README.md

---
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)

![FishCut Example 1](https://huggingface.co/datasets/salahkhenfer/FishCut/resolve/main/Austral_rgb_122.png)
![FishCut Example 2](https://huggingface.co/datasets/salahkhenfer/FishCut/resolve/main/Austral_rgb_168.png)

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}
}
```