Datasets:

jjldo21
/

IndustrialLateralLoads

dataset_info:
  features:
    - name: image
      dtype: image
    - name: mask
      dtype: image
    - name: class_id
      dtype:
        class_label:
          names:
            '0': verde_plastico
            '1': azul
            '2': negro_plastico
            '3': negro_carton
            '4': roja_plastico
            '5': carton
            '6': verde_carton
            '7': roja_eldulze
            '8': verde_plastico_oscuro
            '9': verde_cogollo
            '10': ilfres
    - name: bbox
      sequence: float64
    - name: mask_rgb_color
      sequence: int64
  splits:
    - name: loads
      num_bytes: 303854470
      num_examples: 949
  download_size: 292816441
  dataset_size: 303854470
configs:
  - config_name: default
    data_files:
      - split: loads
        path: data/loads-*
license: mit
task_categories:
  - object-detection
  - image-segmentation
size_categories:
  - n<1K
tags:
  - industry

The IndustrialLateralLoads dataset is designed for object detection and instance segmentation tasks in industrial environments. It contains images of palletized loads with their corresponding annotations. The dataset is available in two formats:

Hugging Face dataset (Parquet): Ready-to-use format with images, masks, and metadata.
Raw files: Original folders accessible in the repository files.

Hugging Face dataset features:

When loaded using the datasets library, each sample contains the following fields:

image: A PIL.Image object containing the original RGB image.
mask: A PIL.Image object containing the binary segmentation mask.
class_id: The classification label of the load (integer mapped to class name).
bbox: The bounding box coordinates in format [x_min, y_min, width, height].
mask_rgb_color: An [R, G, B] list specifying the RGB triplet associated with the class in the binary mask.

Raw data organization (repository files):

If you prefer to download the raw files, the repository includes the following folders:

images: Contains the source images.
masks: Contains the binary PNG segmentation masks.
bboxes: Contains .txt files with bounding box annotations.

Remark 1. Each row in a .txt file (inside the bboxes folder) follows this format:

<class_name> <instance_id> <x_min> <y_min> <width> <height>,

where the <instance_id> field is not relevant in the currently published dataset.

Remark 2. The segmentation masks are provided as binary RGB images. They contain only two distinct pixel values:

Background: Black (0, 0, 0).

Foreground: The specific RGB Color associated with the class ID listed below.

Class ID	Class Name	Foreground RGB Color
0	verde_plastico	`(51, 221, 255)`
1	azul	`(255, 204, 51)`
2	negro_plastico	`(52, 209, 183)`
3	negro_carton	`(255, 96, 55)`
4	roja_plastico	`(36, 179, 83)`
5	carton	`(255, 125, 187)`
6	verde_carton	`(221, 255, 51)`
7	roja_eldulze	`(170, 240, 209)`
8	verde_plastico_oscuro	`(255, 10, 124)`
9	verde_cogollo	`(184, 61, 245)`
10	ilfres	`(116, 113, 206)`

Scientific validation & ongoing research:

This dataset serves as a benchmark for validating computer vision approaches in industrial logistics. It remains an active resource for ongoing research, with current efforts shifting towards instance segmentation methodologies in industrial environments.

Associated publications:

The dataset has been employed to validate the approaches presented in the following scientific studies:

Fog Computing-Driven Logistics: Leveraging Few-Shot Learning and Foundational Computer Vision Models
Enhancing Logistics with Computer Vision and Fog Computing-Driven Auto-ID Technologies
From Few-Shot to Zero-Shot Pallet Load Recognition: A Deployed Embedding-Based Vision System for Industrial Logistics
- Accepted at WACV 2026 (Winter Conference on Applications of Computer Vision)
Training-Free One-Shot Industrial Load Segmentation for Real-Time Logistics Operations
- Under Review

Install Hugging Face datasets package:

pip install datasets

Usage example:

from datasets import load_dataset

# Load the dataset (images and masks are loaded as PIL objects)
dataset = load_dataset("jjldo21/IndustrialLateralLoads")

# Example: Accessing the first sample
sample = dataset['loads'][0]
print(sample['class_id'])       # Class label (e.g., 9 corresponding to 'verde_cogollo')
print(sample['mask_rgb_color']) # Color of the mask (e.g., [184, 61, 245])
sample['image'].show()          # Display image
sample['mask'].show()           # Display mask