Object Detection YOLOv7 New Labels 19.01.23

## Overview

Object Detection YOLOv7 New Labels 19.01.23 is a dataset for object detection tasks - it contains Damages annotations for 766 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).

Label Ayam

## Overview

Label Ayam is a dataset for object detection tasks - it contains ST annotations for 373 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).

Custom Training with YOLOv7 🔥

Some Important links

• Model Inference🤖
• 🚀Training Yolov7 on Kaggle
• Weight and Biases 🐝
• HuggingFace 🤗 Model Repo

Contact Information

• Name - Owais Ahmad
• Phone - +91-9515884381
• Email - owaiskhan9654@gmail.com
• Portfolio - https://owaiskhan9654.github.io/

Objective

To Showcase custom Object Detection on the Given Dataset to train and Infer the Model using newly launched YoloV7.

Data Acquisition

The goal of this task is to train a model that can localize and classify each instance of Person and Car as accurately as possible.

• Link to the Downloadable Dataset

from IPython.display import Markdown, display

display(Markdown("../input/Car-Person-v2-Roboflow/README.roboflow.txt"))

Custom Training with YOLOv7 🔥

In this Notebook, I have processed the images with RoboFlow because in COCO formatted dataset was having different dimensions of image and Also data set was not splitted into different Format. To train a custom YOLOv7 model we need to recognize the objects in the dataset. To do so I have taken the following steps:

• Export the dataset to YOLOv7
• Train YOLOv7 to recognize the objects in our dataset
• Evaluate our YOLOv7 model's performance
• Run test inference to view performance of YOLOv7 model at work

📦 YOLOv7

https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/car-person-2.PNG" width=800>

Image Credit - jinfagang

Step 1: Install Requirements

!git clone https://github.com/WongKinYiu/yolov7 # Downloading YOLOv7 repository and installing requirements
%cd yolov7
!pip install -qr requirements.txt
!pip install -q roboflow

Downloading YOLOV7 starting checkpoint

!wget "https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"

import os
import glob
import wandb
import torch
from roboflow import Roboflow
from kaggle_secrets import UserSecretsClient
from IPython.display import Image, clear_output, display # to display images



print(f"Setup complete. Using torch {torch._version_} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})")

https://camo.githubusercontent.com/dd842f7b0be57140e68b2ab9cb007992acd131c48284eaf6b1aca758bfea358b/68747470733a2f2f692e696d6775722e636f6d2f52557469567a482e706e67">

I will be integrating W&B for visualizations and logging artifacts and comparisons of different models!

YOLOv7-Car-Person-Custom

try:
  user_secrets = UserSecretsClient()
  wandb_api_key = user_secrets.get_secret("wandb_api")
  wandb.login(key=wandb_api_key)
  anonymous = None
except:
  wandb.login(anonymous='must')
  print('To use your W&B account,
Go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB. 
Get your W&B access token from here: https://wandb.ai/authorize')
  
  
  
wandb.init(project="YOLOvR",name=f"7. YOLOv7-Car-Person-Custom-Run-7")

Step 2: Assemble Our Dataset

https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png" alt="">

In order to train our custom model, we need to assemble a dataset of representative images with bounding box annotations around the objects that we want to detect. And we need our dataset to be in YOLOv7 format.

In Roboflow, We can choose between two paths:

• Convert an existing Coco dataset to YOLOv7 format. In Roboflow it supports over 30 formats object detection formats for conversion.
• Uploading only these raw images and annotate them in Roboflow with Roboflow Annotate.

Version v2 Aug 12, 2022 Looks like this.

https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/Roboflow.PNG" alt="">

user_secrets = UserSecretsClient()
roboflow_api_key = user_secrets.get_secret("roboflow_api")

rf = Roboflow(api_key=roboflow_api_key)
project = rf.workspace("owais-ahmad").project("custom-yolov7-on-kaggle-on-custom-dataset-rakiq")
dataset = project.version(2).download("yolov7")

Step 3: Training Custom pretrained YOLOv7 model

Here, I am able to pass a number of arguments: - img: define input image size - batch: determine

This repository contains datasets to train, validate and test deep learning models to detect microfossil fish teeth and denticles called "ichthyoliths". All the dataset contains images of glass slides prepared from deep-sea sediment obtained from Pacific Ocean, and annotation label files formatted to YOLO. 01_original_all The dataset contains 12219 images and 6945 label files. 6945 images include at least one ichthyolith and 5274 images include no ichthyolith. These images and label files were randomly split into three subset, "train" that contains 9740 images and 5551 label files, "val" that contains 1235 images and 695 label files and "test" that contains 1244 images and 699 label files. All the images were selected manually. 02_original_selected This dataset is generated from 01_original_all by removing images without ichthyoliths. The dataset contains 6945 images that include at least one ichthyolith and 6945 label files. The dataset contains three subset, "train" that contains 5551 images and label files, "val" that contains 695 images and label files and "test" that contains 699 images and label files. 03_extended_all This dataset is generated from 01_original_all by adding 4463 images detected by deep learning models. The dataset contains 16682 images and 9473 label files. 9473 images include at least one ichthyolith and 7209 images include no ichthyolith. These images and label files were split into three subset, "train" that contains 13332 images and 7594 label files, "val" that contains 1690 images and 947 label files and "test" that contains 1660 images and 932 label files. Label files were checked manually. 04_extended_selected This dataset is generated from 03_extended_all by removing images without ichthyoliths. The dataset contains 9473 images that include at least one ichthyolith and 9473 label files. The dataset contains three subset, "train" that contains 7594 images and label files, "val" that contains 947 images and label files and "test" that contains 932 images and label files.

Unmanned aerial vehicles (UAVs) have become increasingly popular in recent years for both commercial and recreational purposes. Regrettably, the security of people and infrastructure is also clearly threatened by this increased demand. To address the current security challenge, much research has been carried out and several innovations have been made. Many faults still exist, however, including type or range detection failures and the mistaken identification of other airborne objects (for example, birds). A standard dataset that contains photos of drones and birds and on which the model might be trained for greater accuracy is needed to conduct experiments in this field. The supplied dataset is crucial since it will help train the model, giving it the ability to learn more accurately and make better decisions. The dataset that is being presented is comprised of a diverse range of images of birds and drones in motion. Pexel website's images and videos have been used to construct the dataset. Images were obtained from the frames of the recordings that were acquired, after which they were segmented and augmented with a range of circumstances. This would improve the machine-learning model's detection accuracy while increasing dataset training. The dataset has been formatted according to the YOLOv7 PyTorch specification. The test, train, and valid folders are contained within the given dataset. These folders each feature a plaintext file that corresponds to an associated image. Relevant metadata regarding the discovered object is described in the plaintext file. Images and labels are the two subfolders that constitute the folders. The collection consists of 20,925 images of birds and drones. The images have a 640 x 640 pixel resolution and are stored in JPEG format.

This dataset is for object detection task of the blue-ringed octopus (one of the most venomous animals in the world). With this dataset, I hope people can become more familiar with the blue ringed octopus and be aware of its dangers

I collected the images and labeled them myself (for a competition). I have less experience in collecting datasets, so I cannot guarantee the quality of this dataset. I trained a yolov7 object detection model with this data and got a mean average precision of 0.987 (with an IoU threshold of 0.5) .

About Datasets:

• Consists of 316 images with each label in the Pascal Voc format
• No pre-processing or image augmentation
• Not separated into train and test
• To use as an image classification, just delete its xml/label file
• Made in August-September 2022

How I Collect the Data:

I didn't go into the field to take these images, instead I took them from Google, some also from screenshots of some Youtube videos: - https://www.youtube.com/watch?v=MBHjo6UaHzk&t=62s - https://www.youtube.com/watch?v=c4BoYORmgSM - https://www.youtube.com/watch?v=DSdq8XFQdKo - https://www.youtube.com/watch?v=64mY1klkf4I&t=215s - https://www.youtube.com/watch?v=C0DOusbGWbU - https://www.youtube.com/watch?v=mTnmw5o4vRI - https://www.youtube.com/watch?v=bejKAB2Eazw&t=317s - https://www.youtube.com/watch?v=emisZUHJAEA - https://www.youtube.com/watch?v=6b_UYwyWI6E - https://www.youtube.com/watch?v=vVamzP52qwA - https://www.youtube.com/watch?v=3Bt1LvpZ1Oo

I also played around with the ai text to image generator to create multiple images and manually choose which one is acceptable (r_blue_ringed_octopus_100 - r_blue_ringed_octopus_110 , you can remove it if you want). After collecting the images, I do the labeling my self.

In total