Custom detection dataset in PyTorch - Model Pipeline Trace

Model Pipeline - Custom detection dataset

This pipeline shows how a custom object detection dataset is prepared and used to train a detection model. It starts with loading images and bounding box labels, processes them, trains a model to detect objects, and evaluates its performance.

Data Flow - 5 Stages

1Load raw images and annotations

1000 images x variable size→Read images and bounding box labels from files→1000 images with bounding boxes and labels

Image: 800x600 pixels, Boxes: [[50, 30, 200, 180], [300, 400, 450, 550]], Labels: [1, 3]

↓

2Preprocessing and augmentation

1000 images with boxes and labels→Resize images to 300x300, normalize pixels, adjust boxes accordingly→1000 images 300x300 with adjusted boxes and labels

Image resized to 300x300, box [50,30,200,180] scaled to [18,15,75,90]

↓

3Create PyTorch Dataset and DataLoader

1000 processed images with boxes and labels→Wrap data in Dataset class, batch with DataLoader→Batches of 16 samples, each with images and target dicts

Batch size 16, each sample: image tensor (3x300x300), target: {'boxes': tensor, 'labels': tensor}

↓

4Model training

Batch of 16 images and targets→Forward pass through detection model, compute loss, backpropagation→Updated model weights, loss scalar

Loss: 1.2 at epoch 1, model learns to detect objects

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5Evaluation

Validation images and targets→Model predicts bounding boxes and labels, compute metrics→Metrics like mAP (mean Average Precision)

mAP: 0.65 after 10 epochs

Training Trace - Epoch by Epoch

Loss
1.2 |*       
1.0 | *      
0.8 |  *     
0.6 |   *    
0.4 |    *   
    +---------
     1 2 3 4 5
     Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.20	0.30	Initial training, loss high, accuracy low
2	0.95	0.45	Loss decreased, model improving detection
3	0.75	0.55	Better localization and classification
4	0.60	0.62	Model converging, loss steadily decreasing
5	0.50	0.68	Good detection accuracy, training stable

Prediction Trace - 5 Layers

Layer 1: Input image tensor

Layer 2: Backbone CNN

Layer 3: Region Proposal Network (RPN)

Layer 4: RoI Pooling and Classification Head

Layer 5: Post-processing (NMS)

Model Quiz - 3 Questions

What happens to the bounding boxes during preprocessing?

A They are resized to match the new image size

B They are removed to simplify training

C They are converted to grayscale

D They remain unchanged

Key Insight

This visualization shows how a custom detection dataset is prepared and used to train a detection model. Proper preprocessing of images and bounding boxes is crucial. Training reduces loss and improves accuracy, and post-processing like NMS helps produce clean final predictions.