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+# Neural networks in openpilot
+To view the architecture of the ONNX networks, you can use [netron](https://netron.app/)
+
+## Supercombo
+### 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
+      * 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
+* **traffic convention**
+  * one-shot 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
+
+
+### Supercombo output format (Full size: 6472 x float32)
+* **plan**
+  * 5 potential desired plan predictions : 4955 = 5 * 991
+    * predicted mean and standard deviation of the following values at 33 timesteps : 990 = 2 * 33 * 15
+      * x,y,z position in current frame (meters)
+      * x,y,z velocity in local frame (meters/s)
+      * x,y,z acceleration local frame (meters/(s*s))
+      * roll, pitch , yaw in current frame (radians)
+      * roll, pitch , yaw rates in local frame (radians/s)
+    * probability[^1] of this plan hypothesis being the most likely: 1
+* **lanelines**
+  * 4 lanelines (outer left, left, right, and outer right): 528 = 4 * 132
+    * predicted mean and standard deviation for the following values at 33 x positions : 132 = 2 * 33 * 2
+      * y position in current frame (meters)
+      * z position in current frame (meters)
+* **laneline probabilties**
+  * 2 probabilities[^1] that each of the 4 lanelines exists : 8 = 4 * 2
+    * deprecated probability
+    * used probability
+* **road-edges**
+  * 2 road-edges (left and right): 264 = 2 * 132
+    * predicted mean and standard deviation for the following values at 33 x positions : 132 = 2 * 33 * 2
+      * y position in current frame (meters)
+      * z position in current frame (meters)
+* **leads**
+  * 2 hypotheses for potential lead cars : 102 = 2 * 51
+    * predicted mean and stadard deviation for the following values at 0,2,4,6,8,10s : 48 = 2 * 6 * 4 
+      * x position of lead in current frame (meters)
+      * y position of lead in current frame (meters)
+      * speed of lead (meters/s)
+      * acceleration of lead(meters/(s*s))
+    * probabilities[^1] this hypothesis is the most likely hypothesis at 0s, 2s or 4s from now : 3 
+* **lead probabilities**
+  * probability[^1] that there is a lead car at 0s, 2s, 4s from now : 3 = 1 * 3
+* **desire state**
+  * probability[^1] that the model thinks it is executing each of the 8 potential desire actions : 8
+* **meta** [^2]
+  * Various metadata about the scene : 80 = 1 + 35 + 12 + 2 + 32
+    * Probability[^1] that openpilot is engaged : 1
+    * Probabilities[^1] of various things happening between now and 2,4,6,8,10s : 35 = 5 * 7
+      * Disengage of openpilot with gas pedal
+      * Disengage of openpilot with brake pedal
+      * Override of openpilot steering
+      * 3m/(s*s) of deceleration
+      * 4m/(s*s) of deceleration
+      * 5m/(s*s) of deceleration
+    * Probabilities[^1] of left or right blinker being active at 0,2,4,6,8,10s : 12 = 6 * 2
+    * Unused : 2
+    * Probabilities[^1] that each of the 8 desires is being executed at 0,2,4,6s : 32 = 4 * 8
+
+* **pose** [^2]
+  * predicted mean and standard deviation of current translation and rotation rates : 12 = 2 * 6
+    * x,y,z velocity in current frame (meters/s)
+    * roll, pitch , yaw rates in current frame (radians/s)
+* **recurrent state**
+  * The recurrent state vector that is fed back into the GRU for temporal context : 512
+
+[^1]: All probabilities are in logits, so you need to apply sigmoid or softmax functions to get actual probabilities
+[^2]: These outputs come directly from the vision blocks, they do not have access to temporal state or the desire input
+