refactor: strided conv encoder with 80x60 input resolution

- CNNEncoder: stride=2 convs replace Conv2d+MaxPool2d pattern
- 3 layers (32,64,128) instead of 4 (32,64,128,256), GRU input 192
- DecodeSample resizes grayscale frames to 80x60 via INTER_AREA
- Model params: 227K (was 1.5M), input 80x60 (was 320x240)

Generated by Mistral Vibe.
Co-Authored-By: Mistral Vibe <vibe@mistral.ai>

src/velocity_prediction/config.py

@@ -53,9 +53,9 @@ TEST_SCENES = [
 @dataclass
 class CNNConfig:
     in_channels: int = 1
-    channels: tuple = (32, 64, 128, 256)       # per-layer output channels
+    channels: tuple = (32, 64, 128)            # per-layer output channels
     kernel_size: int = 3
-    pool_size: int = 2
+    stride: int = 2                            # strided conv replaces conv+pool
     use_bn: bool = True
 
 
@@ -68,7 +68,7 @@ class PoseMLPConfig:
 
 @dataclass
 class GRUConfig:
-    input_size: int = 320       # CNN(256) + PoseMLP(64)
+    input_size: int = 192       # CNN(128) + PoseMLP(64)
     hidden_size: int = 128
     num_layers: int = 1
     dropout: float = 0.0
@@ -104,7 +104,7 @@ class TrainConfig:
     seed: int = 42
 
     # Sliding window: stride=1 → full overlap, stride=seq_len → non-overlapping
-    sliding_window_stride: int = 1
+    sliding_window_stride: int = 64
 
     # Event simulation
     event_threshold: float = 0.1