YOLO V/s Embeddings: A comparison between two object detection models
YOLO-Based Detection Model
Type: Object detection
Method: YOLO is a single-stage object detection model that divides the image into a grid and predicts
bounding boxes, class labels, and confidence scores in a single pass.
Output: Bounding boxes with class labels and confidence scores.
Use Case: Ideal for real-time applications like autonomous vehicles, surveillance, and robotics.
Example Models: YOLOv3, YOLOv4, YOLOv5, YOLOv8
Architecture
YOLO processes an image in a single forward pass of a CNN. The image is divided into a grid of cells
(e.g., 13×13 for YOLOv3 at 416×416 resolution). Each cell predicts bounding boxes, class labels, and
confidence scores. Uses anchor boxes to handle different object sizes. Outputs a tensor of shape [S,
S, B*(5+C)] where:
S = grid size (e.g., 13×13)
B = number of anchor boxes per grid cell
C = number of object classes
5 = (x, y, w, h, confidence)
Training Process
Loss Function: Combination of localization loss (bounding box regression), confidence loss, and
classification loss.
Labels: Requires annotated datasets with labeled bounding boxes (e.g., COCO, Pascal VOC).
Optimization: Typically uses SGD or Adam with a backbone CNN like CSPDarknet (in YOLOv4/v5).
Inference Process
Input image is resized (e.g., 416×416). A single forward pass through the model. Non-Maximum
Suppression (NMS) filters overlapping bounding boxes. Outputs detected objects with bounding
boxes.
Strengths
Fast inference due to a single forward pass. Works well for real-time applications (e.g., autonomous
driving, security cameras). Good performance on standard object detection datasets.
Weaknesses
Struggles with overlapping objects (compared to two-stage models like Faster R-CNN). Fixed number
of anchor boxes may not generalize well to all object sizes. Needs retraining for new classes.
Embeddings-Based Detection Model
Type: Feature-based detection
Method: Instead of directly predicting bounding boxes, embeddings-based models generate a high-dimensional representation (embedding vector) for objects or regions in an image. These embeddings are then compared against stored embeddings to identify objects.
Output: A similarity score (e.g., cosine similarity) that determines if an object matches a known
category.
Use Case: Often used for tasks like face recognition (e.g., FaceNet, ArcFace), anomaly detection, object
re-identification, and retrieval-based detection where object categories might not be predefined.
Example Models: FaceNet, DeepSORT (for object tracking), CLIP (image-text matching)
Architecture
Uses a deep feature extraction model (e.g., ResNet, EfficientNet, Vision Transformers). Instead of
directly predicting bounding boxes, it generates a high-dimensional feature vector (embedding) for each object or image. The embeddings are stored in a vector database or compared using similarity metrics.
Training Process
Uses contrastive learning or metric learning. Common loss functions:
Triplet Loss: Forces similar objects to be closer and different objects to be farther in embedding
space.
Cosine Similarity Loss: Maximizes similarity between identical objects.
Center Loss: Ensures class centers are well-separated.
Training datasets can be either:
Labeled (e.g., with identity labels for face recognition).
Self-supervised (e.g., CLIP uses image-text pairs).
Inference Process
Extract embeddings from a new image using a CNN or transformer. Compare embeddings with
stored vectors using cosine similarity or Euclidean distance. If similarity is above a threshold, the
object is recognized.
Strengths
Scalable: New objects can be added without retraining.
Better for recognition tasks: Works well for face recognition, product matching, anomaly detection.
Works without predefined classes (zero-shot learning).
Weaknesses
Requires a reference database of embeddings. Not good for real-time object detection (doesn’t
predict bounding boxes directly). Can struggle with hard negatives (objects that look similar but are
different).
Struggles with overlapping objects (compared to two-stage models like Faster R-CNN). Fixed number
of anchor boxes may not generalize well to all object sizes. Needs retraining for new classes.
Embeddings-Based Detection Model
Type: Feature-based detection
Method: Instead of directly predicting bounding boxes, embeddings-based models generate a high-
dimensional representation (embedding vector) for objects or regions in an image. These embeddings
are then compared against stored embeddings to identify objects.
Output: A similarity score (e.g., cosine similarity) that determines if an object matches a known
category.
Use Case: Often used for tasks like face recognition (e.g., FaceNet, ArcFace), anomaly detection, object
re-identification, and retrieval-based detection where object categories might not be predefined.
Example Models: FaceNet, DeepSORT (for object tracking), CLIP (image-text matching)
Architecture
Uses a deep feature extraction model (e.g., ResNet, EfficientNet, Vision Transformers). Instead of
directly predicting bounding boxes, it generates a high-dimensional feature vector (embedding) for each object or image. The embeddings are stored in a vector database or compared using similarity
metrics.
Training Process
Uses contrastive learning or metric learning. Common loss functions:
Triplet Loss: Forces similar objects to be closer and different objects to be farther in embedding
space.
Cosine Similarity Loss: Maximizes similarity between identical objects.
Center Loss: Ensures class centers are well-separated.
Training datasets can be either:
Labeled (e.g., with identity labels for face recognition).
Self-supervised (e.g., CLIP uses image-text pairs).
Inference Process
Extract embeddings from a new image using a CNN or transformer. Compare embeddings with
stored vectors using cosine similarity or Euclidean distance. If similarity is above a threshold, the
object is recognized.
Strengths
Scalable: New objects can be added without retraining.
Better for recognition tasks: Works well for face recognition, product matching, anomaly detection.
Works without predefined classes (zero-shot learning).
Weaknesses
Requires a reference database of embeddings. Not good for real-time object detection (doesn’t
predict bounding boxes directly). Can struggle with hard negatives (objects that look similar but are
different).
