Object Detection

Object Detection

I was curious how Computer Vision works because I have been told from childhood that computers only understand 0s and 1s! My fascination with the seemingly magical world of computers has been with me since I first heard about the binary code. Little did I know that years later, my curiosity would lead me on a captivating journey into the intricate workings of Object Detection. This technology not only defies the binary stereotype but also opens up a whole new universe of possibilities. Let's uncover the mysteries of object detection together, from the beginning of data collection to the final deployment. Join me in understanding every step, end to end.

Exploring the Object Detection Pipeline: Unveiling the Steps

Now that we've grasped the essence of object detection and the pivotal role of CNN, let's dive into the details of the object detection pipeline. This journey is a series of carefully orchestrated steps, each playing a crucial role in the goal of robust Object Detection. Join me as we navigate through the following key stages.

Step 1: Data Collection

• The foundation of any successful object detection model lies in the quality and diversity of the data it is trained on. We explore the intricacies of sourcing, curating, and organizing datasets that form the backbone of a robust detection system.

• Critical elements for a robust dataset:

Images per class - It is recommended to use at least 1500 images in each class.

Instances per class - The recommended number of instances (labeled objects) per class is ≥ 10,000.

Image variety - The deployed environment must be reflected in it. In practical applications, we suggest utilizing photos from various sources (such as scraped from the internet, locally gathered, or taken with various cameras), at various times of day, seasons, and weather conditions.

Consistency of labels - Every image must have a label for every instance of every class. It won't work to partially label.

Label accuracy - Labels must completely enclose each object. Ensure there is no space between an object and its bounding box. No objects should be without a label.

Label verification - View train_batch*.jpg on train start to ensure your labels are right, as shown in the example mosaic.

Background images - Background images are photos that include no objects and are used in datasets to reduce False Positives (FP). We recommend 0-10% backdrop photos to assist reduce FPs (COCO contains 1000 background images, which accounts for 1% of the total). Labels are not necessary for background photos.

• I have created a 'coin demo' project and am uploading a coin dataset to the Roboflow Annotation tool.

Step 2: Data Annotation The Pillar of Object Detection

• Data annotation is fundamental to building effective object detection models, providing the ground truth for machine learning algorithms. Precise annotations are crucial, teaching the model to recognize and delineate objects accurately by capturing spatial characteristics and contextual information.

• Data annotation is fundamental to building effective object detection models, providing the ground truth for machine learning algorithms. Precise annotations are crucial, teaching the model to recognize and delineate objects accurately by capturing spatial characteristics and contextual information.

 

• Here's an Annotation example of the coin’s dataset used in Roboflow Annotations tool.

• Data annotation comes in various forms to capture different aspects of objects within pixel level and key points. Common types include bounding boxes, which outline the object's location, segmentation masks that precisely delineate object boundaries at the pixel level, and key points that mark specific points of interest within an object. Each annotation type serves specific use cases, offering a nuanced understanding of object characteristics. In our exploration, we will predominantly focus on the widely-used bounding box annotation technique, providing a practical and effective means to convey object localization information for object detection models.

Step 2.1: Data Pre-Processing

• Before delving into model training, the crucial step of data pre-processing ensures that the dataset is refined and well-organized. This refers to tasks like cleaning, resizing, and normalizing the images. Additionally, performing the train-test-validation split helps in evaluating the model's performance accurately. Notably, tools like Roboflow's annotation tool streamline this process seamlessly, providing an automated solution without the need for extensive Python coding.

Step 2.2: Augmentation

• Augmentation techniques play a vital role, enhancing both dataset diversity and annotation robustness through practices like flipping, rotation, scaling, mitigating overfitting risks during model training and exposing the model to a diverse range of scenarios, enhancing its ability to generalize to unseen data. However, a delicate trade-off exists, as excessive augmentation may introduce noise or distortions.

• Here, we will use Roboflow's inbuilt Augmentation option.

Step 3: Model Training

• In the heart of the object detection journey lies model training, a pivotal step where Ultralytics YOLOv8 takes center stage. Leveraging state-of-the-art architecture, YOLOv8 optimizes the learning process by efficiently identifying and refining patterns within the dataset. Its robust capabilities, combined with user-friendly features, make it a formidable ally in achieving accurate and efficient object detection results.

• Google Colab Notebook [click here]: credits https://github.com/roboflow/notebooks

• Copy the code snippet and update it in the notebook provided, this will download your data from Roboflow Universe to train the model

• You can select an appropriate model based on your requirements, such as YOLOv8n (i.e., the nano model), for real-time object detection.

• Start Training your Model.

• Model Results are stored in the default directory below.

• Result on our Validation data.

• Here you can find the final model (i.e. best.pt).

• Great job! You've successfully trained your custom model.

• Here’s how to test it with a sample image.

• Result:

Step 4: Post-Training Challenges

• No journey is without obstacles. We'll discuss common issues encountered after model training, addressing concerns such as:

Overlapping Detections:

• Imagine your model sometimes sees one object as two or more. To fix this, we use something called Non-Maximum Suppression (NMS). It's like having your model pick the best guess when it sees overlapping things. We adjust a setting called IoU to make sure it decides what counts as a separate object. It's like telling your model, "If things overlap too much, just choose the most confident guess and ignore the others." This helps make sure your model doesn't get confused by objects that are too close or on top of each other. (i.e. post-processing techniques)

False Positives:

• To tackle false positives, consider re-evaluating your training data for better diversity and representation. Fine-tuning the model with additional, challenging examples can help it distinguish between real objects and misleading patterns. Adjusting the confidence threshold during inference can also play a crucial role in minimizing false positives by setting a higher threshold for detection. Fine-tune confidence thresholds: Experiment with different confidence thresholds during inference to find the balance between being cautious (higher threshold) and being inclusive (lower threshold) to reduce false positives.

Step 5: Strategies for Model Optimization

1. Increase Dataset Size:

Explanation: Provide more examples for the model to learn from.

Example: If you're training a model to recognize cats and dogs, show it pictures of various breeds, sizes, and colors to make it more adaptable.

2. Improve Labeling Accuracy:

Explanation: Ensure the labels precisely outline the objects.

Example: If you're marking a cat, make sure the box covers the cat entirely without any gaps or extra space.

3. Increase Model Complexity:

Explanation: Use a more advanced model with more parameters for better understanding.

Example: Instead of using a basic model, try a larger YOLOv8 model like yolov8m, yolov8l and yolov8x for enhanced performance.

4. Experiment with Hyperparameters:

Explanation: Adjust settings like learning rate, weight decay, and augmentation to find the best values for your data.

Example: Try teaching your model faster or slower (learning rate) or change how much it learns from each mistake (weight decay).

5. Consider Post-Processing Techniques:

Explanation: Apply additional steps after training to refine results and boost confidence.

Example: Use non-maximum suppression (NMS) with lower overlap thresholds (iou) to handle overlapping objects more effectively and increase confidence in the detection outcomes.

6. Train with Default Settings First:

Explanation: Establish a baseline performance before making major changes.

Example: Train your model initially with default settings to understand its behavior and performance.

7. Experiment with Epochs:

Explanation: Adjust the number of training cycles to prevent overfitting.

Example: If your model is learning too much from the training data and not doing well on new data, try training for fewer cycles (epochs).

8. Consider Image Size and Batch Size:

Explanation: Modify the resolution of images and batch size for optimal results.

Example: Train with the largest image size and use the largest batch size supported by your hardware for better performance.

9. Fine-Tune Hyperparameters:

Explanation:Experiment with hyperparameter settings for improved training.

Example: Adjust augmentation hyperparameters to delay overfitting and modify loss component gain hyperparameters for specific loss components.

Remember, experimentation and iteration are key. Make gradual adjustments, evaluate the impact on performance, and iterate based on the observed results.

Step 6: Deployment

For deploying your model, you have the flexibility to choose platforms and frameworks according to your needs. However, for demonstration, I recommend using Streamlit. The code for integrating YOLOv8 with Streamlit Web Real-Time Connection (WebRTC) can be found in my deployment Git repository [https://github.com/gaurang157/Ai_Object_Detection].

And check Deployment for YOLOv8: https://ai-object-detection.streamlit.app/#33bfacff

Here, replace the default model in the mentioned Git repository with the model you built.