diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..c186276d8dfb70e0e2e4e19695031245d2cca925 100644 --- a/.gitattributes +++ b/.gitattributes @@ -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 +sampleData/08-17-22-0011-00/08-17-22-0011-00.avi filter=lfs diff=lfs merge=lfs -text +sampleData/08-17-22-0012-00/08-17-22-0012-00.avi filter=lfs diff=lfs merge=lfs -text +sampleData/08-17-22-0013-00/08-17-22-0013-00.avi filter=lfs diff=lfs merge=lfs -text +sampleData/08-17-22-0014-00/08-17-22-0014-00.avi filter=lfs diff=lfs merge=lfs -text +sampleData/08-17-22-0015-00/08-17-22-0015-00.avi filter=lfs diff=lfs merge=lfs -text +sampleData/08-17-22-0016-00/08-17-22-0016-00.avi filter=lfs diff=lfs merge=lfs -text +sampleData/08-17-22-0017-00/08-17-22-0017-00.avi filter=lfs diff=lfs merge=lfs -text +sampleData/08-17-22-0018-00/08-17-22-0018-00.avi filter=lfs diff=lfs merge=lfs -text +sampleData/08-17-22-0019-00/08-17-22-0019-00.avi filter=lfs diff=lfs merge=lfs -text +sampleData/08-18-22-0020-00/08-18-22-0020-00.avi filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..3db496467be6fff8a9f840f0e669516d49f16616 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +defect-weld/* +good_weld/* +test_data/* \ No newline at end of file diff --git a/Hackathon Agenda.pdf b/Hackathon Agenda.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ae66d9209abbd90a89c9c355f35338f391b9776a Binary files /dev/null and b/Hackathon Agenda.pdf differ diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..734b3846889cf8b4b2e7bd091c1e59a7ce4110c2 --- /dev/null +++ b/README.md @@ -0,0 +1,280 @@ +# 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/ + / + / + .csv + .flac + .avi + images/*.jpg + +defect_data_weld/ + / + / + .csv + .flac + .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 diff --git a/sampleData/08-17-22-0011-00/08-17-22-0011-00.avi b/sampleData/08-17-22-0011-00/08-17-22-0011-00.avi new file mode 100644 index 0000000000000000000000000000000000000000..bdc9ec8f3bdd7b62cf4c7095a5ff51187b53c2b2 --- /dev/null +++ b/sampleData/08-17-22-0011-00/08-17-22-0011-00.avi @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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