# NTU CZ3004/SC2079 Image Recognition/Symbol Detection - Week 8 - 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 100,000 images across 31 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 9 model available](https://huggingface.co/pyesonekyaw/MDP_ImageRecognition_YOLOv5_Week_9_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 8 - 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 100,000 images across 31 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 9 model available
# NTU CZ3004/SC2079 Image Recognition/Symbol Detection - Week 8 - 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 100,000 images across 31 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 9 model available
TAGS
#object-detection #ultralytics #license-openrail #region-us 
 # NTU CZ3004/SC2079 Image Recognition/Symbol Detection - Week 8 - 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 100,000 images across 31 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 9 model available
TAGS
#object-detection #ultralytics #license-openrail #region-us 
# NTU CZ3004/SC2079 Image Recognition/Symbol Detection - Week 8 - 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 100,000 images across 31 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 9 model available