Detecting Defects in Steel Sheets with Computer Vision
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Section 1: Introduction
- Defect Detection in Manufacturing
- Data Overview
Section 2: U-Net Model Architecture
- Encoder: Going Down the U-Net
- Decoder: Moving Up the U-Net
Section 3: Evaluation Metrics
- What is Dice
- Cross Entropy vs. Dice
- Setup Environment and Import Data
Section 4: Setup, Helper Functions and Explore Data (Code Demo)
- Helper Functions for Encoding and Decoding Masks with OpenCV
- Helper Function for Dice Loss Calculation
- Set Device Configuration
- Exploring Data
Section 5: Train Model with BCE Logits Loss (Code Demo)
- Define Hyperparameters
- Initialize Dataloader
- Initialize UNet Model
- Define Loss and Optimizer
- Train Model
- Evaluate Model
- Check performance when training with entire data
Section 6: Train Model with BCE Logits Loss and Learning Rate Scheduler (Code Demo)
- Train Model with BCE Logits Loss and Learning Rate Scheduler (Code
Demo)
Section 7: Train Model with Dice Loss and Learning Rate Scheduler (Code Demo)
- Train Model with Dice Loss and Learning Rate Scheduler (Code Demo)
Section 8: Visualize with TensorBoard (Code Demo)
- Initial Setup
- Initialize Writer
- Show Images in TensorBoard
- Show Model Architecture in TensorBoard