diff --git a/models/README.md b/models/README.md
index 2f471000b2..522ddd3ec1 100644
--- a/models/README.md
+++ b/models/README.md
@@ -5,14 +5,14 @@ To view the architecture of the ONNX networks, you can use [netron](https://netr
 ### Supercombo input format (Full size: 393738 x float32)
 * **image stream**
   * Two consecutive images (256 * 512 * 3 in RGB) recorded at 20 Hz : 393216 = 2 * 6 * 128 * 256
-    * Each 256 * 512 image is represented in YUV with 6 channels : 6 * 128 * 256
+    * Each 256 * 512 image is represented in YUV420 with 6 channels : 6 * 128 * 256
       * Channels 0,1,2,3 represent the full-res Y channel and are represented in numpy as Y[::2, ::2], Y[::2, 1::2], Y[1::2, ::2], and Y[1::2, 1::2]
       * Channel 4 represents the half-res U channel
       * Channel 4 represents the half-res V channel
 * **desire**
-  * one-shot encoded vector to command model to execute certain actions, bit only needs to be sent for 1 frame : 8
+  * one-hot encoded vector to command model to execute certain actions, bit only needs to be sent for 1 frame : 8
 * **traffic convention**
-  * one-shot encoded vector to tell model whether traffic is right-hand or left-hand traffic : 2
+  * one-hot encoded vector to tell model whether traffic is right-hand or left-hand traffic : 2
 * **recurrent state**
   * The recurrent state vector that is fed back into the GRU for temporal context : 512