First off, I’m very new to this, but I’m excited to get started. Maybe I’m biting off a bit more than I can chew right now, but I want to push forward anyway. No real purpose here other than trying to do something.
I want to use Fast AI to be able to take an image and tell me which Nintendo Switch game cartridges are in the image. I have a large collection of individual game cartridge images, all well labeled. Each image is a fairly reasonable close-up of the each cartridge. It’s pretty easy building something in Fast AI to recognize a single cartridge it has seen before, but I’m struggling with getting it to recognize multiple cartridges in one image (which would make sense because the model hasn’t seen multiple in a single image). I decided to build a random image stitcher to stitch random single cartridges in to a collage of multiple cartridges, then train on that. The model is now performing better, but I can’t help but wonder if I’m trying to solve this the wrong way. Any suggestions?
here are a few suggestions that might help refine your approach or provide alternative ideas:
- Dataset Expansion: Instead of randomly stitching single cartridges together, try creating more realistic scenarios. For instance, simulate images where cartridges are placed side by side, partially overlapping, or in different orientations. This can better mimic real-world scenarios and improve the model’s ability to detect multiple cartridges in various configurations.
- Bounding Boxes or Masks: When labeling your dataset, consider using bounding boxes or masks to annotate the location of each cartridge in the image. This way, you can train the model not just to classify which cartridges are present but also to localize them accurately.
- Object Detection Models: Instead of relying solely on classification models, consider using object detection models like Faster R-CNN, YOLO, or SSD. These models are designed to detect and localize multiple objects within an image, which aligns well with your requirement to identify multiple cartridges.
- Transfer Learning: If you haven’t already, consider leveraging pre-trained models like those available in the Fast AI library or using transfer learning from models pre-trained on large datasets like ImageNet. Fine-tuning these models on your specific dataset can often lead to faster and more accurate convergence.
- Data Augmentation: Use data augmentation techniques such as rotation, scaling, flipping, and adding noise to further diversify your dataset. This can help the model generalize better to different orientations and conditions.
- Evaluation and Iteration: Continuously evaluate your model’s performance on a separate validation set. Analyze where it struggles (e.g., specific orientations, overlapping cartridges) and adjust your training strategy accordingly.
- Community and Resources: Engage with communities like the Fast AI forums, GitHub repositories, or Stack Overflow for specific questions and feedback. You might find others who have tackled similar challenges and can offer valuable insights.