diff --git a/core/grayscale_test.go b/core/grayscale_test.go
index 1f9cfe3..8628fb7 100644
--- a/core/grayscale_test.go
+++ b/core/grayscale_test.go
@@ -1,4 +1,4 @@
-package pigo
+package pigo_test
 
 import (
 	"image"
@@ -7,7 +7,7 @@ import (
 )
 
 const (
-	ImgWidth = 10
+	ImgWidth  = 10
 	ImgHeight = 10
 )
 
diff --git a/core/image_test.go b/core/image_test.go
index ea10b01..6b2b283 100644
--- a/core/image_test.go
+++ b/core/image_test.go
@@ -1,4 +1,4 @@
-package pigo
+package pigo_test
 
 import (
 	"image"
diff --git a/core/pigo_test.go b/core/pigo_test.go
new file mode 100644
index 0000000..2118868
--- /dev/null
+++ b/core/pigo_test.go
@@ -0,0 +1,65 @@
+package pigo_test
+
+import (
+	"io/ioutil"
+	"log"
+	"path/filepath"
+	"testing"
+
+	pigo "github.com/esimov/pigo/core"
+)
+
+var pigoCascadeFile []byte
+
+func init() {
+	var err error
+	pigoCascadeFile, err = ioutil.ReadFile("../data/facefinder")
+	if err != nil {
+		log.Fatalf("Error reading the cascade file: %v", err)
+	}
+}
+
+func BenchmarkPigo(b *testing.B) {
+	source := filepath.Join("../testdata", "sample.jpg")
+	src, err := pigo.GetImage(source)
+	if err != nil {
+		log.Fatalf("Error reading the source file: %s", err)
+	}
+
+	pixs := pigo.RgbToGrayscale(src)
+	cols, rows := src.Bounds().Max.X, src.Bounds().Max.Y
+
+	cParams := pigo.CascadeParams{
+		MinSize:     20,
+		MaxSize:     1000,
+		ShiftFactor: 0.2,
+		ScaleFactor: 1.1,
+		ImageParams: pigo.ImageParams{
+			Pixels: pixs,
+			Rows:   rows,
+			Cols:   cols,
+			Dim:    cols,
+		},
+	}
+	pg := pigo.NewPigo()
+	// Unpack the binary file. This will return the number of cascade trees,
+	// the tree depth, the threshold and the prediction from tree's leaf nodes.
+	classifier, err := pg.Unpack(pigoCascadeFile)
+	if err != nil {
+		log.Fatalf("Error reading the cascade file: %s", err)
+	}
+
+	var dets []pigo.Detection
+	b.ResetTimer()
+
+	for i := 0; i < b.N; i++ {
+		pixs := pigo.RgbToGrayscale(src)
+		cParams.Pixels = pixs
+		// Run the classifier over the obtained leaf nodes and return the detection results.
+		// The result contains quadruplets representing the row, column, scale and detection score.
+		dets = classifier.RunCascade(cParams, 0.0)
+		// Calculate the intersection over union (IoU) of two clusters.
+		dets = classifier.ClusterDetections(dets, 0.1)
+	}
+	_ = dets
+}