Kids_and_Adults_detection

This repository contains the code and resources for developing a deep learning model to detect and classify individuals as kids or adults in images or videos.

Features:

• Object Detection: Detects people in images with bounding boxes.
• Classification: Classifies each detected individual as either a kid or an adult.
• Transfer Learning: Utilizes pre-trained object detection models for faster and more accurate development.

Repository Contents:

1. Data Preparation: Scripts and notebooks for preparing and annotating datasets.
2. Model Training:
   Code for training the detection and classification models.
3. Evaluation: Tools for assessing model performance using metrics like precision, recall, and confusion matrix.

Here are some of the best object detection transfer learning models widely used in practice due to their accuracy, efficiency, and flexibility:

1. Faster R-CNN Overview: A two-stage object detection model where the first stage generates region proposals, and the second stage classifies the proposals and refines bounding boxes. Best For: High accuracy in detecting objects in complex and cluttered scenes. Pre-trained Weights: Available on COCO or Pascal VOC datasets. Transfer Learning Use Case: Fine-tune the network for detecting specific objects in smaller datasets.

2. YOLO (You Only Look Once) Versions: YOLOv3, YOLOv4, YOLOv5, and YOLOv8 (latest with advancements in speed and accuracy). Overview: A single-stage detector known for real-time detection speeds and decent accuracy. Best For: Applications requiring real-time detection with good precision (e.g., surveillance, robotics). Pre-trained Weights: Provided for COCO and other datasets. Transfer Learning Use Case: Quick customization for specific object detection tasks.

3. RetinaNet Overview: A single-stage detector that uses Focal Loss to address class imbalance in object detection. Best For: Scenarios with small objects or datasets where some classes dominate others. Pre-trained Weights: Available for COCO dataset. Transfer Learning Use Case: Fine-tune for datasets with varied object sizes.

4. EfficientDet Overview: A family of models that balance speed and accuracy using compound scaling. Best For: Optimizing performance on edge devices or when computational efficiency is critical. Pre-trained Weights: Available for COCO and Open Images datasets. Transfer Learning Use Case: Great for mobile or embedded systems with custom datasets.

5. DETR (DEtection TRansformer) Overview: A transformer-based model that simplifies object detection by removing the need for anchor boxes and post-processing. Best For: Cutting-edge applications requiring state-of-the-art performance in dense scenes. Pre-trained Weights: Available on COCO. Transfer Learning Use Case: Customize the model for complex detection scenarios with fewer annotations.

6. SSD (Single Shot MultiBox Detector) Overview: A single-stage detector that strikes a balance between speed and accuracy. Best For: Medium-sized objects and applications where real-time performance is necessary. Pre-trained Weights: Available for COCO. Transfer Learning Use Case: Fine-tune for lightweight detection tasks.

7. Mask R-CNN Overview: An extension of Faster R-CNN that performs object detection and instance segmentation. Best For: Applications requiring both object detection and pixel-level segmentation. Pre-trained Weights: Available on COCO. Transfer Learning Use Case: Fine-tune for detecting and segmenting domain-specific objects.

• This repository only includes fast R-CNN, yolo, DETR (Detection transformer) and ssd

Summary of Selection Based on Needs:

1. Real-Time Speed: YOLOv5, YOLOv8, SSD.
2. High Accuracy: Faster R-CNN, RetinaNet.
3. Dense or Complex Scenes: DETR, Mask R-CNN.
4. Mobile/Efficient Deployment: EfficientDet, SSD.

Dataset :

This dataset has 500 images with 2 classes and annotations labels data.

- Categories: Two classes – Children's and Adults.
- Diversity: Includes a wide range of scenarios for robust model training.
- Annotations: Provides bounding boxes and class labels for all detected individuals.

• Kaggle link
• Roboflow link

1. Faster R-CNN - Notebook

This notebook demonstrates the implementation of transfer learning on a Faster R-CNN model using Facebook's Detectron2 library. It includes steps to fine-tune a pre-trained model for custom object detection tasks, with detailed examples of data preparation, training, and evaluation

• Detectron2
• Detectron2 github

2. Yolo - Notebook

This Notebook provides an implementation of transfer learning for object detection using the YOLO (You Only Look Once) model. It covers the process of fine-tuning a pre-trained YOLO model on custom datasets, including data preparation, annotation, model configuration, training, and evaluation. Suitable for efficient and real-time object detection tasks.

• yolo
• yolo 8 finetune

3. DETR (DEtection TRansformer) - Notebook

This notrbook demonstrates how to implement transfer learning using DETR (DEtection TRansformers) for object detection tasks. It includes steps for fine-tuning a pre-trained DETR model on custom datasets, covering data preparation, model configuration, training, and evaluation. DETR combines transformers with CNNs for end-to-end object detection, enabling improved performance on complex detection tasks.

• Huggingface Detr
• Finetune detr