResnet V2 Training - Part 2.srt 3KB
  74. 8. Number Plate Web App/2. Footer.srt 2KB
  75. 2. Labeling/4. Label Images.srt 2KB
  76. 6. Optical Character Recognition (OCR)/2. Install Pytesseract.srt 2KB
  77. 4. Deep Learning for Object Detection/3. Defining Inputs and Outputs.srt 2KB
  78. 2. Labeling/2. Download Image Annotation Tool.srt 2KB
  79. 2. Labeling/1. Get the Data.srt 1KB
  80. 2. Labeling/3. Install Dependencies.srt 1KB
  81. 9. BONUS/1. Bonus Lecture.html 685B
  82. 1. Introduction/2. Download the Resources.html 113B