Dataset

This dataset was created by E4CRYPT3D

Released under CC0: Public Domain

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Nvidia Documentation Question and Answer pairs Q&A dataset for LLM finetuning about the NVIDIA about SDKs and blogs This dataset is obtained by generating Q&A pairs from a few NVIDIA websites such as development kits and guides. This data can be used to fine-tune any LLM for indulging knowledge about NVIDIA into them. Source: https://www.kaggle.com/datasets/gondimalladeepesh/nvidia-documentation-question-and-answer-pairs

Dataset

This dataset was created by Yingyao FENG

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Dataset

This dataset was created by Yingyao FENG

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Dataset

This dataset was created by Sameer

Released under Other (specified in description)

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Original Thread: https://www.kaggle.com/c/deepfake-detection-challenge/discussion/122786

All the links:

• Part 1
• Part 2
• Part 3
• Part 4
• Part 5
• Part 6
• Part 7
• Part 8
• Part 9

Dataset

This dataset was created by Ashfaque Yoosuff

Released under ODC Public Domain Dedication and Licence (PDDL)

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Dataset

This dataset was created by Stephen Hsu

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Started out as a pumpkin detector to test training YOLOv5. Now suffering from extensive feature creep and probably ending up as a cat/dog/spider/pumpkin/randomobjects-detector. Or as a desaster.

The dataset does not fit https://docs.ultralytics.com/tutorials/training-tips-best-results/ well. There are no background images and the labeling is often only partial. Especially in the humans and pumpkin category where there are often lots of objects in one photo people apparently (and understandably) got bored and did not labe everything. And of course the images from the cat-category don't have the humans in it labeled since they come from a cat-identification model which ignored humans. It will need a lot of time to fixt that.

Dataset used: - Cat and Dog Data: Cat / Dog Tutorial NVIDIA Jetson https://github.com/dusty-nv/jetson-inference/blob/master/docs/pytorch-cat-dog.md © 2016-2019 NVIDIA according to bottom of linked page - Spider Data: Kaggle Animal 10 image set https://www.kaggle.com/datasets/alessiocorrado99/animals10 Animal pictures of 10 different categories taken from google images Kaggle project licensed GPL 2 - Pumpkin Data: Kaggle "Vegetable Images" https://www.researchgate.net/publication/352846889_DCNN-Based_Vegetable_Image_Classification_Using_Transfer_Learning_A_Comparative_Study https://www.kaggle.com/datasets/misrakahmed/vegetable-image-dataset Kaggle project licensed CC BY-SA 4.0 - Some pumpkin images manually copied from google image search - https://universe.roboflow.com/chess-project/chess-sample-rzbmc Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/steve-pamer-cvmbg/pumpkins-gfjw5 Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/nbduy/pumpkin-ryavl Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/homeworktest-wbx8v/cat_test-1x0bl/dataset/2 - https://universe.roboflow.com/220616nishikura/catdetector - https://universe.roboflow.com/atoany/cats-s4d4i/dataset/2 - https://universe.roboflow.com/personal-vruc2/agricultured-ioth22 - https://universe.roboflow.com/sreyoshiworkspace-radu9/pet_detection - https://universe.roboflow.com/artyom-hystt/my-dogs-lcpqe - license: Public Domain url: https://universe.roboflow.com/dolazy7-gmail-com-3vj05/sweetpumpkin/dataset/2 - https://universe.roboflow.com/tristram-dacayan/social-distancing-g4pbu - https://universe.roboflow.com/fyp-3edkl/social-distancing-2ygx5 License MIT - Spiders: https://universe.roboflow.com/lucas-lins-souza/animals-train-yruka

Currently I can't guarantee it's all correctly licenced. Checks are in progress. Inform me if you see one of your pictures and want it to be removed!

Dataset

This dataset was created by Sumukh

Released under CC0: Public Domain

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Analysis of ‘Laptop Price’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/muhammetvarl/laptop-price on 28 January 2022.

--- Dataset description provided by original source is as follows ---

1 Company- String -Laptop Manufacturer 2 Product -String -Brand and Model 3 TypeName -String -Type (Notebook, Ultrabook, Gaming, etc.) 4 Inches -Numeric- Screen Size 5 ScreenResolution -String- Screen Resolution 6 Cpu- String -Central Processing Unit (CPU) 7 Ram -String- Laptop RAM 8 Memory -String- Hard Disk / SSD Memory 9 GPU -String- Graphics Processing Units (GPU) 10 OpSys -String- Operating System 11 Weight -String- Laptop Weight 12 Price_euros -Numeric- Price (Euro)

--- Original source retains full ownership of the source dataset ---

Accident-Detection-Model

Accident Detection Model is made using YOLOv8, Google Collab, Python, Roboflow, Deep Learning, OpenCV, Machine Learning, Artificial Intelligence. It can detect an accident on any accident by live camera, image or video provided. This model is trained on a dataset of 3200+ images, These images were annotated on roboflow.

Problem Statement

• Road accidents are a major problem in India, with thousands of people losing their lives and many more suffering serious injuries every year.
• According to the Ministry of Road Transport and Highways, India witnessed around 4.5 lakh road accidents in 2019, which resulted in the deaths of more than 1.5 lakh people.
• The age range that is most severely hit by road accidents is 18 to 45 years old, which accounts for almost 67 percent of all accidental deaths.

Accidents survey

https://user-images.githubusercontent.com/78155393/233774342-287492bb-26c1-4acf-bc2c-9462e97a03ca.png" alt="Survey">

Literature Survey

• Sreyan Ghosh in Mar-2019, The goal is to develop a system using deep learning convolutional neural network that has been trained to identify video frames as accident or non-accident.
• Deeksha Gour Sep-2019, uses computer vision technology, neural networks, deep learning, and various approaches and algorithms to detect objects.

Research Gap

• Lack of real-world data - We trained model for more then 3200 images.
• Large interpretability time and space needed - Using google collab to reduce interpretability time and space required.
• Outdated Versions of previous works - We aer using Latest version of Yolo v8.

Proposed methodology

• We are using Yolov8 to train our custom dataset which has been 3200+ images, collected from different platforms.
• This model after training with 25 iterations and is ready to detect an accident with a significant probability.

Model Set-up

Preparing Custom dataset

• We have collected 1200+ images from different sources like YouTube, Google images, Kaggle.com etc.
• Then we annotated all of them individually on a tool called roboflow.
• During Annotation we marked the images with no accident as NULL and we drew a box on the site of accident on the images having an accident
• Then we divided the data set into train, val, test in the ratio of 8:1:1
• At the final step we downloaded the dataset in yolov8 format.
  #### Using Google Collab
• We are using google colaboratory to code this model because google collab uses gpu which is faster than local environments.
• You can use Jupyter notebooks, which let you blend code, text, and visualisations in a single document, to write and run Python code using Google Colab.
• Users can run individual code cells in Jupyter Notebooks and quickly view the results, which is helpful for experimenting and debugging. Additionally, they enable the development of visualisations that make use of well-known frameworks like Matplotlib, Seaborn, and Plotly.
• In Google collab, First of all we Changed runtime from TPU to GPU.
• We cross checked it by running command ‘!nvidia-smi’
  #### Coding
• First of all, We installed Yolov8 by the command ‘!pip install ultralytics==8.0.20’
• Further we checked about Yolov8 by the command ‘from ultralytics import YOLO from IPython.display import display, Image’
• Then we connected and mounted our google drive account by the code ‘from google.colab import drive drive.mount('/content/drive')’
• Then we ran our main command to run the training process ‘%cd /content/drive/MyDrive/Accident Detection model !yolo task=detect mode=train model=yolov8s.pt data= data.yaml epochs=1 imgsz=640 plots=True’
• After the training we ran command to test and validate our model ‘!yolo task=detect mode=val model=runs/detect/train/weights/best.pt data=data.yaml’ ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt conf=0.25 source=data/test/images’
• Further to get result from any video or image we ran this command ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt source="/content/drive/MyDrive/Accident-Detection-model/data/testing1.jpg/mp4"’
• The results are stored in the runs/detect/predict folder.
  Hence our model is trained, validated and tested to be able to detect accidents on any video or image.

Challenges I ran into

I majorly ran into 3 problems while making this model

• I got difficulty while saving the results in a folder, as yolov8 is latest version so it is still underdevelopment. so i then read some blogs, referred to stackoverflow then i got to know that we need to writ an extra command in new v8 that ''save=true'' This made me save my results in a folder.
• I was facing problem on cvat website because i was not sure what

Analysis of ‘Steam Hardware Survey July 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kunwardeepak/steam-hardware-survey-july-2020 on 14 February 2022.

--- Dataset description provided by original source is as follows ---

INSPIRATION

I got intrigued when steam asked my permission to collect data regarding my system's hardware . Obviously , very next instant i was out googling for this data to see if its public or not . And voila here it is .

CONTENT

This particular datasets contains really limited data of just distribution of CPU, GPU among various platform . Steam it self releases minimal data publicly and from that I decided to web scrap just this particular data as i think for every person either making a new rig or upgrading a current one combination of CPU and GPU contribute to be of major concern for both performance and money .

SOURCE

Data is scraped from the following link . https://store.steampowered.com/hwsurvey/Steam-Hardware-Software-Survey-Welcome-to-Steam

Further I will be publishing a scrapping notebook for this website so as to help any one else scrape data in order to have updated monthly data with ease .

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Theo Viel

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Dataset

This dataset was created by Hari

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Dataset

This dataset was created by Iafoss

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It contains the following files:

More information in "We Analyzed 495 AMD Radeon and Nvidia GPU Specifications and Shared the Dataset with Everyone" by Alena Guzharina, December 7, 2020 (https://blog.jetbrains.com/datalore/2020/12/07/we-analyzed-495-nvidia-and-amd-radeon-gpu-specifications/).

Dataset

This dataset was created by Luke Frazeur

Released under Apache 2.0

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Dataset

This dataset was created by Btbpanda

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https://upload.wikimedia.org/wikipedia/en/thumb/a/a4/NVIDIA_logo.svg/731px-NVIDIA_logo.svg.png" alt="NVidia">

• Nvidia Corporation is an American multinational technology company incorporated in Delaware and based in Santa Clara, California.

• It designs graphics processing units (GPUs) for the gaming and professional markets, as well as system on a chip units (SoCs) for the mobile computing and automotive market.

• Its primary GPU line, labeled "GeForce", is in direct competition with the GPUs of the "Radeon" brand by Advanced Micro Devices (AMD). Nvidia expanded its presence in the gaming industry with its handheld game consoles Shield Portable, Shield Tablet, and Shield Android TV and its cloud gaming service GeForce Now.

• Its professional line of GPUs are used in workstations for applications in such fields as architecture, engineering and construction, media and entertainment, automotive, scientific research, and manufacturing design.

• In addition to GPU manufacturing, Nvidia provides an application programming interface (API) called CUDA that allows the creation of massively parallel programs which utilize GPUs.They are deployed in supercomputing sites around the world. More recently, it has moved into the mobile computing market, where it produces Tegra mobile processors for smartphones and tablets as well as vehicle navigation and entertainment systems.It recently acquired ARM

# Let us analyze the performance of this solid star!