GetFreeCourses Co-Udemy-Modern Computer Vision™ PyTorch Tensorflow2 Keras & OpenCV4

1. 1. Introduction/1. Course Introduction.mp482.94MB
2. 1. Introduction/2. Course Overview.mp463.77MB
3. 1. Introduction/3. What Makes Computer Vision Hard.mp446.16MB
4. 1. Introduction/4. What are Images.mp444.22MB
5. 10. OpenCV - Working With Video/1. Using Your Webcam and Creating a Live Sketch of Yourself.mp466.54MB
6. 10. OpenCV - Working With Video/2. Opening Video Files in OpenCV.mp433.99MB
7. 10. OpenCV - Working With Video/3. Saving or Recording Videos in OpenCV.mp435.77MB
8. 10. OpenCV - Working With Video/4. Video Streams and CCTV - RTSP and IP.mp442.09MB
9. 10. OpenCV - Working With Video/5. Auto Reconnect to Video Streams.mp436.24MB
10. 10. OpenCV - Working With Video/6. Capturing Video using Screenshots.mp446.3MB
11. 10. OpenCV - Working With Video/7. Importing YouTube Videos into OpenCV.mp464.89MB
12. 11. Deep Learning in Computer Vision Introduction/1. Introduction to Convolution Neural Networks.mp415.87MB
13. 11. Deep Learning in Computer Vision Introduction/10. Fully Connected Layers.mp411.33MB
14. 11. Deep Learning in Computer Vision Introduction/11. Softmax.mp48.88MB
15. 11. Deep Learning in Computer Vision Introduction/12. Putting Together Your Convolutional Neural Network.mp429.38MB
16. 11. Deep Learning in Computer Vision Introduction/13. Parameter Counts in CNNs.mp423.83MB
17. 11. Deep Learning in Computer Vision Introduction/14. Why CNNs Work So Well On Images.mp420.51MB
18. 11. Deep Learning in Computer Vision Introduction/15. Training a CNN.mp427.38MB
19. 11. Deep Learning in Computer Vision Introduction/16. Loss Functions.mp424.89MB
20. 11. Deep Learning in Computer Vision Introduction/17. Backpropagation.mp429MB
21. 11. Deep Learning in Computer Vision Introduction/18. Gradient Descent.mp432.94MB
22. 11. Deep Learning in Computer Vision Introduction/19. Optimisers and Learning Rate Schedules.mp440.92MB
23. 11. Deep Learning in Computer Vision Introduction/2. Convolutions.mp433.85MB
24. 11. Deep Learning in Computer Vision Introduction/20. Deep Learning CNN Recap.mp436.39MB
25. 11. Deep Learning in Computer Vision Introduction/21. Deep Learning History.mp457.52MB
26. 11. Deep Learning in Computer Vision Introduction/22. Deep Learning Libraries Overview.mp459.04MB
27. 11. Deep Learning in Computer Vision Introduction/3. Feature Detectors.mp423.87MB
28. 11. Deep Learning in Computer Vision Introduction/4. 3D Convolutions and Color Images.mp416.63MB
29. 11. Deep Learning in Computer Vision Introduction/5. Kernel Size and Depth.mp413.67MB
30. 11. Deep Learning in Computer Vision Introduction/6. Padding.mp414.17MB
31. 11. Deep Learning in Computer Vision Introduction/7. Stride.mp417.14MB
32. 11. Deep Learning in Computer Vision Introduction/8. Activation Functions.mp421.74MB
33. 11. Deep Learning in Computer Vision Introduction/9. Pooling.mp423.42MB
34. 12. Building CNNs in PyTorch/1. Importing Required Libraries.mp447.72MB
35. 12. Building CNNs in PyTorch/2. Transformation Pipeline.mp429.55MB
36. 12. Building CNNs in PyTorch/3. Inspect and Visualise Data.mp470.58MB
37. 12. Building CNNs in PyTorch/4. Data Loaders.mp429.9MB
38. 12. Building CNNs in PyTorch/5. Building our Model.mp4102.34MB
39. 12. Building CNNs in PyTorch/6. Optimisers and Loss Function.mp414.88MB
40. 12. Building CNNs in PyTorch/7. Training Your Model.mp496.07MB
41. 12. Building CNNs in PyTorch/8. Saving Model and Displaying Results.mp449.27MB
42. 12. Building CNNs in PyTorch/9. Plot and Visualize Your Results.mp425.81MB
43. 13. Building CNNs in TensorFlow with Keras/1. Loading Data.mp426.69MB
44. 13. Building CNNs in TensorFlow with Keras/2. View and Inspect Data.mp429.51MB
45. 13. Building CNNs in TensorFlow with Keras/3. Preprocessing Our Data.mp433.53MB
46. 13. Building CNNs in TensorFlow with Keras/4. Constructing the CNN.mp464.47MB
47. 13. Building CNNs in TensorFlow with Keras/5. Training the Model.mp445.45MB
48. 13. Building CNNs in TensorFlow with Keras/6. Plotting the Training Results.mp436.39MB
49. 13. Building CNNs in TensorFlow with Keras/7. Saving and Loading and Visualising Results.mp475.55MB
50. 14. Assessing Model Performance/1. Deep Learning Libraries PyTorch vs Keras Review.mp468.2MB
51. 14. Assessing Model Performance/2. Assessing Model Performance.mp422.58MB
52. 14. Assessing Model Performance/3. Confusion Matrix and Classification Report.mp467.59MB
53. 14. Assessing Model Performance/4. Keras Viewing Misclassifications.mp464.91MB
54. 14. Assessing Model Performance/5. Keras - Confusion Matrix and Classification Report.mp443.42MB
55. 14. Assessing Model Performance/6. PyTorch Viewing Misclassifications.mp451.37MB
56. 14. Assessing Model Performance/7. PyTorch - Confusion Matrix and Misclassifications.mp429.25MB
57. 15. Improving Models and Advanced CNN Design/1. What is Overfitting and Generalisation.mp444.04MB
58. 15. Improving Models and Advanced CNN Design/10. Training a Fashion Classifider (FNIST) with Regularization using Keras.mp496.01MB
59. 15. Improving Models and Advanced CNN Design/11. Training a Fashion Classifider (FNIST) with no Regularization using PyTorch.mp464.16MB
60. 15. Improving Models and Advanced CNN Design/12. Training a Fashion Classifider (FNIST) with Regularization using PyTorch.mp4108.05MB
61. 15. Improving Models and Advanced CNN Design/2. Introduction to Regularization.mp48.02MB
62. 15. Improving Models and Advanced CNN Design/3. Drop Out.mp413.06MB
63. 15. Improving Models and Advanced CNN Design/4. L1 and L2 Regularization.mp415.62MB
64. 15. Improving Models and Advanced CNN Design/5. Data Augmentation.mp431.9MB
65. 15. Improving Models and Advanced CNN Design/6. Early Stopping.mp412.98MB
66. 15. Improving Models and Advanced CNN Design/7. Batch Normalization.mp423.84MB
67. 15. Improving Models and Advanced CNN Design/8. When Do We Use Regularization.mp411.57MB
68. 15. Improving Models and Advanced CNN Design/9. Training a Fashion Classifider (FNIST) with no Regularization using Keras.mp482.69MB
69. 16. Visualizing What CNN's Learn/1. Visualizing CNN Filters or Feature Maps.mp420.47MB
70. 16. Visualizing What CNN's Learn/2. Visualising Filter Activations.mp435.54MB
71. 16. Visualizing What CNN's Learn/3. Keras Filter Visualization and Activations.mp4107.23MB
72. 16. Visualizing What CNN's Learn/4. Maximizing Filters.mp423.06MB
73. 16. Visualizing What CNN's Learn/5. Class Maximization.mp430.13MB
74. 16. Visualizing What CNN's Learn/6. Filter and Class Maximization.mp4156.06MB
75. 16. Visualizing What CNN's Learn/7. Grad-CAM Visualize What Influences Your Model.mp414.91MB
76. 16. Visualizing What CNN's Learn/8. Grad-CAM Plus.mp480.37MB
77. 17. Advamced Convolutional Neural Networks/1. History and Evolution of Convolutional Neural Networks.mp49.13MB
78. 17. Advamced Convolutional Neural Networks/10. EfficientNet.mp424.99MB
79. 17. Advamced Convolutional Neural Networks/11. DenseNet.mp432.02MB
80. 17. Advamced Convolutional Neural Networks/12. The ImageNet Dataset.mp429.53MB
81. 17. Advamced Convolutional Neural Networks/2. LeNet.mp418.67MB
82. 17. Advamced Convolutional Neural Networks/3. AlexNet.mp417.53MB
83. 17. Advamced Convolutional Neural Networks/4. VGG16 and VGG19.mp423.05MB
84. 17. Advamced Convolutional Neural Networks/5. ResNets.mp417.8MB
85. 17. Advamced Convolutional Neural Networks/6. Why ResNets Work So Well.mp423.29MB
86. 17. Advamced Convolutional Neural Networks/7. MobileNetV1 and V2.mp443.82MB
87. 17. Advamced Convolutional Neural Networks/8. InceptionV3.mp423.42MB
88. 17. Advamced Convolutional Neural Networks/9. SqueezeNet.mp423.18MB
89. 18. Building and Loading Advanced CNN Archiectures and Rank-N Accuracy/1. Implementing LeNet and AlexNet in Keras.mp4139.34MB
90. 18. Building and Loading Advanced CNN Archiectures and Rank-N Accuracy/2. Loading Pre-trained Networks in PyTorch (ResNets, DenseNets, MobileNET, VGG19).mp4153.77MB
91. 18. Building and Loading Advanced CNN Archiectures and Rank-N Accuracy/3. Loading Pre-trained Networks in Keras (ResNets, DenseNets, MobileNET, VGG19).mp4111.38MB
92. 18. Building and Loading Advanced CNN Archiectures and Rank-N Accuracy/4. The Top-N or Rank-N Accuracy Metric.mp412.42MB
93. 18. Building and Loading Advanced CNN Archiectures and Rank-N Accuracy/5. Getting the Rank-N Accuracy in PyTorch.mp497.92MB
94. 18. Building and Loading Advanced CNN Archiectures and Rank-N Accuracy/6. Getting the Rank-N Accuracy in Keras.mp455.85MB
95. 19. Using Callbacks in Keras and PyTorch/1. What are Callbacks.mp416.18MB
96. 19. Using Callbacks in Keras and PyTorch/2. Cats vs Dogs Classifier using Callbacks in PyTorch.mp4114.98MB
97. 19. Using Callbacks in Keras and PyTorch/3. Cats vs Dogs Classifier using Callbacks in Keras.mp4114.72MB
98. 2. Download Code and Setup Colab/2. Setup - Download Code and Configure Colab.mp422.57MB
99. 20. PyTorch Lightning/1. Introduction to PyTorch Lightning.mp438.24MB
100. 20. PyTorch Lightning/2. Lightning Setup and Class.mp462.15MB
101. 20. PyTorch Lightning/3. Auto Batch and Learning Rate Selection plus Tensorboards.mp4106.56MB
102. 20. PyTorch Lightning/4. PyTorch Lightning Calls, Saving, Inference.mp471.64MB
103. 20. PyTorch Lightning/5. Training on Multiple GPU, Profiling and TPUs.mp464.15MB
104. 21. Transfer Learning and Fine Tuning/1. Transfer Learning Introduction.mp431.89MB
105. 21. Transfer Learning and Fine Tuning/2. Transfer Learning in PyTorch Lightning.mp455.89MB
106. 21. Transfer Learning and Fine Tuning/3. Transfer Learning and Fine Tuning with Keras.mp496MB
107. 21. Transfer Learning and Fine Tuning/4. Keras Feature Extraction.mp4131.81MB
108. 21. Transfer Learning and Fine Tuning/5. PyTorch Fine Tuning.mp4121.59MB
109. 21. Transfer Learning and Fine Tuning/6. PyTorch Transfer Learning and Freezing Network Layers.mp428.81MB
110. 21. Transfer Learning and Fine Tuning/7. PyTorch Feature Extraction.mp4103.4MB
111. 22. Google DeepStream and Neural Style Transfer/1. Introduction to Google DeepDream Visualizations.mp439.87MB
112. 22. Google DeepStream and Neural Style Transfer/2. Google DeepDream in Keras.mp472.85MB
113. 22. Google DeepStream and Neural Style Transfer/3. Google DeepDream in PyTorch.mp458.32MB
114. 22. Google DeepStream and Neural Style Transfer/4. Introduction to Neural Style Transfer.mp449.97MB
115. 22. Google DeepStream and Neural Style Transfer/5. Neural Style Transfer in Keras.mp4141.08MB
116. 22. Google DeepStream and Neural Style Transfer/6. Neural Style Transfer in PyTorch.mp457.9MB
117. 23. Autoencoders/1. Introduction to Autoencoders.mp425.37MB
118. 23. Autoencoders/2. Autoencoders in Keras.mp480.55MB
119. 23. Autoencoders/3. Autoencoders in PyTorch.mp466.04MB
120. 24. Generative Adversarial Networks (GANs)/1. Introduction to GANs.mp443.57MB
121. 24. Generative Adversarial Networks (GANs)/2. How Do GANs Work.mp426.55MB
122. 24. Generative Adversarial Networks (GANs)/3. Training GANs.mp449.27MB
123. 24. Generative Adversarial Networks (GANs)/4. Use Cases for GANs.mp4108.13MB
124. 24. Generative Adversarial Networks (GANs)/5. Keras DCGAN with MNIST.mp4100.92MB
125. 24. Generative Adversarial Networks (GANs)/6. PyTorch GANs.mp465.41MB
126. 24. Generative Adversarial Networks (GANs)/7. Super Resolution GAN.mp4102.24MB
127. 24. Generative Adversarial Networks (GANs)/8. AnimeGAN.mp434.77MB
128. 24. Generative Adversarial Networks (GANs)/9. ArcaneGAN.mp431.55MB
129. 25. Siamese Network/1. Introduction to Siamese Networks.mp428.8MB
130. 25. Siamese Network/2. Training Siamese Networks.mp413.34MB
131. 25. Siamese Network/3. Siamese Networks in Keras.mp473.01MB
132. 25. Siamese Network/4. Siamese Networks in PyTorch.mp467.92MB
133. 26. Face Recognition (Age, Gender, Emotion and Ethnicity) with Deep Learning/1. Face Recognition Overview.mp432.03MB
134. 26. Face Recognition (Age, Gender, Emotion and Ethnicity) with Deep Learning/2. Facial Similarity Keras VGGFace.mp456.62MB
135. 26. Face Recognition (Age, Gender, Emotion and Ethnicity) with Deep Learning/3. Face Recognition Keras One Shot Learning and Friends.mp480.6MB
136. 26. Face Recognition (Age, Gender, Emotion and Ethnicity) with Deep Learning/4. Face Recognition PyTorch FaceNet.mp451.29MB
137. 26. Face Recognition (Age, Gender, Emotion and Ethnicity) with Deep Learning/5. DeepFace - Age, Gender, Emotion, Ethnicity and Face Recognition.mp4132.36MB
138. 27. Object Detection/1. Object Detection.mp465.25MB
139. 27. Object Detection/2. History of Object Detectors.mp452.87MB
140. 27. Object Detection/3. Intersection Over Union.mp417.44MB
141. 27. Object Detection/4. Mean Average Precision.mp445.71MB
142. 27. Object Detection/5. Non Maximum Suppression.mp419.55MB
143. 27. Object Detection/6. R-CNNs, Fast R-CNNs and Faster R-CNNs.mp441.2MB
144. 27. Object Detection/7. Single Shot Detectors (SSDs).mp430.93MB
145. 28. Modern Object Detectors - YOLO, EfficientDet, Detectron2/1. Introduction to YOLO.mp433.48MB
146. 28. Modern Object Detectors - YOLO, EfficientDet, Detectron2/2. How YOLO Works.mp429.31MB
147. 28. Modern Object Detectors - YOLO, EfficientDet, Detectron2/3. Training YOLO.mp425.03MB
148. 28. Modern Object Detectors - YOLO, EfficientDet, Detectron2/4. YOLO Evolution.mp424.18MB
149. 28. Modern Object Detectors - YOLO, EfficientDet, Detectron2/5. EfficientDet.mp430.74MB
150. 28. Modern Object Detectors - YOLO, EfficientDet, Detectron2/6. Detectron2.mp440.59MB
151. 29. Gun Detector - Scaled-YoloV4/1. Gun Detector - Scaled-YoloV4.mp4128.47MB
152. 3. OpenCV - Image Operations/1. Getting Started with OpenCV4.mp494.74MB
153. 3. OpenCV - Image Operations/10. Dilation, Erosion and Edge Detection.mp479.96MB
154. 3. OpenCV - Image Operations/2. Grayscaling Images.mp452.52MB
155. 3. OpenCV - Image Operations/3. Colour Spaces - RGB and HSV.mp468.3MB
156. 3. OpenCV - Image Operations/4. Drawing on Images.mp452.03MB
157. 3. OpenCV - Image Operations/5. Transformations - Translations and Rotations.mp467.22MB
158. 3. OpenCV - Image Operations/6. Scaling, Re-sizing, Interpolations and Cropping.mp4114.94MB
159. 3. OpenCV - Image Operations/7. Arithmetic and Bitwise Operations.mp466.14MB
160. 3. OpenCV - Image Operations/8. Convolutions, Blurring and Sharpening Images.mp458.96MB
161. 3. OpenCV - Image Operations/9. Thresholding, Binarization & Adaptive Thresholding.mp4117.18MB
162. 30. Mask Detector TFODAPI MobileNetV2_SSD/1. Mask Detector TFODAPI MobileNetV2_SSD.mp481.18MB
163. 31. Sign Language Detector TFODAPI EfficentDet/1. Sign Language Detector TFODAPI EfficentDet.mp485.17MB
164. 32. Pothole Detector - TinyYOLOv4/1. Pothole Detector - TinyYOLOv4.mp459.49MB
165. 33. Mushroom Detector Detectron2/1. Mushroom Detector Detectron2.mp464.96MB
166. 34. Website Region Detector YOLOv4 Darknet/1. Website Region Detector YOLOv4 Darknet.mp449.63MB
167. 35. Drone Maritime Detector R-CNN/1. Drone Maritime Detector R-CNN.mp456.86MB
168. 36. Chess Piece YOLOv3/1. Chess Piece YOLOv3.mp442.66MB
169. 37. Bloodcell Detector YOLOv5/1. Bloodcell Detector YOLOv5.mp458.54MB
170. 38. Hard Hat Detector EfficentDet/1. Hard Hat Detector EfficentDet.mp430.6MB
171. 39. Plant Doctor Detector YOLOv5/1. Plant Doctor Detector YOLOv5.mp471.12MB
172. 4. OpenCV - Image Segmentation/1. Contours - Drawing, Hierarchy and Modes.mp4117.12MB
173. 4. OpenCV - Image Segmentation/2. Moments, Sorting, Approximating and Matching Contours.mp4139.77MB
174. 4. OpenCV - Image Segmentation/3. Line, Circle and Blob Detection.mp456.57MB
175. 4. OpenCV - Image Segmentation/4. Counting Circles, Ellipses and Finding Waldo with Template Matching.mp468.51MB
176. 4. OpenCV - Image Segmentation/5. Finding Corners.mp436.53MB
177. 40. Deep Segmentation - U-Net, SegNet, DeeplabV3 and Mask R-CNN/1. Introduction to Deep Segmentation.mp478.17MB
178. 40. Deep Segmentation - U-Net, SegNet, DeeplabV3 and Mask R-CNN/2. Image Segmentation Keras UNET SegNet.mp473.05MB
179. 40. Deep Segmentation - U-Net, SegNet, DeeplabV3 and Mask R-CNN/3. PyTorch DeepLabV3.mp460.15MB
180. 40. Deep Segmentation - U-Net, SegNet, DeeplabV3 and Mask R-CNN/4. Mask-RCNN Tensorflow Matterport.mp461.72MB
181. 40. Deep Segmentation - U-Net, SegNet, DeeplabV3 and Mask R-CNN/5. Detectron2 Mask R-CNN.mp470.89MB
182. 40. Deep Segmentation - U-Net, SegNet, DeeplabV3 and Mask R-CNN/6. Train Mask R-CNN Shapes Dataset.mp454.97MB
183. 41. Body Pose Estimation/1. Body Pose Estimation.mp446.21MB
184. 42. Tracking with DeepSORT/1. DeepSORT Introduction.mp459.56MB
185. 42. Tracking with DeepSORT/2. DeepSORT with YOLOv5.mp458.34MB
186. 43. Deep Fakes/1. Creating a Deep Fake.mp458.67MB
187. 44. Vision Transformers - ViTs/1. Introduction to Vision Transformers.mp426.74MB
188. 44. Vision Transformers - ViTs/2. Vision Transformer in Detail with PyTorch.mp476.24MB
189. 44. Vision Transformers - ViTs/3. Vision Transformers in Keras.mp447.99MB
190. 45. BiT BigTransfer Classifier Keras/1. BiT BigTransfer Classifier Keras.mp458.62MB
191. 46. Depth Estimation/1. Depth Estimation Project.mp476.77MB
192. 47. Image Similarity using Metric Learning/1. Image Similarity using Metric Learning.mp456.01MB
193. 48. Image Captioning with Keras/1. Image Captioning with Keras.mp496.09MB
194. 49. Video Classification usign CNN+RNN/1. Video Classification usign CNN+RNN.mp456.76MB
195. 5. OpenCV - Haar Cascade Classifiers/1. Face and Eye Detection with Haar Cascade Classifiers.mp4110.22MB
196. 5. OpenCV - Haar Cascade Classifiers/2. Vehicle and Pedestrian Detection.mp486.09MB
197. 50. Video Classification with Transformers/1. Video Classification with Transformers.mp449.24MB
198. 51. Point Cloud Classification PointNet/1. Point Cloud Classification PointNet.mp459.53MB
199. 52. Point Cloud Segmentation Using PointNet/1. Point Cloud Segmentation Using PointNet.mp492.05MB
200. 53. Medical Project - X-Ray Pneumonia Prediction/1. X-Ray Pneumonia Prediction.mp461.41MB
201. 54. Medical Project - 3D CT Scan Classification/1. 3D CT Scan Classification.mp460.32MB
202. 55. Low Light Image Enhancement MIRNet/1. Low Light Image Enhancement MIRNet.mp491.55MB
203. 56. Deploy your CV App using Flask RestFUL API & Web App/1. Flask RestFUL API.mp454.94MB
204. 56. Deploy your CV App using Flask RestFUL API & Web App/2. Flask Web App.mp437.26MB
205. 57. OCR Captcha Cracker/1. OCR Captcha Cracker.mp446.38MB
206. 6. OpenCV - Image Analysis and Transformation/1. Perspective Transforms.mp464.42MB
207. 6. OpenCV - Image Analysis and Transformation/2. Histograms and K-Means Clustering for Dominant Colors.mp479.89MB
208. 6. OpenCV - Image Analysis and Transformation/3. Comparing Images MSE and Structual Similarity.mp442.1MB
209. 6. OpenCV - Image Analysis and Transformation/4. Filtering on Colour.mp440.87MB
210. 6. OpenCV - Image Analysis and Transformation/5. Watershed Algorithm Marker-Dased Image Segmentation.mp443.33MB
211. 6. OpenCV - Image Analysis and Transformation/6. Background and Foreground Subtraction.mp466.11MB
212. 7. OpenCV - Motion and Object Tracking/1. Motion Tracking with Mean Shift and CAMSHIFT.mp471.05MB
213. 7. OpenCV - Motion and Object Tracking/2. Object Tracking with Optical Flow.mp495.15MB
214. 7. OpenCV - Motion and Object Tracking/3. Simple Object Tracking by Color.mp448.62MB
215. 8. OpenCV - Facial Landmark Detection & Face Swaps/1. Facial Landmark Detection with Dlib.mp437.54MB
216. 8. OpenCV - Facial Landmark Detection & Face Swaps/2. Face Swapping with Dlib.mp449.4MB
217. 9. OpenCV Projects/1. Tilt Shift Effects.mp459.03MB
218. 9. OpenCV Projects/10. Add and Remove Noise and Fix Contrast with Histogram Equalization.mp488.97MB
219. 9. OpenCV Projects/11. Detect Blur in Images.mp439.57MB
220. 9. OpenCV Projects/12. Facial Recognition.mp484.31MB
221. 9. OpenCV Projects/2. GrabCut Algorithm for Background Removal.mp445.65MB
222. 9. OpenCV Projects/3. OCR with PyTesseract and EasyOCR (Text Detection).mp4119.5MB
223. 9. OpenCV Projects/4. Barcode, QR Generation and Reading.mp468.68MB
224. 9. OpenCV Projects/5. YOLOv3 in OpenCV.mp479.52MB
225. 9. OpenCV Projects/6. Neural Style Transfer with OpenCV.mp4143.03MB
226. 9. OpenCV Projects/7. SSDs in OpenCV.mp451.82MB
227. 9. OpenCV Projects/8. Colorize Black and White Photos using a Caffe Model in OpenCV.mp480.98MB
228. 9. OpenCV Projects/9. Inpainting to Restore Damaged Photos.mp428.48MB