diff --git a/apps/web/public/models/abacus-column-classifier/group1-shard1of1.bin b/apps/web/public/models/abacus-column-classifier/group1-shard1of1.bin
new file mode 100644
index 00000000..170934b2
Binary files /dev/null and b/apps/web/public/models/abacus-column-classifier/group1-shard1of1.bin differ
diff --git a/apps/web/public/models/abacus-column-classifier/model.json b/apps/web/public/models/abacus-column-classifier/model.json
new file mode 100644
index 00000000..a84789c2
--- /dev/null
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diff --git a/apps/web/public/models/abacus-column-classifier/model.json.backup b/apps/web/public/models/abacus-column-classifier/model.json.backup
new file mode 100644
index 00000000..fec91edf
--- /dev/null
+++ b/apps/web/public/models/abacus-column-classifier/model.json.backup
@@ -0,0 +1,858 @@
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+          "name": "batch_normalization_2/moving_variance",
+          "shape": [128],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": 0.01432291604578495,
+            "scale": 0.00944612571860061,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "batch_normalization_3/gamma",
+          "shape": [128],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": 0.9765098690986633,
+            "scale": 0.0008689317048764697,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "batch_normalization_3/beta",
+          "shape": [128],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.05253423078387391,
+            "scale": 0.00032833894239921196,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "batch_normalization_3/moving_mean",
+          "shape": [128],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": 2.3402893845059225e-8,
+            "scale": 0.124165194550534,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "batch_normalization_3/moving_variance",
+          "shape": [128],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": 0.000532600621227175,
+            "scale": 0.8092722632006888,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "conv2d/kernel",
+          "shape": [3, 3, 1, 32],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.1684967933916578,
+            "scale": 0.0012961291799358293,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "conv2d/bias",
+          "shape": [32],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.014791351323034248,
+            "scale": 0.00019462304372413485,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "conv2d_1/kernel",
+          "shape": [3, 3, 32, 64],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.14185832411635155,
+            "scale": 0.0010912178778180888,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "conv2d_1/bias",
+          "shape": [64],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.052345379924072934,
+            "scale": 0.00033341006321065564,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "conv2d_2/kernel",
+          "shape": [3, 3, 64, 128],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.09215074052997664,
+            "scale": 0.0007199276603904425,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "conv2d_2/bias",
+          "shape": [128],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.052666782806901374,
+            "scale": 0.00035346834098591524,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "dense/kernel",
+          "shape": [16384, 128],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.1078803108311167,
+            "scale": 0.0006960020053620432,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "dense/bias",
+          "shape": [128],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.010696043731535184,
+            "scale": 0.00013539295862702763,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "dense_1/kernel",
+          "shape": [128, 10],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.26071277062098186,
+            "scale": 0.002190863618663713,
+            "original_dtype": "float32"
+          }
+        },
+        {
+          "name": "dense_1/bias",
+          "shape": [10],
+          "dtype": "float32",
+          "quantization": {
+            "dtype": "uint8",
+            "min": -0.020677046455881174,
+            "scale": 0.00016028718182853623,
+            "original_dtype": "float32"
+          }
+        }
+      ]
+    }
+  ]
+}
diff --git a/apps/web/src/hooks/useAbacusVision.ts b/apps/web/src/hooks/useAbacusVision.ts
index 4080ef9f..5a7ee592 100644
--- a/apps/web/src/hooks/useAbacusVision.ts
+++ b/apps/web/src/hooks/useAbacusVision.ts
@@ -83,6 +83,10 @@ export function useAbacusVision(options: UseAbacusVisionOptions = {}): UseAbacus
   // Track previous stable value to avoid duplicate callbacks
   const lastStableValueRef = useRef<number | null>(null)
 
+  // Throttle inference to 5fps for performance
+  const lastInferenceTimeRef = useRef<number>(0)
+  const INFERENCE_INTERVAL_MS = 200 // 5fps
+
   // Ref for calibration functions to avoid infinite loop in auto-calibration effect
   const calibrationRef = useRef(calibration)
   calibrationRef.current = calibration
@@ -274,6 +278,13 @@ export function useAbacusVision(options: UseAbacusVisionOptions = {}): UseAbacus
    * Process a video frame for detection using TensorFlow.js classifier
    */
   const processFrame = useCallback(async () => {
+    // Throttle inference for performance (5fps instead of 60fps)
+    const now = performance.now()
+    if (now - lastInferenceTimeRef.current < INFERENCE_INTERVAL_MS) {
+      return
+    }
+    lastInferenceTimeRef.current = now
+
     // Get video element from camera stream
     const videoElements = document.querySelectorAll('video')
     let video: HTMLVideoElement | null = null
@@ -292,12 +303,10 @@ export function useAbacusVision(options: UseAbacusVisionOptions = {}): UseAbacus
 
     // Process video frame into column strips
     const columnImages = processVideoFrame(video, calibration.calibration)
-
     if (columnImages.length === 0) return
 
     // Run classification
     const result = await classifier.classifyColumns(columnImages)
-
     if (!result) return
 
     // Update column confidences
diff --git a/apps/web/src/hooks/useColumnClassifier.ts b/apps/web/src/hooks/useColumnClassifier.ts
index eb193007..4ec1d619 100644
--- a/apps/web/src/hooks/useColumnClassifier.ts
+++ b/apps/web/src/hooks/useColumnClassifier.ts
@@ -86,7 +86,6 @@ export function useColumnClassifier(): UseColumnClassifierReturn {
         setIsModelLoaded(true)
         return true
       } else {
-        // Model doesn't exist - not an error, just unavailable
         setIsModelUnavailable(true)
         return false
       }
diff --git a/apps/web/src/types/vision.ts b/apps/web/src/types/vision.ts
index e2846096..93e2a509 100644
--- a/apps/web/src/types/vision.ts
+++ b/apps/web/src/types/vision.ts
@@ -187,8 +187,8 @@ export interface FrameStabilityConfig {
  * Default stability configuration
  */
 export const DEFAULT_STABILITY_CONFIG: FrameStabilityConfig = {
-  minConsecutiveFrames: 10, // ~300ms at 30fps
-  minConfidence: 0.7,
+  minConsecutiveFrames: 3, // 600ms at 5fps inference rate
+  minConfidence: 0.5, // Lower threshold - model confidence is often 60-80%
   handMotionThreshold: 0.3,
 }