Omkar2118/datasets huggingface dataset:
SIDL: A Real-World Dataset for Restoring Smartphone Images with Dirty Lenses

Smartphone cameras are ubiquitous in daily life, yet their performance can be severely impacted by dirty lenses, leading to degraded image quality. 
This issue is often overlooked in image restoration research, which assumes ideal or controlled lens conditions. 
To address this gap, we introduced SIDL (Smartphone Images with Dirty Lenses), a novel dataset designed to restore images captured through contaminated smartphone lenses. 
SIDL contains diverse real-world images taken under various lighting conditions and environments. 
These images feature a wide range of lens contaminants, including water drops, fingerprints, and dust. 
Each contaminated image is paired with a clean reference image, enabling supervised learning approaches for restoration tasks.
To evaluate the challenge posed by SIDL, various state-of-the-art restoration models were trained and compared on this dataset. 
Their performances achieved some level of restoration but did not adequately address the diverse and realistic nature of the lens contaminants in SIDL.
This challenge highlights the need for more robust and adaptable image restoration techniques for restoring images with dirty lenses.
Project website: https://sidl-benchmark.github.io

Source : [SIDL: A Real-World Dataset for Restoring Smartphone Images with Dirty Lenses](https://doi.org/10.1609/aaai.v39i3.32257)

Introducing paper: SIDL: A Real-World Dataset for Restoring Smartphone Images with Dirty Lenses
CoDocBench

One of the central tasks in software maintenance is being able to understand and develop code changes. Thus, given a natural language description of the desired new operation of a function, an agent (human or AI) might be asked to generate the set of edits to that function to implement the desired new operation; likewise, given a set of edits to a function, an agent might be asked to generate a changed description, of that function's new workings. Thus, there is an incentive to train a neural model for change-related tasks. Motivated by this, we offer a new, "natural", large dataset of coupled changes to code and documentation mined from actual high-quality GitHub projects, where each sample represents a single commit where the code and the associated docstring were changed together. We present the methodology for gathering the dataset, and some sample, challenging (but realistic) tasks where our dataset provides opportunities for both learning and evaluation. We find that current models (specifically Llama-3.1 405B, Mixtral 8x22B) do find these maintenance-related tasks challenging.

Introducing paper: CoDocBench: A Dataset for Code-Documentation Alignment in Software Maintenance
ATC-GRAPH

ATC-GRAPH is the most extensive ATC benchmark dataset. All drugs in the benchmarks are linked to their Mol files instead of the SMILES sequences utilized in earlier benchmarks. This shift allows for more precise and detailed modeling and learning. In terms of scale, ATC-GRAPH surpasses Chen-2012 and ATC-SMILES by 36.78% and 16.85%, respectively. Significantly, ATC-GRAPH was curated through a cross-validation process involving multiple resources such as KEGG, PubChem, ChEMBL, ChemSpider, and ChemicalBook. This results in ATC-GRAPH being distinguished by its timeliness and comprehensive coverage across all five levels and drug genres.

Introducing paper: GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learning
ATC-SMILES

The benchmark ATC-SMILES is built for ATC classification. ATC-SMILES consists of 4545 compounds/drugs and their SMILES sequences. The benchmark is with the maximum coverage (81.34%) of KEGG dataset which contains all 5588 known drugs/compounds used for ATC analysis. Prior to this benchmark, the most widely adopted one is Chen-2012 which covers 3883 (69.49%) drugs in KEGG and is mainly used for generating inter-drug correlations (e.g. STITCH). The two benchmarks are compared in Table 1. ATC-SMILES is designed to be inclusive to Chen-2012, but there are 2.16% misalignment due to the mismatching of drug IDs that we will explain soon. ATC-SMILES can be extended with new drugs much easier than previous benchmarks as long as the SMILES sequences are available. Trails/experiments are not a must.

Introducing paper: Identifying the kind behind SMILES—anatomical therapeutic chemical classification using structure-only representations
Werewolf-XL

Existing databases usually record posed or induced human behavior in individual or dyadic settings with biased annotations in which a basic emotional class label or a Valence–Arousal pair value represents the emotional states. To address this need,We created the “Werewolf-XL” database, which contains a total of 987 h of spontaneous audio-visual recordings of 129 subjects in a group interaction of nine individuals playing a conversational role-playing game called Werewolf. We provide 133,461 individual utterance-level video clips with internal (18 non-prototypical emotional categories) and external (pleasure, arousal, and dominance) emotion annotations including 14,635 samples from speaking players. Besides, the results of annotation agreement analysis show fair reliability and validity. Furthermore, we provided extensive benchmarks of unimodal and multimodal emotional recognition results. The database (the video clips, annotations, features and protocols) is made publicly available, which provides great opportunities for researchers in affective computing and beyond.
StreamingBench

StreamingBench evaluates Multimodal Large Language Models (MLLMs) in real-time, streaming video understanding tasks. 🌟

🎞️ Overview
As MLLMs continue to advance, they remain largely focused on offline video comprehension, where all frames are pre-loaded before making queries. However, this is far from the human ability to process and respond to video streams in real-time, capturing the dynamic nature of multimedia content. To bridge this gap, StreamingBench introduces the first comprehensive benchmark for streaming video understanding in MLLMs.

Key Evaluation Aspects
🎯 Real-time Visual Understanding: Can the model process and respond to visual changes in real-time?
🔊 Omni-source Understanding: Does the model integrate visual and audio inputs synchronously in real-time video streams?
🎬 Contextual Understanding: Can the model comprehend the broader context within video streams?
Dataset Statistics
📊 900 diverse videos
📝 4,500 human-annotated QA pairs
⏱️ Five questions per video at different timestamps

Introducing paper: StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding
Segmented Peripheral Blood Cells Using OpenCV

Description:

<a href="https://gts.ai/dataset-download/segmented-peripheral-blood-cells-using-opencv/" target="_blank">👉 Download the dataset here</a>

This dataset contains segmented images of white blood cells, derived from the Segmented Peripheral Blood Cell Dataset available. The original dataset comprises 17,092 images of individual normal cells, captured using the CellaVision DM96 analyzer in the Core Laboratory at the Hospital Clinic of Barcelona. These images are categorized into eight classes: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes, erythroblasts, and platelets.

Download Dataset

In this dataset, we provide 195 images per class, each accompanied by a corresponding binary mask. These masks were created using OpenCV techniques, including GrabCut and morphological operations. This dataset is ideal for training segmentation models such as U-Net, Mask R-CNN, and other advanced image segmentation techniques.

Applications

Training Segmentation Models: This dataset is ideal for training models focused on segmenting white blood cells, such as U-Net and Mask R-CNN.

Educational Purposes: A valuable resource for learning and practicing advanced image segmentation techniques.

Benchmarking: Use this dataset to compare the performance of different segmentation algorithms and models.

Methodology

The segmentation masks were created using the GrabCut technique, followed by morphological operations for refinement. This method ensures precise and high-quality segmentation, making the dataset suitable for a wide range of image segmentation tasks.

This dataset is sourced from Kaggle.