Omkar2118/datasets huggingface dataset:
LM-O
Full name: LINEMOD-Occluded

The **LM-O (Linemod-Occluded)** dataset, introduced by Brachmann et al. in their work on **6D object pose estimation**, provides additional ground-truth annotations for all modeled objects in one of the test sets from the **Linemod (LM)** dataset. This extension introduces challenging test cases with various levels of **occlusion** ¹²³.

Here are the key details about the LM-O dataset:

- **Objective**: The primary goal of the LM-O dataset is to evaluate the performance of 6D object pose estimation methods under occlusion conditions.
- **Source**: The dataset builds upon the Linemod dataset, which was originally proposed by Hinterstoisser et al. in their work on model-based training, detection, and pose estimation of texture-less 3D objects in heavily cluttered scenes ¹.
- **Annotations**: LM-O provides additional ground-truth annotations for all other instances of the modeled objects in one of the Linemod test sets. These annotations include information related to occlusion, making it a valuable resource for assessing pose estimation algorithms in challenging scenarios ¹.
- **Test Cases**: The LM-O dataset includes test images showing one annotated object instance with significant clutter but only mild occlusion. By incorporating occluded instances, it simulates real-world scenarios where objects may be partially hidden or obscured ¹.
- **License**: The dataset is available under the **CC BY-SA 4.0** license, allowing researchers to use and build upon it for their own investigations ¹.

Researchers and practitioners can leverage the LM-O dataset to develop and evaluate robust 6D object pose estimation methods that can handle occluded objects effectively. It serves as a valuable benchmark for advancing the field of computer vision and robotics.

(1) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.
(2) BOP: Benchmark for 6D Object Pose Estimation - cvut.cz. https://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop_slides_eccv.pdf.
(3) BOP: Benchmark for 6D Object Pose Estimation | SpringerLink. https://link.springer.com/chapter/10.1007/978-3-030-01249-6_2.
(4) Recovering 6D Object Pose: A Review and Multi-modal Analysis. https://link.springer.com/chapter/10.1007/978-3-030-11024-6_2.
(5) undefined. https://bop.felk.cvut.cz/media/data/bop_datasets.
MVTec ITODD

The **MVTec Industrial 3D Object Detection Dataset (MVTec ITODD)**, introduced by Bertram Drost, Markus Ulrich, Paul Bergmann, and Carsten Steger from MVTec Software GmbH, is a valuable resource for 3D object detection and pose estimation in industrial contexts¹²³. Here are the key details about this dataset:

1. **Purpose and Focus**:
   - MVTec ITODD is specifically designed for **realistic industrial setups**.
   - Unlike other 3D object detection datasets that often represent everyday life scenarios or mobile robot environments, ITODD models tasks relevant to industrial applications, such as **bin picking** and **object inspection**.
   - The dataset emphasizes objects, settings, and requirements that align with the challenges faced in industrial contexts.

2. **Dataset Characteristics**:
   - Contains **28 objects** with varying characteristics.
   - Arranged in over **800 scenes**.
   - Labeled with approximately **3500 rigid 3D transformations** of the object instances as ground truth.
   - Captures different **modalities** by using **two industrial 3D sensors** and **three high-resolution grayscale cameras** observing the scene from various angles.

3. **Evaluation Criteria**:
   - Unlike purely performance-based criteria, ITODD focuses on practical aspects:
     - **Runtimes**
     - **Memory consumption**
     - **Useful correctness measurements**
     - **Accuracy**

4. **Method Evaluation**:
   - The dataset has been evaluated using **five different methods**, revealing room for improvement.
   - Researchers are encouraged to submit their results for evaluation and inclusion in the dataset's result lists on the [official website](https://www.mvtec.com/company/research/datasets/itodd)¹.

In summary, MVTec ITODD provides a valuable benchmark for developing and evaluating 3D object detection algorithms tailored to industrial scenarios¹. Researchers can use this dataset to address the unique challenges posed by real-world industrial applications.

(1) Introducing MVTec ITODD - A Dataset for 3D Object Recognition in Industry. https://www.mvtec.com/fileadmin/Redaktion/mvtec.com/company/research/datasets/mvtec_itodd.pdf.
(2) ICCV 2017 Open Access Repository. https://openaccess.thecvf.com/content_ICCV_2017_workshops/w31/html/Drost_Introducing_MVTec_ITODD_ICCV_2017_paper.html.
(3) (PDF) Introducing MVTec ITODD — A Dataset for 3D Object Recognition in .... https://typeset.io/papers/introducing-mvtec-itodd-a-dataset-for-3d-object-recognition-2np8emo2oc.
(4) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.
(5) undefined. http://www.mvtec.com.
RU-APC
Full name: Rutgers APC

The **RU-APC (Rutgers APC) dataset** is a valuable resource for researchers and developers working on **robotic perception solutions** for **warehouse picking challenges**. Let me provide you with some details about this dataset:

1. **Dataset Overview**:
   - The **Rutgers APC RGB-D Dataset** is provided by the **PRACSYS lab at Rutgers University**.
   - It is designed to equip the research community with rich data for evaluating and improving robotic perception in the context of warehouse automation.
   - The dataset contains **10,368 depth and RGB registered images**.

2. **Object Annotations**:
   - For **24 of the Amazon Picking Challenge (APC) objects**, the dataset includes **hand-annotated 6DOF poses**.
   - These annotations are crucial for accurate pose estimation during object manipulation tasks.

3. **3D Mesh Models**:
   - Alongside the images, the dataset provides **3D mesh models** for **all 25 APC objects** (excluding the *mead_index_cards*).
   - These mesh models can be used for training recognition algorithms.

4. **Context and Significance**:
   - The **Amazon Picking Challenge (APC)** is a competition that focuses on perception, motion planning, and grasping of different objects placed inside bins of an **Amazon-Kiva Pod**.
   - Warehouse automation, especially picking and placing products on shelves, is a critical area of interest.
   - Unlike simpler tabletop scenarios, warehouse shelves introduce challenges due to narrow, dark, and obscuring bins.
   - Accurate pose estimation is essential for successful object manipulation within these shelves.

5. **Variety of Objects**:
   - The selected objects in the dataset were used during the **first Amazon Picking Challenge** held in Seattle in May 2015.
   - These objects exhibit diversity in terms of size, shape, texture, transparency, and other characteristics.
   - They represent good candidates for robotic units to transport in warehouse environments.

(1) Rutgers APC RGB-D Dataset - PRACSYS Group. https://robotics.cs.rutgers.edu/pracsys/rutgers-apc-rgb-d-dataset/.
(2) Datasets - PRACSYS Group. https://robotics.cs.rutgers.edu/pracsys/datasets/.
(3) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.

Introducing paper: A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place
IC-BIN

The **IC-BIN dataset** was introduced by Doumanoglou et al. as part of their research on **recovering 6D object pose and predicting next-best-view in the crowd**¹². This dataset is specifically designed to address the challenges posed by **reflective objects in robotic bin-picking scenarios**.

Here are the key details about the IC-BIN dataset:

1. **Purpose**: The IC-BIN dataset aims to facilitate research in **6D object pose estimation** and **active vision techniques** for reflective objects commonly encountered in bin-picking applications.

2. **Contents**:
   - The dataset comprises **multiple objects stacked in a bin**.
   - It includes **three scenes**, each containing **two objects** from the **IC-MI dataset**.
   - These scenes were recorded from **different viewpoints** to evaluate object pose estimation methods.

3. **Challenges**:
   - Reflective objects are often **texture-less** and cannot be reliably recognized using classic techniques based on local descriptors.
   - The high glossiness of these objects can introduce **fake edges** in RGB images and lead to **inaccurate depth measurements**, especially in cluttered bin scenarios.

4. **Data Annotation**:
   - For each scene, the dataset provides **monochrome/RGB images** and **depth maps** captured from sampled view spheres around the scene.
   - These images and maps are **annotated** with **accurate 6D poses** of visible objects and an associated **visibility score**.
   - Ground truth depth maps were captured using a high-cost Ensenso camera with objects coated in **anti-reflective scanning spray**.

5. **Utility and Evaluation**:
   - Researchers can use the IC-BIN dataset to evaluate the performance of **depth fusion** algorithms.
   - Evaluation results highlight the difficulty of handling highly reflective objects, especially in challenging cases with **degraded depth data quality**, **severe occlusions**, and cluttered scenes.

(1) ROBI: A Multi-View Dataset for Reﬂective Objects in Robotic Bin-Picking. https://arxiv.org/pdf/2105.04112v1.
(2) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.
(3) ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking. https://ar5iv.labs.arxiv.org/html/2105.04112.
(4) undefined. https://www.trailab.utias.utoronto.ca/robi.
IC-MI

The **IC-MI dataset**, introduced by **Tejani et al.**, is part of the **Benchmark for 6D Object Pose Estimation (BOP)**. Let's delve into the details:

1. **Dataset Overview**:
   - The IC-MI dataset contains **models of two texture-less and four textured household objects**.
   - These objects serve as test cases for evaluating 6D object detection and pose estimation methods.
   - The test images showcase **multiple object instances** with **clutter** and **slight occlusion**¹².

2. **Object Categories**:
   - **Texture-less Objects**: Two texture-less objects are included in the dataset.
   - **Textured Objects**: Four textured household objects are part of the dataset.

3. **Ground-Truth Annotations**:
   - The dataset provides **training/test RGB-D images** that are annotated with the following ground-truth information:
     - **6D object poses**: Precise 3D positions and orientations of the objects.
     - **2D bounding boxes**: Enclosing the objects in the 2D image plane.
     - **2D binary masks**: Indicating the object pixels.

4. **Data Creation**:
   - The **3D object models** were manually created or reconstructed using systems similar to **KinectFusion**.
   - Training images were captured by **RGB-D/Gray-D sensors** or rendered from the 3D models.
   - All test images are real-world captures.

5. **Format and Storage**:
   - The datasets are provided in the **BOP format**.
   - The BOP toolkit expects datasets to be stored in the same folder, with each dataset in a subfolder named after its base name (e.g., "lm," "lmo," "tless").
   - The IC-MI dataset is one of the components of this comprehensive benchmark.

In summary, the IC-MI dataset offers valuable resources for advancing 6D object pose estimation research, particularly in scenarios involving texture-less and textured objects with clutter and mild occlusion¹.

(1) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.
(2) BOP: Benchmark for 6D Object Pose Estimation | SpringerLink. https://link.springer.com/chapter/10.1007/978-3-030-01249-6_2.
(3) Sensors | Free Full-Text | Visual Attention and Color Cues for ... - MDPI. https://www.mdpi.com/1424-8220/21/23/8090.
(4) undefined. https://bop.felk.cvut.cz/media/data/bop_datasets.
TUD-L

The **TUD-L (TUD Light)** dataset is part of the **Benchmark for 6D Object Pose Estimation (BOP)**. Let me provide you with some details about this dataset:

1. **Description**:
   - The TUD-L dataset focuses on **lighting conditions** and includes **three moving objects** observed under **eight different illumination scenarios**.
   - It was specifically created as part of the BOP benchmark to evaluate 6D object pose estimation methods.

2. **Object Instances**:
   - The dataset contains three objects:
     - **Dragon**
     - **Frog**
     - **Watering pot**

3. **Challenges**:
   - TUD-L introduces **challenging illumination variations** to test the robustness of pose estimation algorithms.
   - Unlike other datasets, TUD-L has **limited clutter and occlusion**, making it suitable for evaluating lighting-specific performance.

4. **Ground-Truth Annotations**:
   - The dataset provides **ground-truth 6D object poses**, **2D bounding boxes**, and **2D binary masks** for the training and test RGB-D images.
   - These annotations enable researchers to evaluate their algorithms accurately.

5. **Usage**:
   - Researchers can use the TUD-L dataset to develop and evaluate 6D object pose estimation methods under varying lighting conditions.

Introducing paper: BOP: Benchmark for 6D Object Pose Estimation
TYO-L

The **TYO-L (Toyota Light)** dataset is part of the **Benchmark for 6D Object Pose Estimation (BOP)**. Let's delve into the details:

1. **TYO-L Dataset Overview**:
    - **Objects**: The TYO-L dataset contains **21 objects**.
    - **Capture Setup**: These objects were captured in **multiple poses** on a **table-top setup**.
    - **Variations**: The dataset includes **four different tablecloths** and **five different lighting conditions**.
    - **Texture Mapping**: The objects are represented by **texture-mapped 3D models**.
    - **License**: The dataset is licensed under **CC BY-NC 4.0**¹.

2. **Object Characteristics**:
    - The objects in TYO-L exhibit a **wide range of sizes**.
    - While the dataset focuses on pose estimation, it provides valuable information for research related to **low-light image and video enhancement** as well ².

3. **Benchmark for 6D Object Pose Estimation (BOP)**:
    - BOP aims to advance the field of 6D object pose estimation by providing standardized datasets, evaluation metrics, and challenges.
    - Other datasets within BOP include **LM (Linemod)**, **LM-O (Linemod-Occluded)**, and **T-LESS**.
    - Each dataset includes **3D object models**, **training/test RGB-D images**, and annotations for **ground-truth 6D object poses**, **2D bounding boxes**, and **2D binary masks**.
    - The datasets cover a variety of scenarios, including texture-less objects, occlusion, and symmetrical shapes ¹.

In summary, the TYO-L dataset contributes to the advancement of object pose estimation research, particularly in low-light conditions, and provides valuable resources for the scientific community.

(1) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.
(2) Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond. https://arxiv.org/html/2212.10772v5.
(3) Toyota Dataset | Kaggle. https://www.kaggle.com/datasets/chinmaypradhan29/toyota-dataset.
(4) undefined. https://bop.felk.cvut.cz/media/data/bop_datasets.

Introducing paper: BOP: Benchmark for 6D Object Pose Estimation
Diffusion Deepfake

Human face Deepfake dataset sampled from large datasets

- High Quality Dataset 
- Diverse Dataset 
- Challenging Dataset
- Large Dataset
- Text prompts

Introducing paper: Diffusion Deepfake
EgoExoLearn

**EgoExoLearn** is a fascinating dataset designed to bridge the gap between **egocentric** and **exocentric** views of procedural activities. 

1. **What Is EgoExoLearn?**   EgoExoLearn is a large-scale dataset that emulates how humans learn by observing others. It focuses on the process of **asynchronous demonstration following**. Participants in the dataset record **egocentric videos** as they perform tasks. These videos are guided by **exocentric-view demonstration videos**. In simpler terms, imagine someone watching a demonstration video (from an external perspective) and then replicating the same task while recording their own point-of-view video.

2. **Dataset Details:**
EgoExoLearn dataset spans **120 hours** and covers scenarios from daily life and specialized laboratories. It contains:
     - **Egocentric videos**: These are recorded by individuals executing tasks.
     - **Demonstration videos**: These show the same tasks from an external viewpoint.
     - **Gaze data**: High-quality gaze information accompanies the videos.
     - **Multimodal annotations**: Detailed annotations provide context and insights.

3. **Applications and Benchmarks:**
The EgoExoLearn dataset serves as a playground for modeling the human ability and thus provides a playground to bridge **asynchronous procedural actions** from different viewpoints.
It enables new benchmarks such as:
     - **Cross-view association**: Linking actions observed from different perspectives.
     - **Cross-view action planning**: Anticipating and planning actions based on both ego and exo views.
     - **Cross-view referenced skill assessment**: Evaluating skills across viewpoints. Using Exo-view demonstrations as guidance for better ego-view skill assessment.

4. **Why Is It Important?**
    - Understanding how we map others' activities into our own point of view is a fundamental human skill.
    - EgoExoLearn paves the way for creating AI agents capable of **seamlessly learning by observing humans in the real world**.

(1) EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric .... https://arxiv.org/html/2403.16182v1.
(2) EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric .... https://egoexolearn.github.io/.
(3) EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric .... https://arxiv.org/abs/2403.16182.
(4) EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric .... https://allainews.com/item/egoexolearn-a-dataset-for-bridging-asynchronous-ego-and-exo-centric-view-of-procedural-activities-in-real-world-2024-03-26/.
(5) undefined. https://github.com/OpenGVLab/EgoExoLearn/.

Introducing paper: EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World
ROCO

Radiology Objects in COntext (ROCO): A Multimodal Image Dataset
MIMIC Meme Dataset
Full name: Misogyny Identification in Multimodal Internet Content in Hindi-English Code-Mix Language

This dataset endeavors to fill the research void by presenting a meticulously curated collection of misogynistic memes in a code-mixed language of Hindi and English. It introduces two sub-tasks: the first entails a binary classification to determine the presence of misogyny in a meme, while the second task involves categorizing the misogynistic memes into multiple labels, including Objectification, Prejudice, and Humiliation.

For more Information and  Citation: Singh, A., Sharma, D., & Singh, V. K. (2024). MIMIC: Misogyny Identification in Multimodal Internet Content in Hindi-English Code-Mixed Language. ACM Transactions on Asian and Low-Resource Language Information Processing. (https://doi.org/10.1145/3656169)