diff --git a/MLPP/Data/Data.cpp b/MLPP/Data/Data.cpp
index 9d29bea..c27bd4d 100644
--- a/MLPP/Data/Data.cpp
+++ b/MLPP/Data/Data.cpp
@@ -154,6 +154,63 @@ namespace MLPP{
         }
         return grayScale;
     }
+
+    std::vector<std::vector<std::vector<double>>> Data::rgb2ycbcr(std::vector<std::vector<std::vector<double>>> input){
+        LinAlg alg;
+        std::vector<std::vector<std::vector<double>>> YCbCr;
+        YCbCr = alg.resize(YCbCr, input);
+        for(int i = 0; i < YCbCr.size(); i++){
+            for(int j = 0; j < YCbCr[i].size(); j++){
+                YCbCr[0][i][j] = 0.299 * input[0][i][j] + 0.587 * input[1][i][j] + 0.114 * input[2][i][j];
+                YCbCr[1][i][j] = -0.169 * input[0][i][j] - 0.331 * input[1][i][j] + 0.500 * input[2][i][j];
+                YCbCr[2][i][j] = 0.500 * input[0][i][j] - 0.419 * input[1][i][j] - 0.081 * input[2][i][j];
+            }
+        }
+        return YCbCr;
+    }
+
+    std::vector<std::vector<std::vector<double>>> Data::rgb2hsv(std::vector<std::vector<std::vector<double>>> input){
+        LinAlg alg;
+        std::vector<std::vector<std::vector<double>>> HSV;
+        HSV = alg.resize(HSV, input);
+        for(int i = 0; i < HSV.size(); i++){
+            for(int j = 0; j < HSV[i].size(); j++){
+                double rPrime = input[0][i][j] / 255;
+                double gPrime = input[1][i][j] / 255;
+                double bPrime = input[2][i][j] / 255; 
+
+                double cMax = alg.max({rPrime, gPrime, bPrime});
+                double cMin = alg.min({rPrime, gPrime, bPrime});
+                double delta = cMax - cMin; 
+
+                // H calculation.
+                if(delta == 0){
+                    HSV[0][i][j] = 0; 
+                }
+                else{
+                    if(cMax == rPrime){
+                        HSV[0][i][j] = 60 * fmod(((gPrime - bPrime) / delta), 6);
+                    }
+                    else if(cMax == gPrime){
+                        HSV[0][i][j] = 60 * ( (bPrime - rPrime)  / delta + 2); 
+                    }
+                    else{ // cMax == bPrime
+                        HSV[0][i][j] = 60 * ( (rPrime - gPrime)  / delta + 6); 
+                    }
+                }
+                
+                // S calculation.
+                if(cMax == 0){
+                    HSV[1][i][j] = 0; 
+                }
+                else{ HSV[1][i][j] = delta/cMax; }
+
+                // V calculation. 
+                HSV[2][i][j] = cMax;
+            }
+        }
+        return HSV;
+    }
     
     // TEXT-BASED & NLP
     std::string Data::toLower(std::string text){
diff --git a/MLPP/Data/Data.hpp b/MLPP/Data/Data.hpp
index 9de85d9..9770d79 100644
--- a/MLPP/Data/Data.hpp
+++ b/MLPP/Data/Data.hpp
@@ -30,6 +30,8 @@ class Data{
 
         // Images
         std::vector<std::vector<double>> rgb2gray(std::vector<std::vector<std::vector<double>>> input);
+        std::vector<std::vector<std::vector<double>>> rgb2ycbcr(std::vector<std::vector<std::vector<double>>> input);
+        std::vector<std::vector<std::vector<double>>> rgb2hsv(std::vector<std::vector<std::vector<double>>> input);
 
         // Text-Based & NLP
         std::string toLower(std::string text);
diff --git a/a.out b/a.out
index d22b03a..540093a 100755
Binary files a/a.out and b/a.out differ
diff --git a/main.cpp b/main.cpp
index e965fd2..b79b4e9 100644
--- a/main.cpp
+++ b/main.cpp
@@ -363,18 +363,18 @@ int main() {
     // Possible Weight Init Methods: Default, Uniform, HeNormal, HeUniform, XavierNormal, XavierUniform
     // Possible Activations: Linear, Sigmoid, Swish, Softplus, Softsign, CLogLog, Ar{Sinh, Cosh, Tanh, Csch, Sech, Coth},  GaussianCDF, GELU, UnitStep
     // Possible Loss Functions: MSE, RMSE, MBE, LogLoss, CrossEntropy, HingeLoss
-    std::vector<std::vector<double>> inputSet = {{0,0,1,1}, {0,1,0,1}};
-    std::vector<double> outputSet = {0,1,1,0};
-    ANN ann(alg.transpose(inputSet), outputSet);
-    ann.addLayer(2, "Sigmoid");
-    ann.addLayer(2, "Sigmoid");
-    ann.addOutputLayer("Sigmoid", "LogLoss");
+    // std::vector<std::vector<double>> inputSet = {{0,0,1,1}, {0,1,0,1}};
+    // std::vector<double> outputSet = {0,1,1,0};
+    // ANN ann(alg.transpose(inputSet), outputSet);
+    // ann.addLayer(2, "Sigmoid");
+    // ann.addLayer(2, "Sigmoid");
+    // ann.addOutputLayer("Sigmoid", "LogLoss");
     //ann.AMSGrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, 1);
     //ann.Adadelta(1, 1000, 2, 0.9, 0.000001, 1);
     //ann.Momentum(0.1, 8000, 2, 0.9, true, 1);
 
     //ann.setLearningRateScheduler("Step", 0.5, 1000);
-    ann.gradientDescent(1, 5, 1);
+    // ann.gradientDescent(1, 5, 1);
     //alg.printVector(ann.modelSetTest(alg.transpose(inputSet)));
     //std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl;
 
@@ -466,16 +466,23 @@ int main() {
 
 
     // // CONVOLUTION, POOLING, ETC.. 
-    // std::vector<std::vector<double>> input = {
-    //     {1,1,1,1,0,0,0,0},
-    //     {1,1,1,1,0,0,0,0},
-    //     {1,1,1,1,0,0,0,0},
-    //     {1,1,1,1,0,0,0,0},
-    //     {1,1,1,1,0,0,0,0},
-    //     {1,1,1,1,0,0,0,0},
-    //     {1,1,1,1,0,0,0,0},
-    //     {1,1,1,1,0,0,0,0}
-    // };
+    std::vector<std::vector<double>> input = {
+        {255,255,255,255,0,0,0,0},
+        {1,1,1,1,0,0,0,0},
+        {1,1,1,1,0,0,0,0},
+        {1,1,1,1,0,0,0,0},
+        {1,1,1,1,0,0,0,0},
+        {1,1,1,1,0,0,0,0},
+        {1,1,1,1,0,0,0,0},
+        {1,1,1,1,0,0,0,0}
+    };
+
+    std::vector<std::vector<std::vector<double>>> tensorSet; 
+    tensorSet.push_back(input);
+    tensorSet.push_back(input);
+    tensorSet.push_back(input);
+
+    alg.printTensor(data.rgb2hsv(tensorSet));
 
     // alg.printMatrix(conv.convolve(input, conv.getPrewittVertical(), 1)); // Can use padding
     // alg.printMatrix(conv.pool(input, 4, 4, "Max")); // Can use Max, Min, or Average pooling.