Speaker 1: Imagine your smartphone unlocking just by glancing at your face,
Speaker 1: or maybe a robotic arm and a factory meticulously inspecting
Speaker 1: products catching defects you or I might totally miss.
Speaker 2: Yeah, it really feels like artificial intelligence has somehow gained
Speaker 2: the gift of sight.
Speaker 1: It does, but how do computers actually do that? How
Speaker 1: do they get this incredible ability to see and interpret
Speaker 1: the visual world.
Speaker 2: Well, it's quite a journey really, from just raw data
Speaker 2: to pretty profound insights, and it's all built on this
Speaker 2: fascinating intersection of computer vision and artificial neural network.
Speaker 1: It sounds like sci fi, but it's happening now.
Speaker 2: It absolutely is a rapidly evolving reality.
Speaker 1: And that's exactly what we're diving into today. We're drawing
Speaker 1: from a really comprehensive guide on building these kinds of
Speaker 1: powerful AI systems. Our mission basically is to pull back
Speaker 1: the curtain a bit, demystify how computers perceive process and
Speaker 1: we'll ultimately make sense of images and video.
Speaker 2: Well, trace that whole path from the simplest element, the pixel,
Speaker 2: all the way up to these super complex AI architectures.
Speaker 1: Exactly things like object tracking, face recognition. It's a surprising journey.
Speaker 2: And you'll hopefully get a clear understanding of the mechanisms,
Speaker 2: the innovations behind it all. How these intelligent eyes actually work.
Speaker 1: Okay, so let's kick things off right at the beginning.
Speaker 1: How does a computer even see an image? We know
Speaker 1: they're digital pixels and all that, but how does it
Speaker 1: interpret them?
Speaker 2: Right? So, at its core, a digital image is just
Speaker 2: a grid, a grid of pixels. For something simple like
Speaker 2: a grayscale image, each pixel is just one number, usually
Speaker 2: between zero and two to fifty five.
Speaker 1: Zero for black, two hundred and fifty five for white.
Speaker 2: Exactly zero is black, two fifty five is white, and
Speaker 2: everything in between is just a shade of gray. It's
Speaker 2: literally just a matrix of numbers.
Speaker 1: Okay, simple enough for black and white. But what about color?
Speaker 1: How do you get all the richness of color from numbers?
Speaker 2: Ah, that's where models like RGB come in. Red, green, blue.
Speaker 2: Instead of one number per pixel, you get three, a
Speaker 2: little bundle, a tupple.
Speaker 1: Bit basically, so each pixel has a red value, a
Speaker 1: green value, and a.
Speaker 2: Blue value, precisely each one, also ranging from zero to
Speaker 2: two hundred and fifty five. So like zero zero, no color,
Speaker 2: that's black. Okay, two fifty five zero zero zero b
Speaker 2: pure red, pure red. So what do you think there are?
Speaker 2: Zero two fifty five would be or two fifty five
Speaker 2: fifty five to fifty five.
Speaker 1: Okay, following that logic, zero zero two fifty five must
Speaker 1: be pure blue, and if all three are maxed out
Speaker 1: at two fifty five, that's gotta be white, right, combining all.
Speaker 2: The light you got to pure white. It's actually quite elegant,
Speaker 2: isn't it. How these simple number combinations create this huge
Speaker 2: range of colors.
Speaker 1: It really is.
Speaker 2: And you know, once the computer can represent an image
Speaker 2: as these numbers, then it gets the power to manipulate
Speaker 2: them in loads of ways.
Speaker 1: Right. This is where we get into the sort of
Speaker 1: digital darkroom idea. Basic stuff like resizing, moving things.
Speaker 2: Around yep, resizing, translation, rotation, flipping, cropping, standard geometric things,
Speaker 2: and you mentioned resizing it's shoes. Some methods are better
Speaker 2: than others, like by cubic interpolation usually give you a
Speaker 2: smoother and nicer looking result compared to simpler ones like bilinear.
Speaker 1: Okay, that makes sense. But beyond just moving blocks of pixels,
Speaker 1: what about changing the pixels themselves?
Speaker 2: You mentioned arithmetic, image arithmetic and bitwise operations. These are
Speaker 2: more pixel level manipulations. Think about adding a number to
Speaker 2: every pixel value or subtracting.
Speaker 1: One, so like brightening or darkening the whole image exactly.
Speaker 2: And if a calculation pushes a pixel value above two
Speaker 2: fifty five or blow zero, it usually just gets clipped
Speaker 2: stuck at the max or min value. Stops things getting.
Speaker 1: Weird, prevents crazy colors appearing out of nowhere, right.
Speaker 2: And then you have bitwise operations Andy or not, TXO
Speaker 2: or not. These are really powerful for things like masking.
Speaker 1: Masking like cutting out of shape.
Speaker 2: Kind of imagine you have a black and white image
Speaker 2: like a stencil. You can use a bitwise Andy operation
Speaker 2: between that mask and your main image. It essentially keeps
Speaker 2: only the parts of the main image where the mask
Speaker 2: is white. It's like a digital cutout, very precise control.
Speaker 1: Ah I see, so you can isolate specific parts of
Speaker 1: an image very cleanly.
Speaker 2: Yep. And we also use other operations for cleaning things
Speaker 2: up or highlighting details, like.
Speaker 1: Blurring to reduce noise or smooth things out exactly.
Speaker 2: Techniques like gossim blur medium blur are common for smoothing.
Speaker 2: And then on the flip side, if you want to
Speaker 2: find edges the outlines of objects.
Speaker 1: You'd use edge detection filters like so.
Speaker 2: Soble sure, Yeah, these filters are designed to spot sharp
Speaker 2: changes in pixel intensity, which usually happen at edges. It
Speaker 2: helps the computer see the skeleton of objects.
Speaker 1: And what about just simplifying things down to black and white?
Speaker 2: That's binarization. Things like adaptive thresholding or Otsu's method are
Speaker 2: clever ways to turn a grayscale image into just black
Speaker 2: and white pixels, which can be really useful for certain tasks.
Speaker 1: Okay, so we've got the basics breaking images into pixels
Speaker 1: manipulating them, but just seeing pixels isn't understanding right. The
Speaker 1: computer needs to extract actual meaning. How does it learn
Speaker 1: to pick out the important stuff, the meaningful features.
Speaker 2: That is absolut the core challenge, and it's addressed by
Speaker 2: the computer vision pipeline. It's a sequence. First, you ingest
Speaker 2: the image, get the data in, then you process it,
Speaker 2: maybe clean it up like we just discussed. Then comes
Speaker 2: the crucial step, feature extraction.
Speaker 1: Feature extraction, that's the key.
Speaker 2: That's where the magic starts. Really, it's how the computer
Speaker 2: moves beyond just raw pixel values to identify characteristics that
Speaker 2: actually mean something like the curve of an edge, a
Speaker 2: specific texture, the corner of an object.
Speaker 1: So we're looking for features that are discriminating things that
Speaker 1: help tell one object from another exactly.
Speaker 2: They need to be discriminating, identifiable across different images of
Speaker 2: the same object. And ideally you need lots of examples
Speaker 2: to establish those patterns reliably.
Speaker 1: And how does the computer store these features once it
Speaker 1: finds them.
Speaker 2: Typically, these extracted features are represented as a feature vector.
Speaker 2: It sounds fancy, but it's basically just a list of numbers,
Speaker 2: a one dimensional array.
Speaker 1: Okay, a list of numbers representing the important bits of
Speaker 1: the image.
Speaker 2: Yeah, And here's the sort of a high moment. For
Speaker 2: a simple grayscale image, you could just string all the
Speaker 2: pixel values together into one massive vector that is a
Speaker 2: feature vector technically.
Speaker 1: Wow, okay, so you're boiling down the whole image into
Speaker 1: this single numerical signature. That makes it easier for a
Speaker 1: machine learning algorithm to chew on I guess precisely.
Speaker 2: And what's really powerful about modern deep learning, especially convolutional
Speaker 2: neural networks or CNNs. Yes, they can actually learn to
Speaker 2: extract these features automatically. The network figures out the best
Speaker 2: features itself during training.
Speaker 1: That's a huge advantage. Less manual work potentially better features.
Speaker 2: Definitely, But even before deep learning or alongside it, there
Speaker 2: are some really clever advanced feature extraction techniques like.
Speaker 1: What you mentioned histograms GLCM. Hog's right.
Speaker 2: Histograms are a good starting point just counting how many
Speaker 2: pixels have certain intensity values, but you can do more
Speaker 2: like histogram equalization, which spreads out the intensities to improve contrast, makes.
Speaker 1: Details pop okay, and gl.
Speaker 2: GLCM stands for a gray level coocurrence matrix. It's fantastic
Speaker 2: for analyzing texture. It looks at how often pairs of
Speaker 2: pixel values appear together in certain spatial relationship.
Speaker 1: It tells you about the texture, like if it's smooth
Speaker 1: or rough or patterned exactly.
Speaker 2: It gives you statistics like contrasts, correlation, energy, homogeneity, all
Speaker 2: describing the texture.
Speaker 1: Cool and Hog's histograms of oriented gradients sounds complex.
Speaker 2: The idea is pretty neat actually, AG's focus on object
Speaker 2: shape and appearance. They look at how image brightness changes
Speaker 2: the gradients and in which directions these changes point.
Speaker 1: So it's capturing edge information.
Speaker 2: Sort of yeah, edge directions. It breaks the image into
Speaker 2: small cells, calculates histograms of these gradient directions within each cell,
Speaker 2: and then groups cells into blocks to normalize them. Things
Speaker 2: like the number of orientations pixels persol cells per block
Speaker 2: are parameters you set. It's good at describing shape even
Speaker 2: if lighting changes.
Speaker 1: Robust okay, and LBP Local Binary Patterns.
Speaker 2: LP is great for finer texture details. It works by
Speaker 2: comparing each pixel to its neighbors. If a neighbor is brighter,
Speaker 2: you write down a one, if darker, a zero. This
Speaker 2: creates a binary number for each pixel's neighborhood.
Speaker 1: A unique code for the local texture.
Speaker 2: Pretty much. Yeah, and there are enhanced versions that can
Speaker 2: look at different sized neighborhoods or are rotation invariant, meaning
Speaker 2: the texture feature doesn't change if the image is rotated.
Speaker 1: So many ways to describe an image numerically. But having
Speaker 1: all these features isn't the end goal. The computer has
Speaker 1: to learn from them, right. How do we prep for that?
Speaker 2: Right? So, you might have extracted tons of features, maybe
Speaker 2: too many. That's where feature selection comes in. You use methods,
Speaker 2: filter wrapper, embedded techniques to pick out the most impactful
Speaker 2: features for your specific task. Get rid of the.
Speaker 1: Noise, focus on what matters exactly.
Speaker 2: Then you move to model training. You take your selected
Speaker 2: feature set your training data and feed them to a
Speaker 2: machine learning algorithm. The algorithm learns the patterns in those
Speaker 2: features and creates a model.
Speaker 1: And this is where supervised learning comes in again using
Speaker 1: labeled data.
Speaker 2: Yes, For the kinds of computer vision tasks we're focusing on,
Speaker 2: like classification or detection, we typically use supervised learning. We
Speaker 2: show the algorithm examples images with features and tell it
Speaker 2: the correct answer the label, like this is a cat,
Speaker 2: this is a dog. And unsupervised learning that's about finding
Speaker 2: patterns in data without labels. Sometimes you might use it first,
Speaker 2: maybe to help group images or even automatically generate potential
Speaker 2: labels that you then refine for supervised learning. But supervised
Speaker 2: is key for building these predictive vision models.
Speaker 1: Okay, let's get into the real brains behind this. Deep
Speaker 1: learning and artificial neural networks A and NS. We always
Speaker 1: hear they're inspired by the human brain. How close is
Speaker 1: that analogy? Really, it's a.
Speaker 2: Useful starting point. Think of a single artificial neuron as
Speaker 2: a highly simplified model of a biological one. It receives inputs,
Speaker 2: multiplies them by certain weights which represent the connection strength,
Speaker 2: sums them up and then applies a function and to
Speaker 2: produce an output.
Speaker 1: The simplest version is the perceptron.
Speaker 2: Right. A single perceptron can model basic linear relationships like
Speaker 2: drawing a straight line to separate two groups of data points.
Speaker 1: But the real world isn't usually that simple, is it.
Speaker 1: Things are messy nonlinear.
Speaker 2: Exactly, and that's why we need deep learning, which typically
Speaker 2: uses multilayer perceptrons or MLPs. By stacking layers of these neurons,
Speaker 2: the network can learn incredibly complex nonlinear patterns. That's absolutely
Speaker 2: essential for tackling real world computer vision problems.
Speaker 1: So what does the structure The anatomy of one of
Speaker 1: these deep learning models.
Speaker 2: Look like, Well, you've got an input layer where the
Speaker 2: data like our image feature vector comes in. Then you
Speaker 2: have one or more hidden layers. This is where the
Speaker 2: real heavy lifting and the learning happens. The network figures
Speaker 2: out intermediate representations here, and finally an output layer that
Speaker 2: gives you the final result. Maybe it's a probability for
Speaker 2: each class like eighty percent chance it's a cat twenty
Speaker 2: percent dog. The network learns by adjusting the weights on
Speaker 2: all the connections between neurons in these layers. There are
Speaker 2: also bias nodes that add another adjustable parameter.
Speaker 1: Okay, weights determine connection strength, But how does an individual
Speaker 1: neuron decide whether to fire or what value to pass on?
Speaker 1: You mentioned activation functions.
Speaker 2: Yes, activation functions are critical. They introduce the nonlinearity we need.
Speaker 2: After a neuron sums its weighted inputs, the activation function
Speaker 2: process is that some to produce the neuron's final output.
Speaker 1: What kinds are there?
Speaker 2: There's several common ones. Sigma used to be popular, squashing
Speaker 2: values between zero and one. RAILU rectified linear unit is
Speaker 2: very widely used now It's simple palputationally efficient outputs the
Speaker 2: input if positive and zero.
Speaker 1: Otherwise real U sounds almost too simple.
Speaker 2: It works surprisingly well, and there are variants like leaky
Speaker 2: ReLU elu SELU that try to address some minor potential
Speaker 2: issues with ReLU and for the output layer. In classification tasks,
Speaker 2: softmax is key. Why softmax because it takes the raw
Speaker 2: outputs for each class and turns them into probabilities that
Speaker 2: all add up to one. So you get that nice
Speaker 2: interpretable eighty percent cat twenty percent dog output.
Speaker 1: Got it? So the network has its structure, its neurons,
Speaker 1: its activation functions, how does it actually learn. How does
Speaker 1: it get better? Is it trial and error?
Speaker 2: It's a guided trial and error. You could say. The
Speaker 2: process starts with feed forward. Your input data flows through
Speaker 2: the network layer by layer, activating neurons until it produces
Speaker 2: an output a prediction.
Speaker 1: Okay, the first guess right.
Speaker 2: Then you need to measure how wrong that guess was.
Speaker 2: That's where error functions or loss functions come in. They
Speaker 2: calculate the difference between the network's prediction and the actual
Speaker 2: correct answer, the ground truth. What kinds of loss functions
Speaker 2: depends on the task. For regression predicting, a continuous value
Speaker 2: means squared error MSE is common for binary classification cat
Speaker 2: dog binary cross entropy for classifying among multiple classes digits
Speaker 2: zero nine categorical cross entropy is standard.
Speaker 1: So you calculate the error, then what how does the
Speaker 1: network use that error information?
Speaker 2: That's the job of optimization algorithms. Their goal is to
Speaker 2: adjust the network's weights in a way that minimizes the
Speaker 2: loss function. The most fundamental one is gradient descent, or
Speaker 2: more commonly, stochastic gradient descent SGD.
Speaker 1: Stochastic gradient descent. How does that work?
Speaker 2: Instead of calculating the error over the entire data set
Speaker 2: at once, which is slow. SGD uses small or random
Speaker 2: subsets called mini batches. It calculates the air for a batch,
Speaker 2: figures out which way to adjust the weights to reduce
Speaker 2: that error. That's the gradient part, and takes a small
Speaker 2: step in that direction, And.
Speaker 1: The size of that step is the learning rate exactly.
Speaker 2: The learning rate is a crucial hyper parameter. Too big
Speaker 2: and you might overshoot the minimum error, too small and
Speaker 2: learning takes forever. SGD often includes momentum too, which helps
Speaker 2: smooth out the updates and speed up convergence, especially if
Speaker 2: the air landscape is uneven.
Speaker 1: This is making sense. Let's try to ground it. The
Speaker 1: classic example classifying handwritten digits zero through nine. How would
Speaker 1: you actually build a model for that?
Speaker 2: Yeah, that's the MAST data set, the hull low world
Speaker 2: of deep learning. It means it really concrete. Using a
Speaker 2: library like Keris, which is often used with TensorFlow, makes
Speaker 2: it much simpler. How So, Keras gives you building blocks.
Speaker 2: You define your model layer by layer, maybe an input
Speaker 2: layer matching the image size, a couple hidden layers with
Speaker 2: RAILU activations in an output layer. Then you compile the model,
Speaker 2: telling it which optimizer like SGD and loss function like
Speaker 2: categorical cross entropy.
Speaker 1: To use, and then you train it.
Speaker 2: You call model dot fit. Feeding it the training images
Speaker 2: and their labels the actual digits it iterates to the data,
Speaker 2: adjusting weights. After training. You can use model dot evaluate
Speaker 2: on data it hasn't seen before to check performance, and
Speaker 2: model dot predict to classify new unseen digits.
Speaker 1: And that output layer for digits zero nine, it would
Speaker 1: have ten neurons right, one for each digit.
Speaker 2: Exactly ten neurons, usually with the softmax activation, so each
Speaker 2: one outputs the probability that the input image is that
Speaker 2: specific digit. The highest probability wins.
Speaker 1: Okay, so you've trained it, but how do you know
Speaker 1: if it's actually any good? How do you evaluate it properly?
Speaker 2: That's super important. You need to watch out for two
Speaker 2: main problems, overfitting and underfitting.
Speaker 1: Overfitting is when it memorizes the training data too well.
Speaker 2: Yeah, it gets great results on the data it trained on,
Speaker 2: but fails badly on new unseen data it hasn't learned
Speaker 2: the general patterns. Underfitting is the opposite. The model is
Speaker 2: too simple. It hasn't even learned the training data well enough.
Speaker 1: So how do you measure performance beyond just looking at
Speaker 1: the loss.
Speaker 2: We use specific evaluation metrics. Accuracy is the most basic,
Speaker 2: what percentage did it get right overall? But often that's
Speaker 2: not enough. We look at things like precision and recall.
Speaker 1: Precision and recall remind.
Speaker 2: Me precision asks of all the times the model predicted,
Speaker 2: say digit seven, how many were actually sevens? Recall asks
Speaker 2: of all the actual sevens in the data set? How
Speaker 2: many did the model correctly identify? Ah? Okay?
Speaker 1: Different perspectives on correctness right, and.
Speaker 2: The F one score combines precision and recall into the
Speaker 2: single number, giving a balanced view. You might also look
Speaker 2: at true positive rate negative rate. Depends on the specifics.
Speaker 1: And if the metrics aren't great, you tweak things exactly.
Speaker 2: That's hyperperimeter tuning. You adjust things like the learning rate,
Speaker 2: the number of layers, and the number of neurons per layer.
Speaker 2: Maybe try different optimizers or activation functions until you get
Speaker 2: the best performance on your validation data.
Speaker 1: And once you're happy, you can save the trained model.
Speaker 2: Yep. You can save the model's architecture and its learned weights,
Speaker 2: often into a single file like in dot AH five
Speaker 2: filing caras sensorflow. Then you can load it back later
Speaker 2: instantly without retraining to make predictions or even fine tune
Speaker 2: it further with more data.
Speaker 1: So far, we've mostly talked about classifications, saying this image
Speaker 1: contains a cat, But what about finding where the cat is,
Speaker 1: or finding multiple objects like a cat and a dog
Speaker 1: in the same picture and drawing boxes around them. That's
Speaker 1: object detection, isn't it.
Speaker 2: That's exactly right. Object detection takes it a step further
Speaker 2: than classification. It needs to both identify what objects are
Speaker 2: present and localize them, usually by predicting bounding boxes around them.
Speaker 1: And how do you measure sure how good those bounding
Speaker 1: boxes are.
Speaker 2: The standard metric is IOU or intersection over union. You
Speaker 2: compare the predicted bounding box with the true ground truth box.
Speaker 2: IOU measures the overlap area divided by the total combined area.
Speaker 2: Higher IOU means a better prediction.
Speaker 1: It feels like object detection has evolved incredibly fast. I
Speaker 1: remember early models being quite slow.
Speaker 2: Oh definitely. Early approaches like RCNN region based convolutional neural
Speaker 2: network were groundbreaking, but slow. They first proposed potential regions
Speaker 2: in the image and then ran a classifier on each.
Speaker 1: Region, so lots of repeated computation exactly.
Speaker 2: Then came improvements like fast our CNN and Faster our CNN,
Speaker 2: which cleverly shared computations and introduced a region proposal network
Speaker 2: to speed things up dramatically.
Speaker 1: And mask r CNN.
Speaker 2: Mask RCNN was a really neat extension of Faster our CNN.
Speaker 2: Not only did it detect objects and drawboxes, but it
Speaker 2: also predicted a pixel level mask for each object, essentially
Speaker 2: outlining its exact shape. You could even estimate human poses.
Speaker 1: But the real speed revolution came with single shot detectors
Speaker 1: right SSD and YOLO.
Speaker 2: Absolutely SSD single shot multibox detector and Yolo you Only
Speaker 2: Look Once changed the game for real time detection. Instead
Speaker 2: of proposing regions first, they try to detect objects directly
Speaker 2: in a single pass through the network.
Speaker 1: How does SSD work?
Speaker 2: Roughly, SSD uses a set of pre defined default boxes
Speaker 2: of different sizes and aspect ratios at various locations in
Speaker 2: the feature maps extracted by the network. It predicts offsets
Speaker 2: to adjust these boxes and confidence scores for each object
Speaker 2: class directly from these feature maps. It uses techniques like
Speaker 2: data augmentation and non maximum suppression to improve accuracy and efficiency.
Speaker 1: And Yolo you Only Look Once.
Speaker 2: Great name Yolo is famous for its speed. It divides
Speaker 2: the input image into a grid. For each grid cell,
Speaker 2: it predicts bounding boxes, confidence scores for those boxes, how
Speaker 2: likely they contain an object and class probabilities all in
Speaker 2: one go.
Speaker 1: And it got faster and better with new versions.
Speaker 2: Yeah, yolob two used a network called Darknet nineteen and
Speaker 2: three d use the deeper Darknet fifty three, improving accuracy
Speaker 2: while maintaining impressive speed. These single shot detectors made real
Speaker 2: time object detection on video feasible.
Speaker 1: Okay, so detection finds objects in a single frame, but
Speaker 1: what about video? How do you follow a specific object
Speaker 1: from one frame to the next. That's object tracking, right.
Speaker 2: Object tracking builds on detection. You detect objects in each frame,
Speaker 2: But then you need a way to link detections of
Speaker 2: the same object to cross frames, maintaining its unique identity.
Speaker 1: How do you do that linkage? How do you know
Speaker 1: the car detected now is the same car detected a
Speaker 1: second ago?
Speaker 2: There are various methods. One interesting technique involves image hashing
Speaker 2: like different hashing, or de.
Speaker 1: Haash hashing like creating a fingerprint exactly.
Speaker 2: Dehash generates a compact fingerprint or hash value for an
Speaker 2: image patch like the detected object based on differences between
Speaker 2: adjacent pixels. It's very fast to compute.
Speaker 1: So each detected object gets a hash.
Speaker 2: Then what then You compare the DASH of a newly
Speaker 2: detected object in the current frame with the dehashes of
Speaker 2: objects tracked in the previous frame. The comparison is done
Speaker 2: using Hamming distance.
Speaker 1: Hamming distance that just counts how many bits are different
Speaker 1: between two hashes.
Speaker 2: Precisely, a low Hamming distance between two de haashes means
Speaker 2: the image patches are very similar. So if a new
Speaker 2: detections hash is very close to a previously tracked objects hash,
Speaker 2: you can confidently say it's the same object and update
Speaker 2: its track.
Speaker 1: That's clever. A simple comparison tells you if it's the
Speaker 1: same thing.
Speaker 2: Yeah, it's efficient, and you can integrate this tracking logic
Speaker 2: with say a web framework like flask to visualize the
Speaker 2: tracks on a live video stream in your browser.
Speaker 1: Cool. Now, let's narrow down to a really specific but
Speaker 1: huge application. Face recognition. Is that just another object detection problem.
Speaker 2: It starts like one. You first need to detect the face,
Speaker 2: but then it goes further into identification. The core idea
Speaker 2: is to create a unique numerical representation for each face,
Speaker 2: often called a facial footprint, or more technically, face embedding.
Speaker 1: And embedding like the feature vectors we talked about, are.
Speaker 2: Very similar concept Yes, it's a compact vector, typically one
Speaker 2: twenty eight dimensional derived from key facial features maybe around
Speaker 2: eighty notal points like the corners of the eyes, tip
Speaker 2: of the nose, et cetera. This vector captures the unique
Speaker 2: characteristics of that specific face.
Speaker 1: And how are these embeddings generated.
Speaker 2: Deep neural networks are key here, particularly models like face
Speaker 2: net developed by a Google. Face net is designed specifically
Speaker 2: to take a face image and directly output this highly
Speaker 2: discriminating one hundred and twenty eight dimensional embedding.
Speaker 1: So face net learns to create good embeddings exactly.
Speaker 2: It's trained using a clever method involving a triplet loss function.
Speaker 2: The network has shown three images at a time, an
Speaker 2: anchor image a person's face, a positive image another picture
Speaker 2: of the same person, and a negative image a picture
Speaker 2: of a different person and the goal. The goal is
Speaker 2: to learn embeddings such that the distance between the anchor
Speaker 2: and positive embeddings is small, while the distance between the
Speaker 2: anchor and negative embeddings is large. This forces the network
Speaker 2: to create embeddings that cluster faces of the same person
Speaker 2: to get together and push faces of different people far
Speaker 2: apart in that one hundred and twenty eight dimensional space.
Speaker 1: Fascinating. So once you have these embeddings, you can compare
Speaker 1: them to recognize people.
Speaker 2: Yep. For face verification, is this the same person? You
Speaker 2: just compare the abettings of two faces. For recognition, who
Speaker 2: is this person? You compare the new faces of betting
Speaker 2: against a database of known ebttings. Different face neet architectures,
Speaker 2: often based on models like conception, are for trade offs
Speaker 2: between computational cost measured in f lops floating point operations
Speaker 2: per second and the accuracy of the embeddings.
Speaker 1: Okay, this tech is clearly powerful, but let's talk about
Speaker 1: the real world. Where is computer vision really making a difference?
Speaker 1: Moving beyond the lab?
Speaker 2: Oh? Absolutely, One huge area is industrial manufacturing. Think about
Speaker 2: quality control. Real time defect detection using computer vision is
Speaker 2: replacing slow, inconsistent and often expensive manual inspection.
Speaker 1: Can you give an example, sure.
Speaker 2: Consider steal production. There's a data set called nudet with
Speaker 2: images of steel surfaces showing various defects, things like crazing
Speaker 2: inclusion patches, pitted surfaces, rolled in scale scratches.
Speaker 1: Things a human might miss or classify inconsistently exactly.
Speaker 2: A trained computer vision system can scan these surfaces continuously
Speaker 2: and reliably identify these defects much faster and often more
Speaker 2: accurately than a person could, especially where long shifts. It
Speaker 2: leads to better quality, control, less waste, lower costs.
Speaker 1: And building such a system requires good data. Right, you
Speaker 1: need labeled examples of these defects absolutely crucial.
Speaker 2: You need tools for annotation. Microsoft's VOTT the Visual Object
Speaker 2: Tagging Tool is a good example. Lets you drop bounding
Speaker 2: boxes around defects and images and assigned labels, creating the
Speaker 2: ground truth data and needed to train the detection models.
Speaker 1: This sounds like it involves massive data sets, complex models.
Speaker 1: Training must be a huge undertaking, probably not something you
Speaker 1: do on your laptop.
Speaker 2: Definitely not for state of the art models. Training these
Speaker 2: deep learning vision models requires enormous computational resources. We're talking
Speaker 2: large data sets, often multiple high end GPUs working in parallel,
Speaker 2: and training times that can stretch from hours to days,
Speaker 2: even weeks.
Speaker 1: So this is where cloud computing really comes into its own.
Speaker 2: Precisely, cloud platforms like Google Cloud Platform GCP or Microsoft
Speaker 2: Azure provide the scalable infrastructure you need. You can rent
Speaker 2: powerful virtual machines with multiple GPUs, access vast storage for
Speaker 2: your data sets, and leverage specialized machine learning services.
Speaker 1: And when you have all that power, you need ways
Speaker 1: to use it efficiently, right, like training across multiple machines
Speaker 1: or GPUs at once.
Speaker 2: Yes, that's called distributed training. It's essential for handling these
Speaker 2: large models and data sets in a reasonable timeframe. There
Speaker 2: are a couple of main strategies. One is data parallelism.
Speaker 2: You replicate the model on multiple GPUs or machines, but
Speaker 2: you split the data batch among them. Each replica processes
Speaker 2: it's part of the batch, calculates updates gradients, and then
Speaker 2: these updates are somehow combined to update the main model.
Speaker 1: How are they combined?
Speaker 2: It can be synchronous where all workers wait and aggregate
Speaker 2: gradients together at each step, ensuring consistency. Or it could
Speaker 2: be asynchronous where workers updata central model independently, which can
Speaker 2: sometimes be faster but potentially less stable.
Speaker 1: Okay, that's data parallelism. What's the other strategy?
Speaker 2: Model parallelism. This is used when the model itself is
Speaker 2: too massive. To fit into the memory of a single GPU.
Speaker 2: You actually split the model across different devices, with different
Speaker 2: layers residing on different GPUs, data flows between them during computation.
Speaker 2: It's generally more complex to implement than data parallelism.
Speaker 1: And frameworks like TensorFlow provide tools to manage this distribution.
Speaker 2: They do. TensorFlow has built in tf dot distribute DOT
Speaker 2: strategy options. Mirrored strategy is common for using multiple GPUs
Speaker 2: on one machine. It handles the data parallelism synchronously. Multi
Speaker 2: worker mirrored strategy extends this across multiple machines. For really
Speaker 2: large scale parameter server strategy uses dedicated servers just to
Speaker 2: hold and update the model's parameters, while worker nodes do
Speaker 2: the computation.
Speaker 1: So the tools are there to help manage this complexity.
Speaker 2: Yes, and they're also libraries like horvad developed by Uber,
Speaker 2: which are specifically designed to make distributed deep learning training
Speaker 2: easier and more efficient, often integrating well with TensorFlow, PyTorch,
Speaker 2: and cloud environments.
Speaker 1: Hashtag tag tech outro.
Speaker 2: Wow. Okay, that was quite the journey. We've really taken
Speaker 2: a deep dive today, haven't we from understanding the absolute basics,
Speaker 2: like what a pixel even is? To a computer.
Speaker 1: Yeah, through manipulating them, extracting meaningful features, getting.
Speaker 2: Into the brains with neural networks and deep learning how
Speaker 2: they learn.
Speaker 1: And then looking at these reallyad advanced applications like detecting objects,
Speaker 1: tracking them across video frames, even recognizing individual faces. It's
Speaker 1: amazing how it builds up. And as you said, this
Speaker 1: isn't just cool tech.
Speaker 2: In a lab, not at all. Computer vision powered by
Speaker 2: deep learning is genuinely transforming industries manufacturing, security, healthcare, retail.
Speaker 2: It's opening up totally new ways for us to interact
Speaker 2: with machines and the world.
Speaker 1: It really is. So as we wrack up, here's something
Speaker 1: to think about. These aiis are getting more and more sophisticated.
Speaker 1: They're not just seeing, they're starting to interpret, understand context,
Speaker 1: maybe even predict.
Speaker 2: The capabilities are growing exponentially.
Speaker 1: So what happens next as these intelligent eyes become even
Speaker 1: more pervasive? What new frontiers will they unlock? And maybe
Speaker 1: more importantly, what new questions, perhaps ethical ones, will we
Speaker 1: need to grapple with as AI's ability to see starts
Speaker 1: to rival or even exceed our own