diff --git a/exp/kerokero/prepare_data.py b/exp/kerokero/prepare_data.py
--- a/exp/kerokero/prepare_data.py
+++ b/exp/kerokero/prepare_data.py
@@ -9,7 +9,7 @@ import cv2 as cv
 
 sys.path.append("..")
 sys.path.append("../../src")
-from annotations import DataFile,computeBoundingBox,Corners
+from annotations import DataFile,computeBoundingBox,Corners,EPoint
 from geometry import Line
 from kerokero.transformation_matrices import getIdentity,getRotation,getTranslation,getScale,getMirroring,getProjection
 
@@ -20,10 +20,14 @@ class Sample:
 	SIDE=224
 
 	def __init__(self,img,grid):
+		""":param img: a greyscale image as a 2D np.uint8
+		:param grid: iterable of 4 EPoints, ie. Corners"""
 		self.img=img
 		self.grid=grid
 
 	def transform(self):
+		""":return: (img, grid), where img is a 2D np.float32 with values in (0,1),
+		grid [(float) x, (float) y, ...], with x, y in (-1,1)"""
 		center=self._getCenter()
 		m=getIdentity()
 		t1=getTranslation(-center.x,-center.y)
@@ -34,8 +38,10 @@ class Sample:
 			m=np.matmul(mi,m)
 		m=np.matmul(self._computeCrop(m),m)
 		img=cv.warpPerspective(self.img,m,(self.SIDE,self.SIDE))
+		img=np.float32(img)/255
 		grid=Corners(c.transform(m) for c in self.grid)
-		return (img,list(itertools.chain.from_iterable(grid)))
+		grid=list(map(lambda p: 2*p/self.SIDE-EPoint(1,1), grid))
+		return (img,grid,list(itertools.chain.from_iterable(grid)))
 
 	def _getCenter(self):
 		(a,b,c,d)=self.grid
@@ -53,7 +59,7 @@ class Sample:
 		return np.matmul(scale,t2)
 
 	def show(self):
-		img=np.copy(self.img)
+		img=cv.cvtColor(self.img,cv.COLOR_GRAY2BGR)
 		for c in self.grid:
 			cv.circle(img,(int(c.x),int(c.y)),3,[0,255,0],-1)
 		show(img)
@@ -81,7 +87,8 @@ def harvestDir(path):
 		img=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
 		for b in boards:
 			sample=Sample(img,b.grid)
-			(transformedImg,label)=sample.transform()
+			(transformedImg,transformedGrid,label)=sample.transform()
+			# Sample(np.uint8(transformedImg*255),map(lambda c: (c+EPoint(1,1))*Sample.SIDE/2,transformedGrid)).show()
 			yield (transformedImg,label)
 
 
@@ -101,8 +108,8 @@ def loadDataset(root):
 	labels=[labels[k] for k in keys]
 	m=int(n*trainRatio)
 	return (
-		(np.uint8(images[:m]),np.float32(labels[:m])),
-		(np.uint8(images[m:]),np.float32(labels[m:]))
+		(np.float32(images[:m]),np.float32(labels[:m])),
+		(np.float32(images[m:]),np.float32(labels[m:]))
 	)