diff --git a/src/main/java/ai/nets/samj/EfficientSamJ.java b/src/main/java/ai/nets/samj/EfficientSamJ.java
index cb5eb7a..26b0254 100644
--- a/src/main/java/ai/nets/samj/EfficientSamJ.java
+++ b/src/main/java/ai/nets/samj/EfficientSamJ.java
@@ -102,6 +102,8 @@ public class EfficientSamJ extends AbstractSamJ implements AutoCloseable {
 	 * paths and names
 	 */
 	private String IMPORTS_FORMATED;
+	
+	private static int REENCODE_THRESH = 50;
 
 	/**
 	 * Create an instance of the class to be able to run EfficientSAM in Java.
@@ -574,6 +576,7 @@ public List<Polygon> processBox(int[] boundingBox)
 	 */
 	public List<Polygon> processBox(int[] boundingBox, boolean returnAll)
 			throws IOException, RuntimeException, InterruptedException {
+		needsMoreResolution(boundingBox);
 		this.script = "";
 		processBoxWithSAM(returnAll);
 		HashMap<String, Object> inputs = new HashMap<String, Object>();
@@ -584,6 +587,31 @@ public List<Polygon> processBox(int[] boundingBox, boolean returnAll)
 		return polys;
 	}
 
+	/**
+	 * For bounding box masks, check whether the its size is too small compared to the size
+	 * of the encoded image.
+	 * 
+	 * Approximately, if the original image encoded is about 20 times bigger than the bounding box size,
+	 * the resolution of the SAM-based model encodings will not be enough to identify the object of interest,
+	 * thus re-encoding of a zoomed part of the image will be necessary.
+	 * 
+	 * @param boundingBox
+	 * 	bounding box of interest
+	 * @return whether the bounding box of interest is big enough to produce good results or not
+	 */
+	public boolean needsMoreResolution(int[] boundingBox) {
+		long xSize = boundingBox[2] - boundingBox[0];
+		long ySize = boundingBox[3] - boundingBox[1];
+		long encodedX = targetDims[1];
+		long encodedY = targetDims[0];
+		if (xSize * REENCODE_THRESH < encodedX && ySize * REENCODE_THRESH < encodedY)
+			return true;
+		return false;
+	}
+	
+	public boolean checkBoundingBox() {
+		return false;
+	}
 
 	@Override
 	/**
@@ -682,7 +710,7 @@ private void processBoxWithSAM(boolean returnAll) {
 				+ "mask = torch.ge(predicted_logits[0, 0, 0, :, :], 0).cpu().detach().numpy()" + System.lineSeparator()
 				+ "task.update('end predict')" + System.lineSeparator()
 				+ "task.update(str(mask.shape))" + System.lineSeparator()
-				//+ "np.save('/temp/aa.npy', mask)" + System.lineSeparator()
+				//+ "np.save('/home/carlos/git/mask.npy', mask)" + System.lineSeparator()
 				+ "contours_x,contours_y = get_polygons_from_binary_mask(mask, only_biggest=" + (!returnAll ? "True" : "False") + ")" + System.lineSeparator()
 				+ "task.update('all contours traced')" + System.lineSeparator()
 				+ "task.outputs['contours_x'] = contours_x" + System.lineSeparator()