https://github.com/opencodeiiita/deepsearov

In this open source project you will build a model which classify images captured by a remotely operated vehicle (ROV), featuring deep-sea biota belonging to 62 different classes.
https://github.com/opencodeiiita/deepsearov

deep-learning geekhaven image-classification keras neural-network opencode24 python tensorflow

Last synced: 24 days ago
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In this open source project you will build a model which classify images captured by a remotely operated vehicle (ROV), featuring deep-sea biota belonging to 62 different classes.

• Host: GitHub
• URL: https://github.com/opencodeiiita/deepsearov
• Owner: opencodeiiita
• Created: 2024-12-11T18:57:07.000Z (29 days ago)
• Default Branch: main
• Last Pushed: 2024-12-16T11:57:10.000Z (24 days ago)
• Last Synced: 2024-12-16T12:38:18.417Z (24 days ago)
• Topics: deep-learning, geekhaven, image-classification, keras, neural-network, opencode24, python, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 25.4 KB
• Stars: 0
• Watchers: 1
• Forks: 5
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# DeepSeaROV

 The deep sea is one of Earth's least explored frontiers, housing an extraordinary diversity of biota. However, studying these ecosystems is challenging due to their inaccessibility and the vast amounts of data generated during exploration missions. Automated image classification of deep-sea biota can significantly aid researchers in cataloging and analyzing these unique organisms.

 This project presents a meticulously curated dataset comprising images captured by a remotely operated vehicle (ROV) during deep-sea expeditions. The images feature 62 distinct classes of biota.Showcase your amazing deep learning abilities and help unlock the secrets of deep-sea biodiversity! 🌊✨

## **Instructions**

- For any concept/technique refer articles available on internet rather than using ChatGPT, as it may be misleading and many times provide only half information.

- Write commnets for what you did so that mentors can easy understand your work.

##  **Procedure**  

1. **Download** the dataset from the link provided above.  

2. **Fork** this repository and **clone** it to your local machine. *(You may need to re-clone after each task.)*  

3. **Naming Conventions:**  

   - **IIIT Allahabad Students:** Name files as **IIT2023098**, where:  

     - **IIT** = Your branch  

     - **2023098** = Your unique ID  

   - **Other College Participants:** Name files as **COLLEGE_ROLLNO** (e.g., **IITBHU_123456**).  

4. **File Placement:**  

   - Push your **.ipynb solution files** to the **correct folder**:  

     - **Example:** Place the solution for **Task1** in **solution_Task1**.  

5. **Submit a Pull Request:**  

   - Your **PR** will be reviewed by mentors.  

   - Only **relevant PRs** will be merged and **awarded points**.  

## Help

For any query feel free to contact [email protected]. You can also interact with mentors and community on Discord