# Task 319 — ONNX Build Guide

## Rule (267/267 VERIFIED)

**CRITICAL**: Object binary = `(inp == color)` in bbox, NOT `(inp != bg)`!

### Algorithm (2-pass, template = largest then second-largest):

```
1. bg = channel with most pixels (argmax of per-channel sums)
2. Sort non-bg channels by pixel count → [ch1_largest, ch2, ch3]
3. For template_channel in [ch1_largest, ch2]:
   a. Find largest uniform block (bs_r, bs_c) in template's binary
   b. Downsample template by block → pattern (ph × pw)
   c. For each other channel (candidate):
      - Cross-correlate pattern with candidate binary at all positions
      - If max correlation == sum(pattern) → MATCH FOUND
      - Output = candidate channel's binary (one-hot encoded)
   d. If no match with uniform block, try row/col grouping:
      - Group consecutive identical rows, group consecutive identical cols
      - Take first row/col of each group → downsampled pattern
      - Cross-correlate with candidates
4. Output = matched candidate in one-hot format
```

### Statistics:
- Largest template + block method: 248/267
- + row/col grouping: 250/267  
- + second-largest template: 267/267 (100%!)

### ONNX Implementation Notes:

**Input**: [1, 10, 30, 30] — channels ARE the per-color masks

**Key Operations Needed**:
1. Per-channel pixel sum → ReduceSum [10] → find bg (ArgMax) and rank others
2. For template channel: try block sizes (2,2), (2,3), (3,2), (3,3), (2,4), (4,2), (4,3), (3,4), (4,4), (5,5)
3. Block uniformity check: reshape to [h/bs_r, bs_r, w/bs_c, bs_c], check min==max per block
4. Cross-correlation: Conv2D with downsampled pattern as kernel (or MatMul approach)
5. Match detection: max(correlation) == sum(pattern)
6. Output construction: select matched channel

**Memory Estimate**:
- Correlation maps: ~[30,30] per block_size × ~10 sizes = 36KB
- Template/candidate masks: [30,30] × 3 = 11KB
- Auxiliaries: ~50KB
- **Total: ~100-200KB → score 12-13 pts → gain +4-5**

**Block Size Enumeration** (covers 248/267):
- Valid (bs_r, bs_c) where bs divides object dims (max 10×10)
- Try: (2,2), (2,1), (1,2), (3,3), (3,1), (1,3), (2,3), (3,2), (4,2), (2,4), (5,5), (4,4), (5,2), (2,5)

**Row/Col Grouping in ONNX** (covers remaining 2 from 1st template):
- Row boundary: ReduceSum(abs(mask[r] - mask[r-1])) > 0
- Take rows AT boundaries → downsampled rows
- Similarly for cols

**Second Template Pass** (covers final 17):
- Same algorithm but on second-largest channel
- If first template found a match, skip this pass

### Files
- `task319_solver_267.py` — Complete Python solver (267/267)
- `build_task255_onnx.py` — Reference for ONNX building patterns