| --- |
| pretty_name: 'FishCut: Industrial Headless-Hake Cut-Line Localization' |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| task_categories: |
| - keypoint-detection |
| - object-detection |
| - image-feature-extraction |
| annotations_creators: |
| - expert-generated |
| size_categories: |
| - n<1K |
| tags: |
| - food |
| - fish |
| - hake |
| - seafood |
| - cut-line |
| - cutting-point |
| - fish-processing |
| - industrial |
| - computer-vision |
| - keypoint-detection |
| - industry-4.0 |
| - smart-manufacturing |
| - food-informatics |
| - sustainability |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: image_id |
| dtype: string |
| - name: bbox |
| list: float32 |
| - name: cut_line |
| list: |
| list: float32 |
| splits: |
| - name: train |
| num_bytes: 41004650 |
| num_examples: 370 |
| - name: validation |
| num_bytes: 27855903 |
| num_examples: 247 |
| download_size: 68866893 |
| dataset_size: 68860553 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: validation |
| path: data/validation-* |
| --- |
| |
| # 🐟 The FishCut Dataset: Cut-Line Localization for Industrial Headless-Hake Steaking |
|
|
| This dataset supports vision-based **cut-line localization** for industrial fish steaking. In a headless-hake processing line, an operator must place a reference cut on the head side of each fish: a cut placed too close to the head-side boundary risks including bone fragments, while a cut placed too far into the body wastes high-value muscle. FishCut was collected to automate this expert decision with deep learning. |
|
|
| The dataset consists of **617 expert-annotated images** of headless hake trunks, captured on an **active seafood production line** with an Intel RealSense D456 camera. This release provides the **cropped fish-ROI version**, in which each image is cropped around the fish trunk. The dataset is intended for **keypoint detection** and **coordinate-regression** approaches to cut-line localization. |
|
|
|
|
|
|
| ## 🗂️ automated cut-line localization (red = Prediction , green= Ground truth) |
|
|
|  |
|  |
|
|
| Each instance contains: |
| - **image**: an RGB image of a headless hake trunk (cropped fish ROI). |
| - **keypoints**: the reference cut-line, given as four values `[x_b, y_b, x_t, y_t]` — the bottom and top interface points of the cut-line, **normalized** to `[0, 1]` by the image width and height. |
| - **image_id**: a unique identifier for each sample. |
| |
| ## 🏷️ Annotation Format |
| |
| The reference cut-line is defined by **two keypoints** (the bottom and top of the cut), stored as four normalized coordinates `[x_b, y_b, x_t, y_t]`. The ground-truth cut-lines were defined by **three expert operators** with extensive experience in manual hake steaking, who labelled each image **jointly by consensus**, producing a single mutually agreed annotation per image. |
| |
| > Note: coordinates are normalized to the dimensions of each (cropped) image, so multiply by the image width/height to recover pixel positions. |
| |
| ## 🧪 How to read and display examples |
| |
| ```python |
| from datasets import load_dataset |
| from PIL import Image, ImageDraw |
| |
| # --- Configuration --- |
| DATASET_NAME = "salahkhenfer/FishCut" |
| SAMPLE_INDEX = 0 |
| OUTPUT_IMAGE = "annotated_FishCut.png" |
| |
| if __name__ == "__main__": |
| dataset = load_dataset(DATASET_NAME) |
| split = "train" if "train" in dataset else list(dataset.keys())[0] |
| sample = dataset[split][SAMPLE_INDEX] |
| |
| image = sample["image"].convert("RGB") |
| width, height = image.size |
| draw = ImageDraw.Draw(image) |
| |
| # keypoints = [x_b, y_b, x_t, y_t], normalized to [0, 1] |
| xb, yb, xt, yt = sample["keypoints"] |
| p_bottom = (xb * width, yb * height) |
| p_top = (xt * width, yt * height) |
| |
| # draw the reference cut-line in green |
| draw.line([p_bottom, p_top], fill=(0, 255, 0), width=3) |
| |
| image.save(OUTPUT_IMAGE) |
| print(f"Annotated image saved as {OUTPUT_IMAGE}") |
| ``` |
| |
| |
| ## 🤝 Partners |
| <div style="display: flex; flex-wrap: wrap; justify-content: center; gap: 40px; align-items: center;"> |
| <a href="https://www.fbk.eu/en" target="_blank"><img src="FBK.jpg" width="250" alt="FBK logo"></a> |
| <a href="https://www.produmar.com/en/" target="_blank"><img src="produmar.png" width="250" alt="Produmar logo"></a> |
| </div> |
| |
| ## 📖 Citation |
| |
| ```bibtex |
| @article{khenfer2026FishCut, |
| title = {Artificial Intelligence and Computer Vision for Automated Cut-Line Localization in Industrial Fish Processing}, |
| author = {Salah E.Khenfer, Mohamed L.Mekhalfi, Mingdeng Shi,Hua Zou}, |
| journal = {Future Foods}, |
| year = {2026} |
| } |
| ``` |
| |
