As the needs of business and the evolution of algorithms, AI image detection and recognition is becoming more and more common. This essay briefly introduces and compares two commonly used algorithm libraries: YOLO and SSD.

**1. About YOLO **(You only Look Once):

YOLO detection is a straightforward regression dilemma which takes an input image and learns the class possibilities with bounding box coordinates. **YOLO divides every image into a grid of S x S and every grid predicts N bounding boxes and confidence.** The confidence reflects the precision of the bounding box and whether the bounding box in point of fact contains an object in spite of the defined class. YOLO even forecasts the classification score for every box for each class. You can merge both the classes to work out the chance of every class being in attendance in a predicted box.

So, total SxSxN boxes are forecasted. On the other hand, most of these boxes have lower confidence scores and if we set a doorstep

**The GitHub Source Code: https://github.com/pjreddie/darknet**

**2. About SSD **(Single Shot MultiBox Detector in TensorFlow)

**SSD attains a better balance between swiftness and precision. SSD runs a convolutional network on input image only one time and computes a feature map.** Now, we run a small 3×3 sized convolutional kernel on this feature map to foresee the bounding boxes and categorization probability.

SSD also uses anchor boxes at a variety of aspect ratio comparable to Faster-

**The GitHub Source Code: https://github.com/balancap/SSD-Tensorflow**

**3. YOLO vs. SSD**

**SSD is a healthier recommendation**. However, if exactness is not too much of disquiet but **you want to go super quick, YOLO will be the best way** to move forward. First of all, a visual thoughtfulness of swiftness vs precision trade-off would differentiate them well.

SSD is a better option as we are able to run it on a video and the exactness trade-off is very modest. While dealing with large sizes, SSD seems to perform well, but when we look at the accurateness numbers when the object size is small, the performance dips a bit.