Omkar2118/datasets huggingface dataset:
Places365

The **Places365** dataset is a scene recognition dataset. It is composed of 10 million images comprising 434 scene classes. There are two versions of the dataset: Places365-Standard with 1.8 million train and 36000 validation images from K=365 scene classes, and Places365-Challenge-2016, in which the size of the training set is increased up to 6.2 million extra images, including 69 new scene classes (leading to a total of 8 million train images from 434 scene classes).

Source: [Semantic-Aware Scene Recognition](https://arxiv.org/abs/1909.02410)
Image Source: [Places](http://places2.csail.mit.edu/index.html)

Introducing paper: Semantic-Aware Scene Recognition
LM
Full name: LINEMOD

The **LM (Linemod) dataset** is a valuable resource introduced by Stefan Hinterstoisser and colleagues in their research on **model-based training, detection, and pose estimation of texture-less 3D objects in heavily cluttered scenes**¹. Let's delve into the details:

1. **Purpose and Context**:
   - The primary goal of the LM dataset is to facilitate the development and evaluation of methods for detecting and estimating the **6 degrees-of-freedom pose** of texture-less 3D objects.
   - It specifically targets scenarios where objects lack distinctive textures and are embedded in complex backgrounds with occlusions.

2. **Methodology**:
   - The dataset builds upon the **LINEMOD approach**, which combines depth and color information to create templates representing different views of an object.
   - LINEMOD templates are learned from 3D models and serve as a basis for object detection.
   - The initial LINEMOD method had limitations, including online template learning and approximate pose estimation.

3. **Improvements and Contributions**:
   - Hinterstoisser et al. enhance LINEMOD by incorporating accurate 3D models of objects.
   - Their approach leverages the 3D model to address the shortcomings of the original LINEMOD.
   - Notable improvements include better pose estimation and reduced false positives.
   - The proposed framework is suitable for **robotics applications**, such as object manipulation.

4. **Dataset Details**:
   - The LM dataset consists of **15 registered video sequences**, each containing **over 1100 frames**.
   - These sequences feature **15 different texture-less household objects**.
   - Objects in the dataset exhibit discriminative color, shape, and size characteristics.
   - Researchers can use this dataset to evaluate and compare their methods for object detection and pose estimation.

![LM Dataset Example](https://i.imgur.com/EXAMPLE_IMAGE.png)

In summary, the LM dataset provides a valuable benchmark for advancing the field of 6D object pose estimation, especially in scenarios where texture information is limited¹². Researchers can access this dataset to test and refine their algorithms, ultimately contributing to advancements in robotics and machine vision.

(1) Model Based Training, Detection and Pose ... - Stefan HINTERSTOISSER. http://stefan-hinterstoisser.com/papers/hinterstoisser2012accv.pdf.
(2) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.
(3) paroj/linemod_dataset: Hinterstoisser et al. ACCV12 dataset - GitHub. https://github.com/paroj/linemod_dataset.