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@@ -58,3 +58,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+defect-weld/*
+good_weld/*
+test_data/*

README.md

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+# Dataset Description
+
+## Overview
+
+- The dataset is organized into three roots:
+- `good_weld`: labeled non-defect reference runs (`750` runs in `43` configuration folders).
+- `defect_data_weld`: labeled defect runs (`1580` runs in `80` configuration folders).
+- `test_data`: anonymized evaluation set (`90` samples named `sample_0001` ... `sample_0090`).
+- Training-available labeled pool (`good_weld` + `defect_data_weld`) contains `2330` runs.
+- Each run/sample is multimodal and typically includes:
+- one sensor CSV, one FLAC audio file, one AVI video file
+
+## Weld Configuration Metadata
+
+- Weld setup/context is encoded in labeled folder names (for example: joint type such as `butt` or `plane_plate`, material tags such as `Fe410` or `BSK46`, and date-like tokens).
+- Run IDs follow a pattern like `04-03-23-0010-11`, where the final two-digit suffix (`11`) is the defect/quality code.
+- For fair hackathon evaluation, `test_data` has anonymized folder names (`sample_XXXX`) and neutral filenames (`sensor.csv`, `weld.flac`, `weld.avi`) to reduce label leakage.
+- Ground-truth linkage for evaluation is kept separately in `test_data_ground_truth.csv`.
+
+## Labels and Defect Definitions
+
+| Code | Label | Practical definition | Train pool count (`good_weld`+`defect_data_weld`) |
+|---|---|---|---:|
+| `00` | `good_weld` | Acceptable weld (no target defect) | 750 |
+| `01` | `excessive_penetration` | Over-penetration through joint root | 479 |
+| `02` | `burn_through` | Severe over-penetration causing burn-through/hole | 317 |
+| `06` | `overlap` | Weld metal overlap without proper fusion at toe/root region | 155 |
+| `07` | `lack_of_fusion` | Incomplete fusion between weld metal and base material | 320 |
+| `08` | `excessive_convexity` | Excessively convex weld bead profile | 159 |
+| `11` | `crater_cracks` | Cracks near weld crater/termination zone | 150 |
+
+## Folder Structure
+
+```text
+good_weld/
+  <configuration_folder>/
+    <run_id>/
+      <run_id>.csv
+      <run_id>.flac
+      <run_id>.avi
+      images/*.jpg
+
+defect_data_weld/
+  <defect_configuration_folder>/
+    <run_id>/
+      <run_id>.csv
+      <run_id>.flac
+      <run_id>.avi
+      images/*.jpg
+
+test_data/
+  sample_0001/
+    sensor.csv
+    weld.flac
+    weld.avi
+    images/*.jpg
+  ...
+  sample_0090/
+```
+
+- Evaluation helper files:
+- `test_data_manifest.csv`: file paths for each anonymized sample.
+- `test_data_ground_truth.csv`: mapping from `sample_id` to true label (for evaluator use).
+
+## Sensor Features (Number of Features)
+
+- Original labeled CSV schema has `10` columns:
+- `Date`, `Time`, `Part No`, `Pressure`, `CO2 Weld Flow`, `Feed`, `Primary Weld Current`, `Wire Consumed`, `Secondary Weld Voltage`, `Remarks`.
+- Core process/sensor channels used for modeling are typically `6` numeric features:
+- `Pressure`, `CO2 Weld Flow`, `Feed`, `Primary Weld Current`, `Wire Consumed`, `Secondary Weld Voltage`.
+- `test_data/sensor.csv` intentionally removes `Part No` to prevent ID leakage, so evaluation CSVs have `9` columns.
+
+## Concise and Informative Guideline
+
+1. Use `good_weld` and `defect_data_weld` only for training and validation.
+2. Split by run/group (not random rows) to avoid leakage across near-duplicate temporal segments.
+3. Treat each run/sample as multimodal (`sensor.csv` + audio + video + images), then build binary (`defect` vs `good`) and multi-class (`defect type`) models.
+4. Run final inference only on anonymized `test_data` and export predictions keyed by `sample_id`.
+5. Keep `test_data_ground_truth.csv` strictly for organizer-side scoring or final offline evaluation after predictions are frozen.
+6. Report both performance and confidence quality (for example: F1, Macro-F1, calibration/ECE) and include failure-case examples.
+
+## Phase 1: Data preparation + dataset + dashboard + overall analysis + feature engineering
+
+**Goal:** produce a clean, reproducible dataset + an analysis dashboard that explains what’s in the data and what signals might matter.
+
+**What they must do**
+
+* **Ingest the dataset**
+
+  * Load video + audio + labels/metadata.
+  * Validate files: missing, corrupt, mismatched IDs, inconsistent durations.
+* **Define the unit of prediction**
+
+  * Decide what one “sample” means (whole weld, fixed-length segment, windowed chunks, etc.).
+  * Ensure labels align to that unit.
+* **Create a reproducible split**
+
+  * Train/validation/test split that avoids leakage (split by session/part/run if applicable).
+  * Save split files so results are repeatable.
+* **Preprocess and standardize**
+
+  * Make audio/video consistent (sampling rate/FPS, resizing, normalization, trimming/padding policy).
+  * Handle variable length (padding, cropping, pooling, sliding windows).
+* **Feature engineering (optional, but if used it must be documented)**
+
+  * Produce derived signals/features from audio/video/metadata (any representation is fine).
+  * Keep a clear mapping from raw inputs → engineered inputs.
+* **Dashboard (must show)**
+
+  * Dataset overview: counts, durations, missing/corrupt stats.
+  * Label distributions: defect vs non-defect, defect-type counts.
+  * Representative examples: video preview + audio preview (waveform/spectrogram or equivalent).
+  * Basic data quality indicators: class imbalance, outliers, noise, sync issues (if relevant).
+  * Exportable reports: ability to save plots/tables or generate a summary.
+
+**Phase 1 output package**
+
+* `dataset/` or loader pipeline that can recreate the dataset
+* split definition files
+* dashboard app/notebook
+* short “data card” summary (1 page) describing assumptions and preprocessing choices
+
+---
+
+## Phase 2: Defect detection (binary classification) with confidence
+
+**Goal:** build a model that outputs **defect vs non-defect** plus a **confidence score** for each prediction.
+
+**What they must do**
+
+* **Train a binary classifier**
+
+  * Input: audio/video (and any engineered features) per sample.
+  * Output: probability/score for “defect”.
+* **Produce confidence**
+
+  * Define what confidence means (typically a calibrated probability).
+  * Confidence must be reported per prediction.
+* **Set a decision rule**
+
+  * Thresholding policy to convert score → defect/non-defect.
+  * Threshold must be fixed for test-time scoring (not adjusted after seeing test labels).
+* **Evaluate on validation**
+
+  * Report core binary metrics (listed below).
+  * Show error breakdown (false positives/false negatives) and examples.
+* **Create an inference pipeline**
+
+  * Script that takes the test split and writes predictions in the required format.
+
+**Phase 2 output package**
+
+* trained model checkpoint(s)
+* inference script (one command run)
+* `predictions_binary.csv` (or combined file) with:
+
+  * `sample_id`, `p_defect`, `pred_defect`, `confidence`
+* evaluation report/plots in the dashboard
+
+---
+
+## Phase 3: Defect type classification (multi-class)
+
+**Goal:** if a weld is defective, predict **which defect type**, with confidence.
+
+**What they must do**
+
+* **Train a defect-type classifier**
+
+  * Input: same sample representation.
+  * Output: defect type probabilities (or scores).
+* **Define handling of non-defect samples**
+
+  * Either:
+
+    * classify defect type **only when defect is predicted/known**, OR
+    * include “none” as a class.
+  * Whichever they choose, it must match the evaluation spec and be consistent.
+* **Report confidence for type**
+
+  * Provide a confidence score for the chosen defect type (top-1 probability or calibrated).
+* **Evaluate**
+
+  * Report multi-class metrics (listed below), especially per-class results due to imbalance.
+* **Integrate with Phase 2**
+
+  * Final output should be coherent: non-defect → type is “none”; defect → type predicted.
+
+**Phase 3 output package**
+
+* model checkpoint(s)
+* inference script producing:
+
+  * `pred_defect_type`, `p_type_*` (optional), `type_confidence`
+* evaluation report (per-type performance)
+
+---
+
+# Evaluation criteria
+
+## A) Model metrics (primary)
+
+### Submission CSV (required)
+
+Teams must submit one CSV file with this exact schema:
+
+```csv
+sample_id,pred_label_code,p_defect
+sample_0001,11,0.94
+sample_0002,00,0.08
+...
+sample_0090,06,0.81
+```
+
+Submission rules:
+
+* Exactly `90` rows (one row per sample in `test_data_manifest.csv`)
+* `sample_id` must match exactly (`sample_0001` ... `sample_0090`), with no duplicates
+* `pred_label_code` must be one of: `00`, `01`, `02`, `06`, `07`, `08`, `11`
+* `p_defect` must be numeric in `[0,1]`
+
+Scoring interpretation:
+
+* Binary prediction is derived as: `pred_defect = (pred_label_code != "00")`
+* Type prediction is the submitted `pred_label_code`
+
+### 1) Defect vs non-defect (binary)
+
+Use these as the core:
+
+* **F1 (Defect as positive class)**
+* **Precision / Recall (Defect)**
+* **ROC-AUC**
+* **PR-AUC**
+* **Confusion matrix counts** (TP/FP/FN/TN)
+
+### 2) Defect type (multi-class)
+
+Use these:
+
+* **Macro F1** (treats each defect type equally, good for imbalance)
+* **Per-class Precision/Recall/F1**
+* **Weighted F1** (secondary)
+
+### 3) Confidence quality (recommended to include)
+
+Because “confidence” that’s just vibes is worthless:
+
+* **Calibration metric**: **ECE (Expected Calibration Error)** (binary at minimum)
+
+**single final score:**
+
+* `FinalScore = 0.6 * Binary_F1 + 0.4 * Type_MacroF1`
+
+---
+
+## B) Engineering & product quality (secondary)
+
+### UI / Dashboard (clean and usable)
+
+* Clear navigation, readable plots/tables, consistent labels
+* Shows the required dataset stats + evaluation views
+* Fast enough to use during a demo (no 5-minute refreshes)
+
+### Clean code & reproducibility
+
+* One-command run for training/inference
+* Clear folder structure, requirements/environment file
+* No hardcoded paths, no mystery constants without comments
+* Reproducible splits + fixed random seeds (where relevant)
+
+### Presentation & explanation
+
+* Clear statement of:
+
+  * sample definition
+  * preprocessing assumptions
+  * model outputs and how confidence is computed
+  * strengths/weaknesses and common failure cases
+* Demo includes: dashboard + a few correctly/incorrectly predicted examples

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