Volleyball Detection using Deep Learning

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title : Volleyball Detection using Deep Learning

🔴 Aim : The aim is to create a deep learning project to detect the volleyball from the given dataset.

🔴 Dataset : https://www.kaggle.com/datasets/pythonistasamurai/volleyball-ball-object-detection-dataset

🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

---

📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

---

🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

---

✅ To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Email ID :
• Participant ID (if applicable):
• Approach for this Project :
• What is your participant role? (Mention the Open Source program)

---

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[tulu-g559]

@abhisheks008
Assign this to me

[abhisheks008]

@abhisheks008 Assign this to me

Which open source event you are participating in??

[tulu-g559]

@abhisheks008
Under JWoC

[abhisheks008]

Can you share your approach for solving this issue?
@tulu-g559

[tulu-g559]

@abhisheks008

Prepare the Dataset:
Obtain the volleyball detection dataset from Kaggle.
Verify that the dataset annotations are in YOLO format, consisting of images and related label files that indicate bounding box coordinates.

Configure Training Parameters:
Specify the model architecture and input image dimensions.
Configure training parameters like the number of epochs, batch size, and learning rate.

Train the Model:
Begin the training process with the pre-prepared dataset and set parameters.
Track training metrics such as loss values, as well as mean Average Precision (mAP), to check model performance.

Evaluate and Predict:
Once trained, test the model on validation data to see how accurate it is.
Use the trained model to predict the presence and location of volleyballs in new images.

[abhisheks008]

Which deep learning models are you planning to build here with this dataset?

Mention 3-4 deep learning models.

[tulu-g559]

YOLOv8 (You Only Look Once v8)
I am gonna use

1. Faster R-CNN (Region-based Convolutional Neural Network)
2. SSD (Single Shot MultiBox Detector)
3. EfficientDet

[abhisheks008]

YOLOv8 (You Only Look Once v8) I am gonna use

1. Faster R-CNN (Region-based Convolutional Neural Network)
2. SSD (Single Shot MultiBox Detector)
3. EfficientDet

Assigned @tulu-g559