Context

Yolov3 WEIGHTS

Inspiration

State Of The Art Object Detection Model Weights

Dataset

This dataset was created by Ahmed Alsikely

Contents

Trained YOLOv3 Model Weights for License Plate Detection

Competition Weights

## Overview

Competition Weights is a dataset for object detection tasks - it contains All annotations for 5,176 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).

Dataset

This dataset was created by Shubham

Contents

It contains the following files:

Training Get Weights

## Overview

Training Get Weights is a dataset for object detection tasks - it contains Character Detection annotations for 161 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).

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.

Weight

## Overview

Weight is a dataset for object detection tasks - it contains Objects annotations for 741 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).

Chick Weight

## Overview

Chick Weight is a dataset for object detection tasks - it contains Weight annotations for 1,724 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.

Dataset

This dataset was created by Alessio Peluso

Contents

Dataset

This dataset was created by Badruddoza Kaif

Contents

Create my trash dataset and use hexacopter to detect trash pollution with YOLOV3.

Trash dataset : 
1. bottle
2. bag
3. plastic_bag

YOLOV3 weight :
1. anchor.txt
2. classes.txt
3. weight.h5

Item Weight

## Overview

Item Weight is a dataset for object detection tasks - it contains Mg Kg Gram Ounce Pound Ton Mmg annotations for 220 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).

Personal Protective Equipment Dataset (PPED)

This dataset serves as a benchmark for PPE in chemical plants We provide datasets and experimental results.

1. The dataset

We produced a data set based on the actual needs and relevant regulations in chemical plants. The standard GB 39800.1-2020 formulated by the Ministry of Emergency Management of the People’s Republic of China defines the protective requirements for plants and chemical laboratories. The complete dataset is contained in the folder PPED/data.

1.1. Image collection

We took more than 3300 pictures. We set the following different characteristics, including different environments, different distances, different lighting conditions, different angles, and the diversity of the number of people photographed.

Backgrounds: There are 4 backgrounds, including office, near machines, factory and regular outdoor scenes.

Scale: By taking pictures from different distances, the captured PPEs are classified in small, medium and large scales.

Light: Good lighting conditions and poor lighting conditions were studied.

Diversity: Some images contain a single person, and some contain multiple people.

Angle: The pictures we took can be divided into front and side.

A total of more than 3300 photos were taken in the raw data under all conditions. All images are located in the folder “PPED/data/JPEGImages”.

1.2. Label

We use Labelimg as the labeling tool, and we use the PASCAL-VOC labelimg format. Yolo use the txt format, we can use trans_voc2yolo.py to convert the XML file in PASCAL-VOC format to txt file. Annotations are stored in the folder PPED/data/Annotations

1.3. Dataset Features

The pictures are made by us according to the different conditions mentioned above. The file PPED/data/feature.csv is a CSV file which notes all the .os of all the image. It records every feature of the picture, including lighting conditions, angles, backgrounds, number of people and scale.

1.4. Dataset Division

The data set is divided into 9:1 training set and test set.

1. Baseline Experiments

We provide baseline results with five models, namely Faster R-CNN ®, Faster R-CNN (M), SSD, YOLOv3-spp, and YOLOv5. All code and results is given in folder PPED/experiment.

2.1. Environment and Configuration:

Intel Core i7-8700 CPU

NVIDIA GTX1060 GPU

16 GB of RAM

Python: 3.8.10

pytorch: 1.9.0

pycocotools: pycocotools-win

Windows 10

2.2. Applied Models

The source codes and results of the applied models is given in folder PPED/experiment with sub-folders corresponding to the model names.

2.2.1. Faster R-CNN

Faster R-CNN

backbone: resnet50+fpn

We downloaded the pre-training weights from https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth.

We modified the dataset path, training classes and training parameters including batch size.

We run train_res50_fpn.py start training.

Then, the weights are trained by the training set.

Finally, we validate the results on the test set.

backbone: mobilenetv2

the same training method as resnet50+fpn, but the effect is not as good as resnet50+fpn, so it is directly discarded.

The Faster R-CNN source code used in our experiment is given in folder PPED/experiment/Faster R-CNN. The weights of the fully-trained Faster R-CNN (R), Faster R-CNN (M) model are stored in file PPED/experiment/trained_models/resNetFpn-model-19.pth and mobile-model.pth. The performance measurements of Faster R-CNN (R) Faster R-CNN (M) are stored in folder PPED/experiment/results/Faster RCNN(R)and Faster RCNN(M).

2.2.2. SSD

backbone: resnet50

We downloaded pre-training weights from https://download.pytorch.org/models/resnet50-19c8e357.pth.

The same training method as Faster R-CNN is applied.

The SSD source code used in our experiment is given in folder PPED/experiment/ssd. The weights of the fully-trained SSD model are stored in file PPED/experiment/trained_models/SSD_19.pth. The performance measurements of SSD are stored in folder PPED/experiment/results/SSD.

2.2.3. YOLOv3-spp

backbone: DarkNet53

We modified the type information of the XML file to match our application.

We run trans_voc2yolo.py to convert the XML file in VOC format to a txt file.

The weights used are: yolov3-spp-ultralytics-608.pt.

The YOLOv3-spp source code used in our experiment is given in folder PPED/experiment/YOLOv3-spp. The weights of the fully-trained YOLOv3-spp model are stored in file PPED/experiment/trained_models/YOLOvspp-19.pt. The performance measurements of YOLOv3-spp are stored in folder PPED/experiment/results/YOLOv3-spp.

2.2.4. YOLOv5

backbone: CSP_DarkNet

We modified the type information of the XML file to match our application.

We run trans_voc2yolo.py to convert the XML file in VOC format to a txt file.

The weights used are: yolov5s.

The YOLOv5 source code used in our experiment is given in folder PPED/experiment/yolov5. The weights of the fully-trained YOLOv5 model are stored in file PPED/experiment/trained_models/YOLOv5.pt. The performance measurements of YOLOv5 are stored in folder PPED/experiment/results/YOLOv5.

2.3. Evaluation

The computed evaluation metrics as well as the code needed to compute them from our dataset are provided in the folder PPED/experiment/eval.

1. Code Sources

Faster R-CNN (R and M)

https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/faster_rcnn

official code: https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py

SSD

https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/ssd

official code: https://github.com/pytorch/vision/blob/main/torchvision/models/detection/ssd.py

YOLOv3-spp

https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/yolov3-spp

YOLOv5

https://github.com/ultralytics/yolov5

Content

YOLOv3 is a the fastest model to detect an object. This dataset contains weights file trained with YOLOv3 and helmet images. It also contains cfg and names file that can be easily used with OpenCV to detect helmets in images.

Chinese Chemical Safety Signs (CCSS)

This dataset is compiled as a benchmark for recognizing chemical safety signs from images. We provide both the dataset and the experimental results.

1. The Dataset

The complete dataset is contained in the folder ccss/data. The images include signs based on the Chinese standard "Safety Signs and their Application Guidelines" (GB 2894-2008) for safety signs in chemical environments. This standard, in turn, refers to the standards ISO 7010 (Graphical symbols – Safety Colours and Safety Signs – Safety signs used in workplaces and public areas), GB/T 10001 (Public Information Graphic Symbols for Signs), and GB 13495 (Fire Safety Signs)

1.1. Image Collection

We collect photos of commonly used chemical safety signs in chemical laboratories and chemistry teaching. For a discussion of the standards we base our collections, refer to the book "Talking about Hazardous Chemicals and Safety Signs" for common signs, and refer to the safety signs guidelines (GB 2894-2008).

• The shooting was mainly carried out in 6 locations, namely on the road, in a parking lot, construction walls, in a chemical laboratory, outside near big machines, and inside the factory and corridor.
• Shooting scale: Images in which the signs appear in small, medium and large scales were taken for each location by shooting photos from different distances.
• Shooting light: good lighting conditions and poor lighting conditions were investigated.
• Part of the images contain multiple targets and the other part contains only single signs.

Under all conditions, a total of 4650 photos were taken in the original data. These were expanded to 27,900 photos were via data enhancement. All images are located in folder ccss/data/JPEGImages.

The file ccss/data/features/enhanced_data_to_original_data.csv provides a mapping between the enhanced image name and the corresponding original image.

1.2. Annotation and Labelimg

We use Labelimg as labeling tool, which, in turn, uses the PASCAL-VOC labelimg format. The annotation is stored in the folder ccss/data/Annotations.

Faster R-CNN and SSD are two algorithms that use this format. When training YOLOv5, you can run trans_voc2yolo.py to convert the XML file in PASCAL-VOC format to a txt file.

We provide further meta-information about the dataset in form of a CSV file features.csv which notes, for each image, which other features it has (lighting conditions, scale, multiplicity, etc.). We apply the COCO standard for deciding whether a target is small, medium, or large in size.

1.3. Dataset Features

As stated above, the images have been shot under different conditions. We provide all the feature information in folder ccss/data/features. For each feature, there is a separate list of file names in that folder. The file ccss/data/features/features_on_original_data.csv is a CSV file which notes all the features of each original image.

1.4. Dataset Division

The data set is fixedly divided into 7:3 training set and test set. You can find the corresponding image names in the files ccss/data/training_data_file_names.txt and ccss/data/test_data_file_names.txt.

2. Baseline Experiments

We provide baseline results with five models, namely Faster R-CNN (R), Faster R-CNN (M), SSD, YOLOv3-spp, and YOLOv5. All code and results is given in folder ccss/experiment.

2.2. Environment and Configuration:

• Single Intel Core i7-8700 CPU
• NVIDIA GTX1060 GPU
• 16 GB of RAM
• Python: 3.8.10
• pytorch: 1.9.0
• pycocotools: pycocotools-win
• Visual Studio 2017
• Windows 10

2.3. Applied Models

The source codes and results of the applied models is given in folder ccss/experiment with sub-folders corresponding to the model names.

2.3.1. Faster R-CNN

• Faster R-CNN (R) has the backbone resnet50+fpn.
  • we downloaded the pre-training weights from https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
  • we modify the type information of the JSON file to match our application.
  • run train_res50_fpn.py
  • finally, the weights trained by the training set.
  The Faster R-CNN (R) source code used in our experiment is given in folder ccss/experiment/sources/faster_rcnn (R). The weights of the fully-trained Faster R-CNN (R) model are stored in file ccss/experiment/trained_models/faster_rcnn (R).pth. The performance measurements of Faster R-CNN (R) are stored in folder ccss/experiment/performance_indicators/faster_rcnn (R).
• Faster R-CNN (M) has the backbone mobilenetv2.
  • backbone: MobileNetV2.
  • we modify the type information of the JSON file to match our application.
  • run train_mobilenetv2.py
  • finally, the weights trained by the training set.
  The Faster R-CNN (M) source code used in our experiment is given in folder ccss/experiment/sources/faster_rcnn (M). The weights of the fully-trained Faster R-CNN (M) model are stored in file ccss/experiment/trained_models/faster_rcnn (M).pth. The performance measurements of Faster R-CNN (M) are stored in folder ccss/experiment/performance_indicators/faster_rcnn (M).

2.3.2. SSD

• backbone: resnet50
• we downloaded pre-training weights from https://download.pytorch.org/models/resnet50-19c8e357.pth
• the same training method as Faster R-CNN is applied.

The SSD source code used in our experiment is given in folder ccss/experiment/sources/ssd. The weights of the fully-trained SSD model are stored in file ccss/experiment/trained_models/ssd.pth. The performance measurements of SSD are stored in folder ccss/experiment/performance_indicators/ssd.

2.3.3. YOLOv3-spp

• backbone: DarkNet53
• we modified the type information of the XML file to match our application
• run trans_voc2yolo.py to convert the XML file in VOC format to a txt file.
• the weights used are: yolov3-spp-ultralytics-608.pt.

The YOLOv3-spp source code used in our experiment is given in folder ccss/experiment/sources/yolov3-spp. The weights of the fully-trained YOLOv3-spp model are stored in file ccss/experiment/trained_models/yolov3-spp.pt. The performance measurements of YOLOv3-spp are stored in folder ccss/experiment/performance_indicators/yolov3-spp.

2.3.4. YOLOv5

• backbone: CSP_DarkNet
• we modified the type information of the XML file to match our application
• run trans_voc2yolo.py to convert the XML file in VOC format to a txt file.
• the weights used are: yolov5s.

The YOLOv5 source code used in our experiment is given in folder ccss/experiment/sources/yolov5. The weights of the fully-trained YOLOv5 model are stored in file ccss/experiment/trained_models/yolov5.pt. The performance measurements of YOLOv5 are stored in folder ccss/experiment/performance_indicators/yolov5.

2.4. Evaluation

The computed evaluation metrics as well as the code needed to compute them from our dataset are provided in the folder ccss/experiment/performance_indicators. They are provided over the complete test st as well as separately for the image features (over the test set).

3. Code Sources

1. Faster R-CNN (R and M)
   • https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/faster_rcnn
   • official code: https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py
2. SSD
   • https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/ssd
   • official code: https://github.com/pytorch/vision/blob/main/torchvision/models/detection/ssd.py
3. YOLOv3-spp
   • https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/yolov3-spp
4. YOLOv5
   • https://github.com/ultralytics/yolov5

We are particularly thankful to the author of the GitHub repository WZMIAOMIAO/deep-learning-for-image-processing (with whom we are not affiliated). Their instructive videos and codes were most helpful during our work. In

Weight OD

## Overview

Weight OD is a dataset for object detection tasks - it contains Weight annotations for 1,284 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).

Context

League of Legends is a MOBA (Multiplayer Online Battle Arena) where 2 teams (blue and red) face off. There are 3 lanes, a jungle, and 5 roles. The goal is to take down the enemy Nexus to win the game.

Content

This dataset contains plain images and noised images with bounding boxes drawn to identify the champions from within the mini-map throughout the course of a game of League of Legends.

The Champions currently available are (the numbers show the class number for the relevant champion): 0 - Veigar 1 - Diana 2 - Vladimir 3 - Ryze 4 - Ekko 5 - Irelia 6 - Master Yi 7 - Nocturne 8 - Pantheon 9 - Yorick

A YOLOv3 weights file is also added where it has been trained to identify the above mentioned champions.

Acknowledgements

I would like to thank Riot Games for developing and supporting League of Legends and Make Sense AI for enabling the creation of this dataset.

Inspiration

This dataset is available to help encourage and improve the information captured from the mini-map during the course of a League of Legends Game by developers.

Dataset

This dataset was created by IRONSIGHT

Contents

Context

Data for my Yolo v3 Object Detection in Tensorflow kernel.

Content

Contains sample images, fonts, class names and weights.

Acknowledgements

YOLO: Real-Time Object Detection