--- 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).