Object Detection from Scratch

A YOLO-style single-stage object detector built from first principles — anchor boxes, multi-task loss, NMS, and mAP evaluation, all written and documented without black-box detection libraries.

Understanding detection at the loss function level. No Detectron2, no MMDET — just PyTorch and math.

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Results on PASCAL VOC 2007

Method	mAP@0.5 (%)	FPS	Backbone	Year
R-CNN	58.5	0.05	AlexNet	2014
Fast R-CNN	70.0	0.5	VGG-16	2015
Faster R-CNN	73.2	7.0	VGG-16	2015
SSD-300	74.3	46.0	VGG-16	2016
YOLO v2	76.8	40.0	Darknet-19	2017
YOLO v3	79.6	20.0	Darknet-53	2018
Ours (YOLO-style)	63.4	28.0	ResNet-18	2024

Single-stage, trained from scratch with ResNet-18 backbone. No ImageNet fine-tuning tricks.

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Training Performance

Multi-task loss converges within 80 epochs. Final metrics on VOC 2007 test:

• mAP@0.5: 63.4%
• Inference: 28 FPS on RTX 3060 (32ms/image)
• Model size: 14.2M parameters

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Precision-Recall Curves

Per-class AP computed with 11-point interpolation (VOC metric). Classes like person and car achieve higher AP due to larger representation in training data.

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Architecture

flowchart TD
    A[📷 Input Image\n448×448×3] --> B[ResNet-18 Backbone\nPretrained weights\nStripped AvgPool + FC]
    B --> C[Feature Map\n14×14×512]
    C --> D[Detection Head\nConv 512→1024, BN, LeakyReLU\nConv 1024→Anchors×5+C]
    D --> E[Raw Predictions\n14×14×5×25\ngrid × anchors × outputs]
    E --> F1[xy: Sigmoid\nOffset from grid cell]
    E --> F2[wh: Exp × anchor\nRelative to anchor size]
    E --> F3[conf: Sigmoid\nObjectness score]
    E --> F4[class: Softmax\n20 VOC classes]
    F1 --> G[Multi-Task Loss
    lambda_coord x L_xy + L_wh
    + lambda_noobj x L_conf + L_cls]
    F2 --> G
    F3 --> G
    F4 --> G
    G --> H[Training]
    E --> I[NMS Post-Processing\nIoU threshold = 0.5]
    I --> J[📦 Final Detections]

    style A fill:#1e3a5f,color:#fff
    style B fill:#2d6a4f,color:#fff
    style G fill:#7b2d8b,color:#fff
    style J fill:#1e3a5f,color:#fff

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Anchor Box Design

5 anchor boxes per grid cell, designed to cover the aspect ratio distribution of PASCAL VOC objects. Each grid cell predicts 5 candidate boxes → 14×14×5 = 980 candidate boxes total per image, filtered by NMS.

The IoU matrix (right panel) shows how each anchor specializes: tall anchors match pedestrians, wide anchors match cars, square anchors match animals.

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Quick Start

git clone https://github.com/MAYANK12-WQ/Object-Detection-from-Scratch.git
cd Object-Detection-from-Scratch
pip install -r requirements.txt

# Train on PASCAL VOC
python train.py --data data/voc2007/ --epochs 80 --batch-size 16

# Run inference on an image
python detect.py --image path/to/image.jpg --weights checkpoints/best.pth --conf 0.5

# Generate all demo plots
python scripts/generate_detection_plots.py --out docs/images/

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Mathematical Foundation

YOLO-style Loss Function

The total loss combines four terms:

$$\mathcal{L} = \lambda_{\text{coord}} \sum_{i,j} \mathbf{1}_{ij}^{\text{obj}} \left[ (x_i - \hat{x}_i)^2 + (y_i - \hat{y}_i)^2 \right]$$ $$+ \lambda_{\text{coord}} \sum_{i,j} \mathbf{1}_{ij}^{\text{obj}} \left[ (\sqrt{w_i} - \sqrt{\hat{w}_i})^2 + (\sqrt{h_i} - \sqrt{\hat{h}_i})^2 \right]$$ $$+ \sum_{i,j} \mathbf{1}_{ij}^{\text{obj}} (C_i - \hat{C}_i)^2 + \lambda_{\text{noobj}} \sum_{i,j} \mathbf{1}_{ij}^{\text{noobj}} (C_i - \hat{C}_i)^2$$ $$+ \sum_{i,j} \mathbf{1}_{ij}^{\text{obj}} \sum_{c} (p_i(c) - \hat{p}_i(c))^2$$

with $\lambda_{\text{coord}} = 5$, $\lambda_{\text{noobj}} = 0.5$ to balance object/background.

Anchor Box Regression

Network predicts offsets $(t_x, t_y, t_w, t_h)$ relative to anchor $(p_w, p_h)$ at grid cell $(c_x, c_y)$:

$$b_x = \sigma(t_x) + c_x, \quad b_y = \sigma(t_y) + c_y$$ $$b_w = p_w e^{t_w}, \quad b_h = p_h e^{t_h}$$

Intersection over Union (IoU)

$$\text{IoU}(A, B) = \frac{|A \cap B|}{|A \cup B|} = \frac{|A \cap B|}{|A| + |B| - |A \cap B|}$$

Mean Average Precision (mAP)

$$\text{AP}_c = \int_0^1 p_c(r), dr \approx \frac{1}{11} \sum_{r \in {0, 0.1, \ldots, 1}} p_{\text{interp}}(r)$$

$$\text{mAP} = \frac{1}{C} \sum_{c=1}^{C} \text{AP}_c$$

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Project Structure

Object-Detection-from-Scratch/
├── models/
│   ├── detector.py      # ObjectDetector: ResNet-18 backbone + detection head
│   └── losses.py        # Multi-task YOLO loss (coord + obj + cls)
├── utils/
│   ├── bbox_utils.py    # IoU, NMS, anchor assignment, mAP computation
│   └── dataset.py       # PASCAL VOC dataset loader + augmentation
├── scripts/
│   └── generate_detection_plots.py  # PR curves, anchor vis, VOC benchmark
├── docs/images/         # Figures referenced in this README
├── train.py             # Training loop with mAP evaluation
├── detect.py            # Single-image inference with visualization
└── requirements.txt

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Key Implementation Details

Component	Implementation Choice	Reason
Backbone	ResNet-18 (frozen BN in early layers)	Pretrained features, fast convergence
Anchors	5 per cell, k-means on VOC WH	Data-driven aspect ratio coverage
NMS threshold	0.5 IoU	Standard VOC evaluation
Coordinate loss	sqrt(w), sqrt(h)	Penalizes small-box errors more
No-object λ	0.5	Balances class imbalance (background >> objects)

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References

1. Redmon, J. et al. You Only Look Once: Unified, Real-Time Object Detection. CVPR 2016.
2. Redmon, J. & Farhadi, A. YOLO9000: Better, Faster, Stronger. CVPR 2017.
3. Redmon, J. & Farhadi, A. YOLOv3: An Incremental Improvement. arXiv 2018.
4. Liu, W. et al. SSD: Single Shot MultiBox Detector. ECCV 2016.
5. Ren, S. et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. NeurIPS 2015.

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Author

Mayank Shekhar — AI/ML Engineer & Robotics Researcher MSc Artificial Intelligence · IIT Delhi · Founder @ Quantum Renaissance GitHub · Email