diff --git a/src/detectionSoftware/run.py b/src/detectionSoftware/run.py
index 9909eabd..c6d80cdf 100644
--- a/src/detectionSoftware/run.py
+++ b/src/detectionSoftware/run.py
@@ -1,22 +1,58 @@
 import sys
+import subprocess
 import threading
-import gi
 import logging
 from flask import Flask, Response, render_template_string
 
-# GStreamer dependencies
+# --- PART 1: ROBUST DETECTION (Unchanged) ---
+def detect_camera_resolution_isolated():
+    print("--- Spawning isolated process for detection ---")
+    detection_script = """
+import sys
+try:
+    from pypylon import pylon
+    tl_factory = pylon.TlFactory.GetInstance()
+    devices = tl_factory.EnumerateDevices()
+    if not devices:
+        print("0,0")
+    else:
+        cam = pylon.InstantCamera(tl_factory.CreateDevice(devices[0]))
+        cam.Open()
+        print(f"{cam.Width.GetValue()},{cam.Height.GetValue()}")
+        cam.Close()
+except Exception:
+    print("0,0")
+"""
+    try:
+        result = subprocess.run(
+            [sys.executable, "-c", detection_script],
+            capture_output=True, text=True, check=True
+        )
+        parts = result.stdout.strip().split(',')
+        w, h = int(parts[0]), int(parts[1])
+        if w == 0: return 1920, 1080
+        print(f"Isolated Detection Success: {w}x{h}")
+        return w, h
+    except Exception as e:
+        print(f"Subprocess detection failed: {e}")
+        return 1920, 1080
+
+CAM_W, CAM_H = detect_camera_resolution_isolated()
+
+# Calculate Grid
+STREAM_WIDTH = CAM_W
+STREAM_HEIGHT = CAM_H
+TILED_WIDTH = CAM_W * 2
+TILED_HEIGHT = CAM_H
+
+# --- PART 2: FLASK & GSTREAMER ---
+import gi
 gi.require_version('Gst', '1.0')
 from gi.repository import Gst, GLib
 
-# --- Configuration ---
 CAMERA_1_SERIAL = "40650847" 
 CAMERA_2_SERIAL = "40653314"
 
-STREAM_WIDTH = 1920
-STREAM_HEIGHT = 1080
-TILED_WIDTH = 1920
-TILED_HEIGHT = 1080
-
 app = Flask(__name__)
 frame_buffer = None
 buffer_lock = threading.Lock()
@@ -44,65 +80,76 @@ class GStreamerPipeline(threading.Thread):
 
         buffer = sample.get_buffer()
         success, map_info = buffer.map(Gst.MapFlags.READ)
-        
         if not success: return Gst.FlowReturn.ERROR
 
         global frame_buffer
         with buffer_lock:
+            # Copy memory from GPU buffer to CPU for Flask
             frame_buffer = bytes(map_info.data)
 
         buffer.unmap(map_info)
         return Gst.FlowReturn.OK
 
     def build_pipeline(self):
-        # FIX: Added 'compute-hw=1' to nvvideoconvert.
-        # This forces the conversion to happen on the GPU (CUDA) instead of the VIC,
-        # which fixes the "RGB/BGR not supported" error.
+        # PERFORMANCE TUNING:
+        # 1. PacketSize=8192: Critical for GigE cameras to reduce CPU interrupts (Enable Jumbo Frames on NIC!)
+        # 2. FrameRate: Capped at 30 to prevent saturating the USB/GigE bus.
+        
+        cam_settings = (
+            "cam::TriggerMode=Off "
+            "cam::AcquisitionFrameRateEnable=true cam::AcquisitionFrameRate=30.0 "
+            "cam::ExposureAuto=Continuous "
+            "cam::GainAuto=Continuous "
+        )
 
-        # Source 1
+        # Source 1: Mono -> CPU Convert (Light) -> GPU Upload
         src1 = (
-            f"pylonsrc device-serial-number={CAMERA_1_SERIAL} "
-            "cam::TriggerMode=Off cam::AcquisitionFrameRateEnable=true cam::AcquisitionFrameRate=30.0 ! "
-            "videoconvert ! "
-            "nvvideoconvert compute-hw=1 ! " 
+            f"pylonsrc device-serial-number={CAMERA_1_SERIAL} {cam_settings} ! "
+            "video/x-raw,format=GRAY8 ! "
+            "videoconvert ! "             # Light CPU load: Gray -> I420
+            "video/x-raw,format=I420 ! "
+            "nvvideoconvert compute-hw=1 ! " # Upload to GPU Memory (NVMM)
+            "video/x-raw(memory:NVMM) ! "    # Explicitly state we want NVMM
             "m.sink_0 "
         )
 
         # Source 2
         src2 = (
-            f"pylonsrc device-serial-number={CAMERA_2_SERIAL} "
-            "cam::TriggerMode=Off cam::AcquisitionFrameRateEnable=true cam::AcquisitionFrameRate=30.0 ! "
+            f"pylonsrc device-serial-number={CAMERA_2_SERIAL} {cam_settings} ! "
+            "video/x-raw,format=GRAY8 ! "
             "videoconvert ! "
+            "video/x-raw,format=I420 ! "
             "nvvideoconvert compute-hw=1 ! "
+            "video/x-raw(memory:NVMM) ! "
             "m.sink_1 "
         )
 
-        # Muxer -> Tiler -> Output
+        # Muxer -> Tiler -> Hardware Encoder
+        # nvjpegenc is the key here. It uses the dedicated JPEG block on the Jetson.
         processing = (
             f"nvstreammux name=m batch-size=2 width={STREAM_WIDTH} height={STREAM_HEIGHT} live-source=1 ! "
-            f"nvmultistreamtiler width={TILED_WIDTH} height={TILED_HEIGHT} rows=2 columns=1 ! "
-            "nvvideoconvert ! "
-            "video/x-raw, format=I420 ! "
-            "jpegenc quality=85 ! "
+            f"nvmultistreamtiler width={TILED_WIDTH} height={TILED_HEIGHT} rows=1 columns=2 ! "
+            "nvvideoconvert compute-hw=1 ! " # Ensure we are ready for encoding
+            "video/x-raw(memory:NVMM) ! "    # Keep in NVMM
+            "nvjpegenc quality=85 ! "        # HARDWARE ENCODE
             "appsink name=sink emit-signals=True sync=False max-buffers=1 drop=True"
         )
 
         pipeline_str = f"{src1} {src2} {processing}"
         
-        print(f"Launching Pipeline (GPU Mode)...")
+        print(f"Launching CUDA-Accelerated Pipeline...")
         self.pipeline = Gst.parse_launch(pipeline_str)
         
         appsink = self.pipeline.get_by_name("sink")
         appsink.connect("new-sample", self.on_new_sample)
 
-# --- Flask ---
-
+# --- Flask Routes ---
 @app.route('/')
 def index():
     return render_template_string('''
         <html><body style="background:#111; color:white; text-align:center;">
-            <h1>Basler Feed</h1>
-            <img src="{{ url_for('video_feed') }}" style="border: 2px solid green; max-width:90%;">
+            <h1>Basler CUDA Feed</h1>
+            <img src="{{ url_for('video_feed') }}" style="border: 2px solid #555; max-width:95%;">
         </body></html>
     ''')
 
@@ -114,7 +161,7 @@ def video_feed():
                 if frame_buffer:
                     yield (b'--frame\r\n'
                            b'Content-Type: image/jpeg\r\n\r\n' + frame_buffer + b'\r\n')
-            GLib.usleep(33000) 
+            GLib.usleep(25000) # ~40 FPS cap for web
 
     return Response(generate(), mimetype='multipart/x-mixed-replace; boundary=frame')