# NTU CZ3004/SC2079 Image Recognition/Symbol Detection - Week 9 - YOLOv5

CZ3004 is a module in Nanyang Technological University's Computer Science curriculum that involves creating a robot car that can navigate within an arena and around obstacles. 
Part of the assessment is to go to obstacles and detect alphanumeric symbols pasted on them.

## Training Data
The training dataset had 20,000 images across 3 classes, with each class having roughly the same number of images. The images were either downloaded from RoboFlow Universe or obtained by ourselves in real life. 

## Training Procedure
The notebook from Ultralytics was used for training. Training was done on Google Colab for 20 epochs. 

## Other Models
There is also a [Week 8 model available](https://huggingface.co/pyesonekyaw/MDP_ImageRecognition_YOLOv5_Week_8_AY22-23_NTU-SG)
{"license": "openrail", "tags": ["object-detection", "ultralytics"]}
object-detection ultralytics license:openrail region:us
TAGS
#object-detection #ultralytics #license-openrail #region-us 

# NTU CZ3004/SC2079 Image Recognition/Symbol Detection - Week 9 - YOLOv5

CZ3004 is a module in Nanyang Technological University's Computer Science curriculum that involves creating a robot car that can navigate within an arena and around obstacles. 
Part of the assessment is to go to obstacles and detect alphanumeric symbols pasted on them.

## Training Data
The training dataset had 20,000 images across 3 classes, with each class having roughly the same number of images. The images were either downloaded from RoboFlow Universe or obtained by ourselves in real life. 

## Training Procedure
The notebook from Ultralytics was used for training. Training was done on Google Colab for 20 epochs. 

## Other Models
There is also a Week 8 model available
# NTU CZ3004/SC2079 Image Recognition/Symbol Detection - Week 9 - YOLOv5

CZ3004 is a module in Nanyang Technological University's Computer Science curriculum that involves creating a robot car that can navigate within an arena and around obstacles. 
Part of the assessment is to go to obstacles and detect alphanumeric symbols pasted on them. ## Training Data
The training dataset had 20,000 images across 3 classes, with each class having roughly the same number of images. The images were either downloaded from RoboFlow Universe or obtained by ourselves in real life. ## Training Procedure
The notebook from Ultralytics was used for training. Training was done on Google Colab for 20 epochs. ## Other Models
There is also a Week 8 model available
TAGS
#object-detection #ultralytics #license-openrail #region-us 
 # NTU CZ3004/SC2079 Image Recognition/Symbol Detection - Week 9 - YOLOv5

CZ3004 is a module in Nanyang Technological University's Computer Science curriculum that involves creating a robot car that can navigate within an arena and around obstacles. 
Part of the assessment is to go to obstacles and detect alphanumeric symbols pasted on them. ## Training Data
The training dataset had 20,000 images across 3 classes, with each class having roughly the same number of images. The images were either downloaded from RoboFlow Universe or obtained by ourselves in real life. ## Training Procedure
The notebook from Ultralytics was used for training. Training was done on Google Colab for 20 epochs. ## Other Models
There is also a Week 8 model available
TAGS
#object-detection #ultralytics #license-openrail #region-us 
# NTU CZ3004/SC2079 Image Recognition/Symbol Detection - Week 9 - YOLOv5

CZ3004 is a module in Nanyang Technological University's Computer Science curriculum that involves creating a robot car that can navigate within an arena and around obstacles. 
Part of the assessment is to go to obstacles and detect alphanumeric symbols pasted on them.## Training Data
The training dataset had 20,000 images across 3 classes, with each class having roughly the same number of images. The images were either downloaded from RoboFlow Universe or obtained by ourselves in real life.## Training Procedure
The notebook from Ultralytics was used for training. Training was done on Google Colab for 20 epochs.## Other Models
There is also a Week 8 model available