Example Curriculum
Introduction
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Understanding Vision Transformers
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Understanding Meta's SAM (Segment Anything Model)
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- Introduction to Prompt Encoders for SAM (4:56)
- SAM AutoPrompt Mode (15:36)
- SAM Manual Click Mode (7:54)
- ViT Embeddings inside SAM (4:54)
- Calculating Attention Score for Vision Transformers in SAM (16:41)
- How SAM is Trained (8:04)
- Calculating Prompt Self Attention for SAM (4:07)
- Prompt Image Cross Attention (7:35)
- Image to Prompt Cross Attention (5:56)
- (Optional) Finishing SAM Example Part 1 (8:49)
- (Optional) Finishing SAM Example Part 2 (7:33)
- Finishing
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Setting up Our AWS Environment
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Setting Up Open Source Models Like Meta's SAM
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- Downloading the SAM Model from Meta (3:23)
- Updating IAM Permissions (1:55)
- Importing Libraries (5:00)
- Understanding how we use Rekognition with SAM (12:28)
- Defining Helper Functions (12:36)
- Clarification Regarding Helper Functions (1:35)
- Rekognition Detection and Filtering (12:26)
- Initialise SAM Model from S3 (10:15)
- Main Processing Function Part 1 (14:33)
- Main Processing Function Part 2 (3:50)
- Running the Main Processing Cell (4:53)
- Implement a New Life System
Visualizing Our Outputs
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- Visualizing Rekognition Detections (7:49)
- Visualize All SAM Masks (9:27)
- Visualizing Match Quality IOU Scores Part 1 (10:22)
- Visualizing Match Quality IOU Scores Part 2 (9:29)
- Visualizing Image Segmentations with Bounding Boxes (11:03)
- Visualizing Masks and Labels Without Bounding Boxes (6:30)
- Visualizing Segementations in Black and White Masks (4:38)
- Exercise: Imposter Syndrome (2:55)
Saving Results to S3
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Testing & Setup
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Where To Go From Here?
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