Hello everyone,
I’m working on a project for object detection and positioning of clamps in a CNC environment using the YOLOv11 model. The challenge is to identify three different types of clamps which also vary in size. The goal is to reliably detect these clamps and validate their position.
However, I’m unsure about how to set up the image capture for training the model. My questions are:
- How many images do I need to reliably train the YOLOv11 model?
Do I need to collect thousands of images to create a robust model, or is a smaller dataset sufficient if I incorporate variations of the clamps?
- Which angles and perspectives should I consider when capturing the clamp images?
Is a frontal view and side view enough, or should I also include angled images? Should I experiment with multiple distances to account for the size differences of the clamps?
- Should the distance from the camera remain constant for all captures, or can I work with variable distances?
If I vary the distance to the camera, the size of the clamp in the image will change. Will YOLOv11 be able to correctly recognize the size of the clamp, even when the images are taken from different distances?
I’d really appreciate your experiences and insights on this topic, especially regarding image capture and dataset preparation.
Thanks in advance!
Hello Aminov99,
Thanks for reaching out with your questions about preparing your dataset for YOLO11 clamp detection in a CNC environment.
Regarding the number of images, while there’s no magic number, the Data Collection and Annotation guide suggests starting with at least a few hundred annotated objects per class for effective transfer learning. Since you have three clamp types with size variations, aim for diverse examples covering these variations. Quality and diversity, representing the actual conditions, are key.
For image capture, diversity is crucial for robustness. As highlighted in discussions on high-quality datasets, capturing clamps from various angles (frontal, side, angled views) and multiple distances is recommended. This helps the model generalize to different perspectives and apparent sizes it might encounter.
Using variable distances during capture is beneficial. It trains the model to recognize clamps regardless of their size in the image due to distance changes. YOLO11 can handle objects at different scales, especially if trained on a dataset that includes this variability. The training process itself, often involving parameters like imgsz, helps normalize inputs, and built-in augmentations further aid scale invariance. You can find more about how YOLO11 handles image sizing in the Data Preprocessing guide.
Focus on capturing images that reflect the real operational environment, including variations in lighting, backgrounds, clamp sizes, angles, and distances. Good luck with your project!
Hello Paula,
Thank you so much for your quick and helpful response! The information is very valuable, and I truly appreciate it. In relation to your answer, I have one small question regarding the positioning of the clamps and the hole:
As mentioned earlier, each clamp can be positioned in four different ways, but only one position is correct. The question I have is whether it would be helpful to explicitly mark not only the clamp but also the thread and the hole (with bounding boxes) during the image annotation, to ensure that the model not only detects the clamp but also validates the correct positioning based on these features, and then labeling each image, for example, as „Clamp 1 - Correctly Positioned“ or „Clamp 2 - Incorrectly Positioned“?
Thank you again for your support, and I look forward to your response!
Best regards,
Let’s pretend for a minute that you have fully trained model that works exactly as you want it to. Things to think about:
- Where will the model get used and how will the camera be set up?
- Will there be a single view for the camera when the model is in use?
- Could the distance vary significantly from the camera to the target object?
- What parts of the object will be visible from the camera’s placement?
When it comes to training a model for a specific detection task, there are usually constraints on the set up that will help guide the data collection process. My advice, from experience, is to set up your image capture as if you had a model ready to go and begin capturing real data. That will be the best method for you to get the data you’ll need for training.
Also, I’ll share a word of caution around the term “size” when it comes to vision models. The way models work is usually independent of object size. That means that size likely won’t be a significant factor in identification, even if it’s actually useful. As an example, a model trained on detecting cars, will detect a toy car just as well as a real car, the size makes no difference.
Since the clamps only (or mostly) vary by size, it’s not likely that they will be easily distinguishable using a vision model, however the threads are probably the biggest differentiator (from your images) that might make it feasible. Label the entire object, threads and all to use for training. If it’s possible to mark the different size clamps, that would also be helpful, but it would have to be visible to the camera and obviously not interfere with the clamp mechanism.
Assuming there’s only a single camera view, you could try capture images with various lighting conditions and/or objects in or around the clamps. You may even want to install the clamps “incorrectly” for labeling, but that might not be terribly helpful depending on your overall goal.
