6. Fast RCNN | cipskills

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Computer Vision Mastery with GenAI – 12 Projects

1. Computer Vision Introduction & Basics

1. Introduction (5:30)
2. Past Present Future Trends (5:38)
3. Applications (4:02)
4. Image Processing basics (3:26)
5. Color Spaces (3:17)

2. Neural Networks-Into the world of Deep Learning

1. Intuition Neural Networks (8:55)
2. Neural Networks (46:30)
3. Approach to deep learning problems (7:13)
4. Lifecycle of model 5 steps (5:14)

3. Tensorflow and Keras

1. Sequential Vs Functional API (14:13)
2. Sequential API code (58:23)
3. Functional API Code (24:40)
4. ML problem Cost Gradient CV (28:04)
5. Activation Functions (24:46)
6. Sequential Vs Functional API (14:13)
7. Tips for Improving Model Performance (24:18)
8. Feed Forward Network Implementation and Keras Callbacks (56:15)
9. Optimizers (36:49)
10. Loss functions (17:53)
11. Performance Metrics (2:11)

4. Image Classification Explained & Project

1. CNN INTRO (39:05)
2. CNN_Implementation (47:32)
3. CNN Exercise -1 Problem (1:01)
4. CNN Exercise -1 Solution (36:21)
5. CNN Exercise -2 Problem (0:32)
6. CNN Exercise -2 Solution (16:33)

5. Keras Preprocessing Layers and Transfer Learning

1. Keras Preprocessing Layers Intro (10:23)
2. Keras Preprocessing Layers Image Augmentation Code (28:19)
3. Keras Preprocessing Layers Exercise-3 (0:35)
4. Keras Preprocessing Layers Solution-3 (9:24)
5. Transfer Learning Introduction (15:29)
6. transfer learning code (56:54)
7. Transfer Learning Exercise 4 -XrayDataset (0:53)
8. Transfer learning Exercise-4 Solution (25:12)

6. RNN LSTM & GRU Introduction

1. LSTM GRU Introduction (17:01)

7. GANS & image captioning Project

1. GANs Introduction (15:12)
2. GAN COMPONENTS (15:56)
3. GANs Training (18:27)
4. GANs Applications Pros _ Cons (9:23)
5. GAN Implementation (47:55)
6. Project Image Captioning Problem-5 (7:15)
6.2 images (2)
7. Project image captioning solution Part- 1 (24:43)
8. Project image captioning solution Part- 2 (39:38)
9. Project Image captioning solution Part- 3 (26:07)

8. Datasets Part 1 (Till this Point)

1.1 cat_dog_sub
2.1 test+&+Validation+for+Xray+Dataset
2.2 train_Normal+for+Xray+Dataset
2.3 train_PNEUMONIA+for+Xray+Dataset

9. Object Detection Everything you should know

1. Object Detection Part start (0:42)
2. Semantic segmentation vs instance segmentation (9:36)
3. Types of Segmentation (11:15)
4. Two step object detection (14:18)
5. RCNN Architecture (7:10)
6. Fast RCNN (8:33)
7. Faster RCNN (5:55)
8. Mask RCNN (4:56)
9. Intro to YOLO (23:58)
10. SSD (12:55)

10. Image Annotation Tools

1. Image Annotation Tools (38:05)

11. YOLO Models for Object Detection, classification, segmentation, Pose Detection

1. YOLOV5 Hardhat & Vest object detection Project-6 (41:31)
1.1 Hardhat - Vest.v1i.yolov5pytorch
2. YOLOv8 intro (6:21)
3. YOLOv8 classification Project-7 (47:43)
3.2 Weather_yolo_img_clsf_data (1)
4. Instance segmentation using YOLOV8-seg Project -8 (18:56)
4.3 Pothole segmentation yolov8 (1)
5. Keypoint detection using YOLOV8-pose (18:33)
5.2 street
5.3 street2
6. YOLO on videos (21:09)
6.2 YOLO Videos for inference

12. Segmentation using FAST-SAM

1. Fast SAM (Segment Anything Model) (19:43)
1.1 dogs

13. Object Tracking & Counting Project

1. YOLOV8 object Tracking (11:15)
2. Object Tracking & Counting Project-9 (47:34)

14. Human Action Recognition Project

1. Human Action Recognition Project 10 (64:16)

15. Image Analysis Models

1. YOLO-WORLD demo (21:58)
2. Moondream1 (14:10)

16. Face Detection & Recognition (AGE GENDER MOOD Analysis)

1. Face Recognition Using DeepFace Project 11 (31:00)

17. Deepfake Generation

1. DeepFake Generation Project 12 (17:41)

18. More learning GENERATIVE AI - Image Generation Via Prompting -Diffusion Models

1. 74 Stable Diffusion (25:47)
2. 75 clip and unet for stable diffusion (15:05)
3. 76 Stable diffusion tools (31:12)
4. 77 Stable diffusion tools (19:00)
5. 78 stable diffusion resources (30:02)
6. 79 STABLE DIFFUSION code (18:14)
7. 80 stable diffusion UI (20:06)
8. 81 stable cascade (16:24)
9. 82 forge setup (11:13)

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