Test Blender Synth Data

## Overview

Test Blender Synth Data is a dataset for object detection tasks - it contains Geometric Shape annotations for 329 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Blenderproc

## Overview

Blenderproc is a dataset for object detection tasks - it contains Conector Tapa annotations for 230 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

4404 Global import shipment records of Blender with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

MixInstruct

  Introduction

This is the official realease of dataset MixInstruct for project LLM-Blender. This dataset contains 11 responses from the current popular instruction following-LLMs that includes:

Stanford Alpaca FastChat Vicuna Dolly V2 StableLM Open Assistant Koala Baize Flan-T5 ChatGLM MOSS Moasic MPT

We evaluate each response with auto metrics including BLEU, ROUGE, BERTScore, BARTScore. And provide pairwise comparison results by prompting ChatGPT for the… See the full description on the dataset page: https://huggingface.co/datasets/llm-blender/mix-instruct.

93596 Global import shipment records of Of blender with prices, volume & current Buyer’s suppliers relationships based on actual Global import trade database.

The MatSim Dataset and benchmark

Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.

MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).

Based on the paper: One-shot recognition of any material anywhere using contrastive learning with physics-based rendering

Benchmark_MATSIM.zip: contain the benchmark made of real-world images as described in the paper

MatSim_object_train_split_1,2,3.zip: Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.

MatSim_Vessels_Train_1,2,3.zip : Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.

*Note: these are subsets of the dataset; the full dataset can be found at:
https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX

or
https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF

Code:

Up to date code for generating the dataset, reading and evaluation and trained nets can be found in this URL:https://github.com/sagieppel/MatSim-Dataset-Generator-Scripts-And-Neural-net

Dataset Generation Scripts.zip: Contain the Blender (3.1) Python scripts used for generating the dataset, this code might be odl up to date code can be found here
Net_Code_And_Trained_Model.zip: Contain a reference neural net code, including loaders, trained models, and evaluators scripts that can be used to read and train with the synthetic dataset or test the model with the benchmark. Note code in the ZIP file is not up to date and contains some bugs For the Latest version of this code see this URL

Further documentation can be found inside the zip files or in the paper.

Accurate and robust 6DOF (Six Degrees of Freedom) pose estimation is a critical task in various fields, including computer vision, robotics, and augmented reality. This research paper presents a novel approach to enhance the accuracy and reliability of 6DOF pose estimation by introducing a robust method for generating synthetic data and leveraging the ease of multi-class training using the generated dataset. The proposed method tackles the challenge of insufficient real-world annotated data by creating a large and diverse synthetic dataset that accurately mimics real-world scenarios. The proposed method only requires a CAD model of the object and there is no limit to the number of unique data that can be generated. Furthermore, a multi-class training strategy that harnesses the synthetic dataset's diversity is proposed and presented. This approach mitigates class imbalance issues and significantly boosts accuracy across varied object classes and poses. Experimental results underscore the method's effectiveness in challenging conditions, highlighting its potential for advancing 6DOF pose estimation across diverse applications. Our approach only uses a single RGB frame and is real-time. Methods This dataset has been synthetically generated using 3D software like Blender and APIs like Blendeproc.

Collections of pairwise feedback datasets.

openai/summarize_from_feedback openai/webgpt_comparisons Dahoas/instruct-synthetic-prompt-responses Anthropic/hh-rlhf lmsys/chatbot_arena_conversations openbmb/UltraFeedback argilla/ultrafeedback-binarized-preferences-cleaned berkeley-nest/Nectar

Codes to reproduce the dataset: jdf-prog/UnifiedFeedback

  Dataset formats

{ "id": "...", "conv_A": [ { "role": "user", "content": "...", }, { "role": "assistant"… See the full description on the dataset page: https://huggingface.co/datasets/llm-blender/Unified-Feedback.

Dataset

This dataset was created by Dmitriy Levchenko

Contents

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset was created using LeRobot.

  Dataset Structure

meta/info.json: { "codebase_version": "v2.0", "robot_type": null, "total_episodes": 100, "total_frames": 55127, "total_tasks": 10, "total_videos": 200, "total_chunks": 1, "chunks_size": 1000, "fps": 50, "splits": { "train": "0:100"}, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/autobio-bench/insert-blender.

This dataset was created using LeRobot.

  Dataset Structure

meta/info.json: { "codebase_version": "v2.0", "robot_type": null, "total_episodes": 100, "total_frames": 79278, "total_tasks": 100, "total_videos": 200, "total_chunks": 1, "chunks_size": 1000, "fps": 50, "splits": { "train": "0:100"}, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/autobio-bench/thermal_mixer-blender.

Dataset

This dataset was created by Mirza Milan Farabi

Released under MIT

Contents

Dataset

This dataset was created by Mirza Milan Farabi

Released under MIT

Contents

This dataset is about books. It has 1 row and is filtered where the book is Procedural 3D modeling using geometry nodes in blender : discover the professional usage of geometry nodes and develop a creative approach to a node-based workflow. It features 7 columns including author, publication date, language, and book publisher.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

ABO is a large-scale dataset designed for material prediction and multi-view retrieval experiments. The dataset contains Blender renderings of 30 viewpoints for each of the 7,953 3D objects, as well as camera intrinsics and extrinsic for each rendering.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Blender is the free open source 3D content creation suite, available for all major operating systems under the GNU General Public License. Because of the overwhelming success of the first open movie project, Ton Roosendaal, the Blender Foundation''s chairman, has established the Blender Institute. This now is the permanent office and studio to more efficiently organize the Blender Foundation goals, but especially to coordinate and facilitate Open Projects related to 3D movies, games or visual effects.