| --- |
| annotations_creators: |
| - expert-generated |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - object-detection |
| pretty_name: SawitMVC |
| tags: |
| - oil-palm |
| - agriculture |
| - yolo |
| - multi-view |
| - bunch-counting |
| - maturity-classification |
| - palm-oil |
| - computer-vision |
| - deduplication |
| - counting |
| --- |
| |
| # SawitMVC |
|
|
| SawitMVC is a multi-view oil palm fruit bunch detection and counting dataset. It contains expert-reviewed YOLO annotations and per-tree JSON ground truth for counting unique fruit bunches across 4-8 camera views. |
|
|
| ## Dataset Summary |
|
|
| | Property | Value | |
| |---|---| |
| | Trees | **953** (DAMIMAS: 854, LONSUM: 99) | |
| | Images | **3,992** (960 x 1280 px, JPEG) | |
| | Views per tree | 4 sides (45 trees have 8 sides) | |
| | Annotation format | YOLO v8 labels + JSON ground truth | |
| | Classes | 4 maturity levels (B1-B4) | |
| | Unique bunches (GT) | 9,823 | |
|
|
| ## Split |
|
|
| | Split | Trees | Images | |
| |---|---|---| |
| | train | 716 | 3,000 | |
| | val | 96 | 404 | |
| | test | 141 | 588 | |
|
|
| Split assigned at **tree level** — all sides (4 or 8) of a tree go to the same split. Stratified by variety × dominant maturity class × view-count (seed=42, ratio 75/10/15). |
|
|
| ## Dataset Statistics |
|
|
| ### Class Distribution (unique bunches) |
|
|
| | Class | Count | % | Description | |
| |:---:|---:|---:|---| |
| | B1 | 954 | 9.7% | Ripe — least frequent | |
| | B2 | 1,791 | 18.2% | Transitioning | |
| | B3 | 5,067 | 51.6% | Unripe — dominant class | |
| | B4 | 2,011 | 20.5% | Very unripe | |
| | **Total** | **9,823** | 100% | Unique bunches (GT) | |
|
|
| Raw detections across all views: **18,540** → duplication ratio **1.887×** (same bunch seen from multiple sides). |
|
|
| ### Per-Split Class Distribution |
|
|
| | Split | Trees | B1 | B2 | B3 | B4 | Total bunches | |
| |---|---:|---:|---:|---:|---:|---:| |
| | train | 716 | 739 | 1,337 | 3,818 | 1,533 | 7,427 | |
| | val | 96 | 95 | 198 | 504 | 195 | 992 | |
| | test | 141 | 120 | 256 | 745 | 283 | 1,404 | |
|
|
| ### Bunches per Tree |
|
|
| | Metric | Value | |
| |---|---| |
| | Min | 0 | |
| | Median | 10 | |
| | Mean | 10.31 | |
| | Max | 22 | |
| | Std dev | 3.74 | |
|
|
| ### Bunch Appearance Distribution |
|
|
| How many camera sides each unique bunch is visible from: |
|
|
| | Visible from N sides | Bunches | % | |
| |:---:|---:|---:| |
| | 1 | 2,495 | 25.4% | |
| | 2 | 6,264 | 63.8% | |
| | 3 | 834 | 8.5% | |
| | 4 | 147 | 1.5% | |
| | 5 | 71 | 0.7% | |
| | 6 | 12 | 0.1% | |
|
|
| Most bunches (63.8%) appear in exactly 2 adjacent sides, consistent with the camera capture geometry. |
|
|
| ## Tasks |
|
|
| 1. **Object detection**: detect and classify oil palm fruit bunches in each image. |
| 2. **Multi-view counting**: use JSON ground truth to count each physical bunch once even when it appears in multiple camera views. |
|
|
| ## Maturity Classes |
|
|
| | Class ID | Label | Stage | Description | |
| |:---:|:---:|---|---| |
| | 0 | **B1** | Ripe | Red, large, round; optimal harvest stage | |
| | 1 | **B2** | Transitioning | Dark fruit transitioning to red | |
| | 2 | **B3** | Unripe | Black, spiny, elongated | |
| | 3 | **B4** | Very unripe | Small, deeply positioned, black to green | |
|
|
| Biological order: **B1 -> B2 -> B3 -> B4** from most ripe to least ripe. |
|
|
| ## Sample Visualization |
|
|
| Each color represents one unique bunch. The same color across panels means the same physical bunch appears from multiple sides. |
|
|
| **4-view tree:** |
|  |
|
|
| **8-view tree:** |
|  |
|
|
| ## Dataset Structure |
|
|
| ```text |
| SawitMVC/ |
| |-- images/ # 3,992 images, flat structure |
| |-- labels/ # 3,992 YOLO .txt files, flat structure |
| |-- json/ # 953 JSON ground-truth files, one per tree |
| |-- data/ |
| | `-- ground_truth.parquet # Per-tree ground-truth summary |
| |-- data.yaml # YOLO dataset config |
| |-- split_manifest.csv # Tree-level split and stratification metadata |
| `-- croissant.json # ML Croissant metadata |
| ``` |
|
|
| ## File Naming |
|
|
| ```text |
| DAMIMAS_A21B_0001_1.jpg -> variety=DAMIMAS, code=A21B, tree=0001, side=1 |
| DAMIMAS_A21B_0001_1.txt -> YOLO label for the same image |
| DAMIMAS_A21B_0001.json -> ground truth for all views of tree 0001 |
| ``` |
|
|
| ## YOLO Label Format |
|
|
| ```text |
| # class_id cx_norm cy_norm w_norm h_norm |
| 2 0.660417 0.408203 0.056250 0.041406 |
| 1 0.622396 0.443750 0.098958 0.087500 |
| ``` |
|
|
| Coordinates are normalized to `[0, 1]` relative to a 960 x 1280 image. Class IDs are `0=B1`, `1=B2`, `2=B3`, `3=B4`. |
|
|
| ## JSON Ground Truth Format |
|
|
| ```json |
| { |
| "version": 4, |
| "tree_id": "DAMIMAS_A21B_0001", |
| "split": "train", |
| "metadata": { |
| "date": "2026-05-16", |
| "variety": "DAMIMAS" |
| }, |
| "images": { |
| "side_1": { |
| "filename": "DAMIMAS_A21B_0001_1.jpg", |
| "side_index": 0, |
| "side_label": "Side 1", |
| "bbox_count": 5, |
| "annotations": [ |
| { |
| "box_index": 0, |
| "class_id": 2, |
| "class_name": "B3", |
| "bbox_yolo": [0.660417, 0.408203, 0.05625, 0.041406] |
| } |
| ] |
| } |
| }, |
| "bunches": [ |
| { |
| "bunch_id": 1, |
| "class": "B3", |
| "appearance_count": 2, |
| "appearances": [ |
| {"side": "side_1", "side_index": 0, "box_index": 0}, |
| {"side": "side_2", "side_index": 1, "box_index": 2} |
| ] |
| } |
| ], |
| "summary": { |
| "total_unique_bunches": 8, |
| "total_detections": 17, |
| "duplicates_linked": 9, |
| "by_class": {"B1": 1, "B2": 2, "B3": 5, "B4": 0}, |
| "by_side": {"side_1": 5, "side_2": 4, "side_3": 4, "side_4": 4} |
| } |
| } |
| ``` |
|
|
| `summary.by_class` is the ground truth for counting evaluation. `_confirmedLinks` stores annotator-confirmed cross-view links using numeric `sideA`, `sideB`, `bboxIdA`, and `bboxIdB` references. |
|
|
| ## Parquet Ground Truth |
|
|
| `data/ground_truth.parquet` contains one row per tree. |
|
|
| Columns: `tree_id, split, variety, num_sides, total_unique_bunches, B1, B2, B3, B4, total_detections, duplicates_linked` |
|
|
| Example query: |
|
|
| ```sql |
| SELECT variety, AVG(total_unique_bunches) AS avg_bunches, |
| SUM(B1) AS total_B1, SUM(B2) AS total_B2, |
| SUM(B3) AS total_B3, SUM(B4) AS total_B4 |
| FROM ground_truth |
| GROUP BY variety; |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("ULM-DS-Lab/SawitMVC", data_dir="images") |
| gt = load_dataset("ULM-DS-Lab/SawitMVC", data_files="data/ground_truth.parquet") |
| ``` |
|
|
| ```python |
| import json |
| from pathlib import Path |
| |
| tree = json.loads(Path("json/DAMIMAS_A21B_0001.json").read_text(encoding="utf-8-sig")) |
| gt = tree["summary"]["by_class"] |
| total = tree["summary"]["total_unique_bunches"] |
| ``` |
|
|
| ## YOLO Training |
|
|
| ```bash |
| yolo detect train data=data.yaml model=yolov8n.pt epochs=100 imgsz=960 |
| ``` |
|
|
| ## Dataset Collection |
|
|
| - **Source:** Field surveys at DAMIMAS and LONSUM palm oil plantations in Indonesia |
| - **Capture:** Smartphone cameras, 4-8 positions per tree |
| - **Annotation:** Expert agronomists using multi-view cross-referencing |
| - **Resolution:** 960 x 1280 pixels |
| - **Date:** February 2026 |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{ulm_sawitmvc_2026, |
| title = {SawitMVC}, |
| author = {Fatma Indriani and Setyo Wahyu Saputro and Muhammad Zainal Muttaqin and Alia Rahmi and Triando Hamonangan Saragih and Rahmat Budianoor and Hartoni and Dwi Kartini and Naufal Said}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/ULM-DS-Lab/SawitMVC} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. |
|
|
| You may share and adapt the dataset for non-commercial purposes with appropriate attribution. Commercial use is not permitted. |
|
|
| [](https://creativecommons.org/licenses/by-nc/4.0/) |
|
|
|
|
