how to create object detection model from scratch ?

[Akshay-Shingala]

hello, I am having some trouble in creating an object detection model
most tutorials and videos create models for classification to some value prediction or they retrain available pre-train models.
but how can I create an object detection model or segmentation model?
if I use a pre-train model then I can only feed them with my detection dataset how can I improve model accuracy for detecting objects and create a bounding box for it

[ourbillionaireboysclub-p]

Hey Akshay, I totally get where you’re coming from — object detection is a big jump from classification, and most resources either skip the “from scratch” part or rely heavily on pre-trained models. Let me break it down in a friendly, no-nonsense way so you can build something that works, then improve it step by step.

First, just to clarify terms:

· From absolute scratch means you design the entire CNN architecture yourself, initialise weights randomly, and train on your dataset without any pre-trained backbone.
· Using a pre-trained model (like a ResNet backbone in Faster R-CNN) means you’re still building a detection model, just standing on the shoulders of a network that already knows what edges / textures look like. You absolutely can feed it your own detection dataset and improve its accuracy — that’s standard fine-tuning.

I’ll show you both paths, because your real goal is “create a model that draws bounding boxes well on my data.”

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Path 1: Build a simple object detector from absolute scratch (no pre-trained weights)

The most beginner-friendly design is a single-shot grid-based detector (like a minimal YOLO v1). No region proposal network, no complex anchor boxes — just a straight CNN → tensor of predictions.

Core idea

· Split your image into an S x S grid (e.g., 7×7).
· Each grid cell predicts B bounding boxes (say, 2) and C class probabilities.
· Output shape per cell: B * 5 + C (5 = x, y, w, h, confidence).

Example: for 20 classes, S=7, B=2 → output tensor 7x7x(2*5+20) = 7x7x30.

Steps (Python + PyTorch, but concept applies anywhere)

1. Dataset – Annotate images with bounding boxes. Normalise coordinates to [0,1] relative to image size. Split box centre into grid cell indices.
2. Backbone – A tiny custom CNN: several conv layers with stride 2 to shrink to S×S, then a few 1×1 convs to get the right output channels. No fully connected layers.
   Simple example (input 448×448 → 7×7×1024 feature map → 7×7×30 output).
3. Loss function – You need a multi-task loss that combines:
   · Localisation loss (MSE or smooth L1) for x, y, w, h only of the boxes “responsible” for an object.
   · Confidence loss (BCE or MSE) for box confidence = P(object) * IoU(truth, pred).
   · Classification loss (cross-entropy) for grid cells that contain an object.
   · Weight the parts carefully (YOLO uses λ_coord=5, λ_noobj=0.5) to balance.
4. Training loop – Standard: forward pass, loss, backward, optimise. Use data augmentation (flips, colour jitter) from the start.
5. Post-processing – At inference, apply non-max suppression (NMS) to remove duplicate boxes.

This will be messy at first (low mAP), but it’s the purest way to understand every piece. There’s a fantastic bare-bones PyTorch implementation by aladdinpersson on GitHub called “YOLOv1 from scratch” — I highly recommend reading that alongside the original YOLO paper.

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Path 2: Fine-tune a pre-trained detection model (way more practical)

If you want solid accuracy faster, start from a pre-trained detection model and fine-tune on your data. This is not “just classification” – modern torchvision / detectron2 / MMDetection let you swap in your own dataset and train the whole head + optionally the backbone.

For example, with torchvision’s Faster R-CNN:

import torchvision
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator

# Load a backbone pre-trained on ImageNet
backbone = torchvision.models.mobilenet_v2(weights="DEFAULT").features
backbone.out_channels = 1280

# Define anchor generator
anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
                                   aspect_ratios=((0.5, 1.0, 2.0),))

# Create model with custom number of classes (e.g., 3 classes + background)
model = FasterRCNN(backbone,
                   num_classes=4,  # N classes + 1 background
                   rpn_anchor_generator=anchor_generator)

Then you train with your custom dataset following the official torchvision detection finetuning tutorial. The model will learn to produce bounding boxes for your objects even though the backbone started with ImageNet features.

How to improve accuracy when fine-tuning

· Freeze the first few layers of the backbone, gradually unfreeze as you lower the learning rate.
· Tweak anchor sizes to match your objects. If you have small objects, add smaller anchor boxes.
· Heavy augmentation – mosaic, random crop, colour jitter, shear, etc. Use a library like albumentations.
· Train longer – detection models usually need many epochs (50–300) depending on dataset size.
· Use a strong backbone – swapping MobileNet for ResNet50 or EfficientNet will give a big boost.
· Evaluate with mAP not just loss. TensorBoard the bounding box losses separately to diagnose.
· Hard negative mining – sometimes adding false positives back into training helps.

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Segmentation bonus

If segmentation is on your radar, the “from scratch” equivalent would be an FCN (Fully Convolutional Network) where the output is a pixel-wise class map. It’s conceptually similar to the grid approach, but now each pixel predicts a class. For fine-tuning, pre-trained DeepLabV3 or Mask R-CNN models work great.

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What I’d suggest for you right now

If you’re still getting comfortable, start with Path 2 (fine-tuning Faster R-CNN on your dataset) because:

· It converges faster,
· You can directly see bounding boxes,
· You learn about anchors, loss balancing, and NMS without writing everything from scratch.

Once you’re confident, go back and implement a YOLO-style model from scratch — that will cement your understanding beautifully.

If you want, share a bit about your dataset (number of images, object sizes, classes) and I can give you more specific hyperparameters to try. You’ve got this!