Datasets:

proppos
/

waste-benchmark

	---
	license: other
	dataset_info:
	features:
	- name: before_image
	dtype: image
	- name: after_image
	dtype: image
	- name: before_filename
	dtype: string
	- name: after_filename
	dtype: string
	- name: before_hash
	dtype: string
	- name: after_hash
	dtype: string
	- name: before_mask
	dtype: string
	- name: after_mask
	dtype: string
	- name: total_waste_ratio
	dtype: float32
	- name: total_waste_ratio_gt
	dtype: float32
	- name: main_dish_waste_ratio
	dtype: float32
	- name: side_ratio
	dtype: float32
	- name: class
	dtype: string
	- name: pair_id
	dtype: string
	- name: quality
	dtype: int32
	- name: annotator
	dtype: string
	- name: notes
	dtype: string
	splits:
	- name: benchmark
	num_bytes: 858514979
	num_examples: 1321
	- name: variability_study
	num_bytes: 268131851
	num_examples: 446
	- name: lefood
	num_bytes: 212648001
	num_examples: 524
	download_size: 1308237830
	dataset_size: 1339294831
	configs:
	- config_name: default
	data_files:
	- split: benchmark
	path: data/benchmark-*
	- split: variability_study
	path: data/variability_study-*
	- split: lefood
	path: data/lefood-*
	---

	# Waste Benchmark Dataset

	## Overview
	The Waste Benchmark is a specialized computer vision dataset specifically built to test and validate waste computation models. It consists of tray image pairs (before and after consumption) processed through a custom technical workflow involving automated pre-annotation and manual expert refinement.
	This dataset may not be the final version as it is still in construction

	## Expert Labeling Criteria & Disclaimer
	Annotations were generated by expert annotators using the Food Waste Annotation Tool.
	Marta López Poch (mlopez@proppos.com) and Ambia Mohammad Bibi being qualified dietitists and nutritionist and thus considered experts on this task.

	### Process & Methodology
	* Workflow: Tasks are imported into Label Studio after receiving initial masks from a SegFormer model (`proppos/segformer_food`) to speed up the process.
	* Manual Refinement: Experts manually refine these masks and assign waste ratios based on visual volume estimation.
	* Site Context: The data primarily originates from Germans Trias hospital.

	### Bias and Ground Truth Warning
	Important: These labels should not be interpreted as absolute objective ground truth.
	* Expert annotators possess individual biases regarding volume and waste perception.
	* The labels represent an expert's visual judgment.
	* Users should account for human subjectivity when measuring model accuracy against these labels.


	## Usage Guide

	### Dataset Splits
	This repository contains two distinct subsets to facilitate both model training and error analysis:

	1. `benchmark`: The primary dataset (1,926 examples). This split represents the "standard" high-quality annotations intended for model evaluation and benchmarking.
	2. `variability_study`: A secondary subset (947 examples) designed specifically to study variability among annotators. This split is used to conduct quality studies and analyze the variance of human/expert annotators to understand the limits of manual waste estimation.

	### Dataset Features

	Each example in the dataset contains the following features:

	* `before_image` (Image): A PIL Image object representing the meal tray before consumption. These were originally sourced from S3 and represent the baseline for waste computation.
	* `after_image` (Image): A PIL Image object representing the same tray after consumption. Comparison with the 'before' image allows for change detection.
	* `before_mask` (String): An RLE-encoded string representing the segmentation masks in the `before_image`. These were initialized via SegFormer and manually refined by experts.
	* `after_mask` (String): An RLE-encoded string for the food items in the `after_image`.
	* Mask Categories: (Only on the Benchmark subset) Both `before_mask` and `after_mask` contain two distinct semantic categories:
	* Main Dish: The primary protein or central component of the meal.
	* Side Dish: Accompanying items such as vegetables, starches, or salads.
	* `total_waste_ratio` (Float32): The primary label indicating the percentage of total food wasted, calculated as a ratio between 0.0 and 1.0.
	* `pair_id` (String): A unique identifier for the specific meal tray event (e.g., `gt_001`), used to trace data back to original source logs or hospital sites.
	* `annotator` (String): The identifier for the expert who completed the task refinement. Crucial for the `variability_study` split to track inter-annotator variance.
	* `notes` (String): Manual comments provided by the annotator during the refinement process, including details on image quality or labeling challenges.



	### Loading the Dataset
	To access this private dataset, ensure you have a Hugging Face token with appropriate permissions.

	```python
	from datasets import load_dataset

	# Load the main benchmark split
	ds = load_dataset("proppos/waste-benchmark", split="benchmark", use_auth_token=True)

	#Decoding Masks (RLE)
	#The before_mask and after_mask properties are stored as Run-Length Encoded (RLE) strings. To use these for training, they must be decoded into binary masks.
	import json
	import numpy as np
	import json
	import numpy as np

	def rle_to_multiclass_mask(annotation_list, height, width):
	"""
	Decodes the raw Label Studio JSON list into a single multiclass mask.
	Main Dish = 1, Side Dish = 2
	"""
	mask = np.zeros((height, width), dtype=np.uint8)

	label_map = {"Main Dish": 1, "Side Dish": 2}

	for item in annotation_list:
	if item['type'] != 'brushlabels':
	continue

	label = item['value']['brushlabels'][0]
	category_id = label_map.get(label, 0)
	rle = item['value']['rle']

	flat_mask = np.zeros(height * width, dtype=np.uint8)
	for i in range(0, len(rle), 2):
	flat_mask[rle[i] : rle[i] + rle[i+1]] = category_id

	# Merge into the main mask
	mask = np.maximum(mask, flat_mask.reshape((height, width)))

	return mask

	```

	# Contact & Maintenance

	Main Maintainer: Genís Láinez (glainez@proppos.com).