manab-kb / object-recognition-and-classification-system Goto Github PK

Description

The Convolutional Neural Network (CNN) in the CNN.py file, present under the Code (Modules) folder implements Object Recognition and Classification in Fixed/Non-movable images. On the other hand, the Live Object Detector (SIFT Detector) in the Image Detection.py file, present under the Code (Modules) folder implements Object Detection in Live Images (Live Feed from camera). An important feature missing in this project, is a Live Object Recognition and Classification System, which combines capabilities of the already existing features.

Tip

Look through the README.md file to gain further information on the functionalities of the project.

Suggested Fix

Create a Live Object Recognition and Classification System.

Tasks

We suggest you to read through the CONTRIBUTING.md file before you continue.

1. Create a Live Object Recognition and Classification System.
2. Run the code on your local computer, to test for Model-Efficiency and Functionality.
3. Once you think you're ready, upload the code back to the folder mentioned in the description above - follow all the steps mentioned in the CONTRIBUTING.md file.
4. Upload the entire model in the CNN_Model folder.

Bounty Rules

The score are split into three types, in order of increasing rarity.

• Bounty Points (common to all issues) - 40 Points
• Brownie Points (for some of the PRs into which more effort went) - 10 Points
• Enthusiasm Points (for the PRs that went above and beyond) - 20 Points

Limit For Acceptance of PR's

• The first 5 successfully accepted PR's will be awarded with Brownie Points.
• Bounty Points will be awarded to all successfully accepted PR's, only after the Acceptance Limit has been crossed.
• Enthusiasm Points will be awarded before the event closes. (All successfully accepted PR's are eligible for Enthusiasm Points)

Resources

Make sure to check out the links below if you feel lost at any point in time.

• https://bdtechtalks.com/2020/01/06/convolutional-neural-networks-cnn-convnets/
• https://www.analyticsvidhya.com/blog/2019/10/detailed-guide-powerful-sift-technique-image-matching-python/

Description

The Convolutional Neural Network (CNN) in the CNN.py file, present under the Code (Modules) folder is not complete. Certain layers seem to be missing, thereby preventing the model from being created successfully.

Suggested Fix

Add the missing layers back to the CNN.

Tasks

We suggest you to read through the CONTRIBUTING.md file before you continue.

1. Add the missing layers to the CNN.
2. Run the code on your local computer, to test for Model-Efficiency and Functionality.
3. Once you think you're ready, upload the code back to the folder mentioned in the description above - follow all the steps mentioned in the CONTRIBUTING.md file.
4. Upload the entire model in the CNN_Model folder.

Bounty Rules

The score are split into three types, in order of increasing rarity.

• Bounty Points (common to all issues) - 35 Points
• Brownie Points (for some of the PRs into which more effort went) - 10 Points
• Enthusiasm Points (for the PRs that went above and beyond) - 10 Points

Limit For Acceptance of PR's

• The first 5 successfully accepted PR's will be awarded with Brownie Points.
• Bounty Points will be awarded to all successfully accepted PR's, only after the Acceptance Limit has been crossed.
• Enthusiasm Points will be awarded before the event closes. (All successfully accepted PR's are eligible for Enthusiasm Points)

Screenshots

Errors Encountered:

Resources

Make sure to check out the link below if you feel lost at any point in time.

• https://bdtechtalks.com/2020/01/06/convolutional-neural-networks-cnn-convnets/

Description

The Convolutional Neural Network (CNN) in the CNN.py file, present under the Code (Modules) folder can only recognize objects belonging to 10 different classes - Aeroplanes, Automobiles, Birds, Cats, Deer, Dogs, Frogs, Horses, Ships and Trucks.

Tip

Look through the README.md file to gain further information on the functionalities of the project.

Suggested Fix

Add recognition capabilities into the CNN Model for additional classes of objects.

Tasks

We suggest you to read through the CONTRIBUTING.md file before you continue.

1. Add recognition capabilities into the CNN Model for additional classes of objects.
2. Run the code on your local computer, to test for Model-Efficiency and Functionality.
3. Once you think you're ready, upload the code back to the folder mentioned in the description above - follow all the steps mentioned in the CONTRIBUTING.md file.
4. Upload the entire model in the CNN_Model folder.

Bounty Rules

The score are split into three types, in order of increasing rarity.

• Bounty Points (common to all issues) - 30 Points
• Brownie Points (for some of the PRs into which more effort went) - 5 Points
• Enthusiasm Points (for the PRs that went above and beyond) - 10 Points

Limit For Acceptance of PR's

• The first 3 successfully accepted PR's will be awarded with Brownie Points.
• Bounty Points will be awarded to all successfully accepted PR's, only after the Acceptance Limit has been crossed.
• Enthusiasm Points will be awarded before the event closes. (All successfully accepted PR's are eligible for Enthusiasm Points)

Resources

Make sure to check out the link below if you feel lost at any point in time.

• https://bdtechtalks.com/2020/01/06/convolutional-neural-networks-cnn-convnets/