Version: 8.0.139

Usage: python !pip install /kaggle/input/ultralytics-whl/ultralytics-8.0.139-py3-none-any.whl GitHub: https://github.com/ultralytics/ultralytics PyPi: https://pypi.org/project/ultralytics/

Licencia: GNU Affero General Public License v3 or later (AGPLv3+) (AGPL-3.0) Responsable: Ultralytics Etiquetasmachine-learning, deep-learning, computer-vision, ML, DL, AI, YOLO, YOLOv3, YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLO11, HUB, Ultralytics Requiere: Python >=3.8

Dataset

This dataset was created by Mahmoud Abdelshafy

Released under Apache 2.0

Contents

This is the yoloV5 model cloned from ultralytics/yolov5 for offline use.

Setup

1. Add this dataset to the notebook and run the following commands.

2. Add model weights. You can use your own or take it from here.

!mkdir /root/.config/Ultralytics/
!cp ../input/yolo-arial/Arial.ttf /root/.config/Ultralytics/Arial.ttf

Load model

You can now call torch.hub.load() to load the yolov5 model offline. Make sure to set the parameters as repo or dir = './path/to/local/yolov5' model = 'custom' source = 'local' force_reload = True path = './path/to/best.pt'

Example

Using yolov5x6 weights

import torch
yolov5x6_model = torch.hub.load('../input/yolov5', 'custom', source='local', force_reload=True, path='../input/ultralyticsyolov5aweights/yolov5x6.pt')

From https://github.com/ultralytics/yolov5 2021/7/8

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!

Yolov V5 ver1.0

This dataset is cloned from https://github.com/ultralytics/yolov5

Version Note

As of the dataset creation date, the original Yolo V5 repo on Github is updated frequently, and this version can be dated very soon

Acknowledgements

Thanks Ultralytics team, especially the main author Glenn Jocher for creating this powerful and easy-to-use library.

Licence

GPL-3.0

How to download weights for an older Yolov5 version

1. Go to this location: https://github.com/ultralytics/yolov5/releases
2. Scroll down to v4.0.
3. Scroll down and click on Assets.
4. Click on the weights to download.

How to download a Yolo model. This downloads the latest Yolo version: https://www.kaggle.com/code/vbookshelf/exp-90-covid-downloading-yolo-v5/notebook?scriptVersionId=66895104

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

Dataset

This dataset was created by Fathy sahlool

Released under Apache 2.0

Contents

Context

For usage of Yolov5 without connecting to Internet.

Content

Yolov5-master and four model weight files "yolov5s.pt", "yolov5m.pt", "yolov5l.pt", "yolov5x.pt".

Acknowledgements

Downloaded from https://github.com/ultralytics/yolov5

These are all YOLO weights pretrained on the MS COCO dataset. The yolov3-spp-ultralytics.pt and yolov3-tiny.pt are taken from Ultralytics YOLOv3. The yolov4.pt is converted from yolov4.weights by AlexeyAB/darknet into PyTorch .pt format.

FSOCO dataset split into train (80%), validation (10%), and test (10%) set. Ready for Ultralytics YOLO training.

📦 MP-IDB-YOLO – Ultralytics YOLO-Formatted Malaria Parasite Image Dataset for Instance Segmentation and Object Detection

This dataset is a repackaged version of the original MP-IDB (The Malaria Parasite Image Database for Image Processing and Analysis), formatted for Ultralytics YOLO (You Only Look Once) instance segmentation annotation. The goal of this release is to make it easier for researchers and practitioners to apply state-of-the-art instance segmentation or object detection techniques to malaria cell detection and classification tasks.

⚠️ This dataset is a derivative work. All original images and annotations belong to the original MP-IDB authors. This version only converts them into Ultralytics YOLO-compatible format.

📚 About the Original Dataset

The original MP-IDB dataset was created and released by Andrea Loddo, Cecilia Di Ruberto, Michel Kocher, and Guy Prod’Hom, and is described in the following publication:

MP-IDB: The Malaria Parasite Image Database for Image Processing and Analysis
In Processing and Analysis of Biomedical Information, Springer, 2019.
DOI: 10.1007/978-3-030-13835-6_7

The dataset includes annotated microscopic blood smear images of four malaria species:

• Plasmodium falciparum
• Plasmodium malariae
• Plasmodium ovale
• Plasmodium vivax

Each image contains cells in one or more of the following parasite life stages, indicated in filenames:

• R: Ring
• T: Trophozoite
• S: Schizont
• G: Gametocyte

Expert pathologists provided the ground truth for each image.

🛠️ What’s Included in This YOLO Version

This version of the dataset includes:

• ✅ Original images from MP-IDB
• ✅ Instance Segmentation annotations in Ultralytics YOLO format (.txt files)
• ✅ Class definitions matching parasite species and life stages
• ✅ A YAML file ready for training using the Ultralytics Package

This reformatting is designed to save time for those building instance segmentation or object detection models for medical imaging and accelerate prototyping using YOLO and the Ultralytics Package.

📜 License and Attribution

The original MP-IDB dataset is released under the MIT License by Andrea Loddo and contributors. Please make sure to cite the original work if you use this dataset in your own research or application:

The dataset contains sample images from the Open Images Dataset v7. This dataset only contains images for the category 'ducks' and is arranged to fine-tune the YOLOv8 image segmentation models.

Directories:

The dataset contains two main directors, i.e., images and labels. These directories further contain 'train' and 'val' directories. As the names suggest, these directories contain images and labels for the training and validation of image segmentation models.

Dataset Description:

Training Images: 400 Validation Images: 50

Class/es: Duck

config.yaml

The dataset also contains a config.yaml file. This file contains paths for relevant directories that YOLOv8 needs to load datasets

Almost 12000 images used in Google Recaptcha V2 collected by category more than 500 of which with manual markup for training object detection model such as YOLO.

If you find this dataset useful, please leave an upvote, that motivates me to collect such datasets✋

Feel free to using this data for your commercial or educational goals. P.S. https://github.com/Artistrazh/recaptcha_v2_solver is my project for solving Google Recaptcha V2 using yolov3, BLIP and this dataset.

https://github.com/ultralytics/yolov5

Only clone, not modify because the license.

Configure for one class as Wheat New configure with yolov5x.yaml

Using: !cp -r ../input/all-in-one-yolov5-wheat/yolov5/* . !pip install -r requirements.txt if issue with torch version and torchvission, check version cuda and comeback the old version torch, example cuda 10.1 : !pip install torch==1.5.1+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html

!git clone https://github.com/NVIDIA/apex && cd apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . --user && cd .. && rm -rf apex

!python train.py --img 1024 --batch 2 --epochs 12 --data ../input/all-in-one-yolov5-wheat/wheat0.yaml --cfg ../input/all-in-one-yolov5-wheat/yolov5x.yaml --name yolov5_fold0 --weights "yolov5x.pt" --adam

Context

One of my passions is playing board games with my friends. However one of them lives abroad and so we like to stream the game when playing with him. However instead of just having a normal stream, I wanted to show some additional information about the monsters that are on the game board. This originated in a fun project to train CNNs in order to detect these monsters.

Content

To have enough training data, I made a little project in UE4 to generate these training images. For each image there is a mask for every monster that appears in it. The dataset also includes annotations for the train images in the COCO format (annotations.json) and labes for the bounding box in Darknet format in the folder labels.

There is a training and validation subset for the images, labels and masks folders. The structure is as follows: for the first training image containing an earth_demon and harrower_infester:

• The image is stored at images/train/image_1.png
• The label-file is stored at labels/train/label_1.png. This file contains two lines. One line for each monster. A line is constructed as follows: class_id center_x center_y width height. Note that the position and dimensions are relative to the image width and height.
• There are two mask images located at masks/train. One is named image_1_mask_0_harrower_infester.png and the other image_1_mask_1_earth_demon.png.

The code for generating this dataset and training a MaskRCNN and YoloV5 model can be found at https://github.com/ericdepotter/Gloomhaven-Monster-Recognizer.

Acknowledgements

I took pictures for the images of the monsters myself. The images of the game tiles I obtained from this collection of Gloomhaven assets.

Inspiration

This is a classic object detection or object segmentation problem.