Boosting Skull Detection: A New AI Model Attempt

Hey guys! So, we've been on a mission to crack the code of accurate skull detection using Computer Vision, and I've got an update on our latest project. Remember our initial AI skull detector? Well, it was clocking in at a modest 30% accuracy rate. Not exactly what we were aiming for, right? That's why I dove back in, determined to build a better model. The goal? To drastically improve our skull detection capabilities. This time, I took a different approach, hoping to achieve more reliable results. Let's dive into the details of my process, the challenges I faced, and where we're at now.

Rethinking the Data: A New Database Structure for Enhanced Accuracy

First off, I recognized that our data organization could be a bottleneck. To optimize the training process, I restructured our database. I knew this was crucial for the model's learning capabilities and subsequent accuracy. Here's how I set it up:

1. Canid Skulls Folder: This became our main directory, containing everything related to the project.
2. Test, Val, and Train Subfolders: These folders were created to hold our dataset divided into testing, validation, and training sets. This division is vital in machine learning, to evaluate the model's performance on unseen data and ensure it generalizes well to new, real-world scenarios. Each of these subfolders contained the different species of canid skulls.
   • test/: Where we'd hold a subset of our data to test the model after training. Inside the test folder, were subfolders for dog_skull, coyote_skull, wolf_skull, and fox_skull.
   • val/: The validation set, used to fine-tune the model during training. The structure mirrored the test set.
   • train/: The core of our dataset, also mirroring the structure of the test and validation sets. This is where the model would learn to recognize skull patterns.

By organizing the data this way, I aimed to provide the model with a clear, structured learning environment. This is super important because it directly impacts how well the model learns and how accurate it becomes. The careful structure makes it easier for the AI to understand the different types of skulls and the variations within each type.

Data Sourcing: Zoo Images Only for Data Integrity

One of the most critical aspects of any machine-learning project is the quality of the data. To ensure the integrity of our dataset, I decided to use only images from the zoo. This approach offered several key advantages. I avoided images from the internet to ensure there wouldn't be any inaccurate images to train the model, which is often a problem. Why? Because images from the internet can be mislabeled or of inconsistent quality. That kind of inconsistency can seriously mess up the learning process of the AI, ultimately reducing the accuracy of the model.

Using images from the zoo gave me greater control over the dataset's quality. High-quality images with accurate labels are essential for training the model effectively. By limiting the data source, I increased the chances that the model would be trained on reliable and consistent data, crucial for achieving better accuracy. This also meant that I could have more confidence in the results and the model's ability to accurately identify skulls in real-world scenarios.

The Training Process: From Google Collab to Visual Studio Code

Next, the real fun began: coding the training process. I started out on Google Collab. It's a great platform to start with, since it's accessible and offers enough computing power to handle initial model training.

I kicked things off by downloading the dataset and all the necessary dependencies. Then, I loaded the data and started configuring the model to begin the training process. I also started working on the code to handle the data loading, model architecture, and training loops. Things seemed to be progressing well. But I ran into a bit of a snag, and the issue was related to RAM storage and usage. It turns out that Google Collab, although useful, has its limitations. And the limited RAM capacity was proving to be a real bottleneck.

After a bit of detective work, I realized the problem wasn’t with my code itself, but with Google Collab's constraints. It didn’t have the horsepower to handle the demands of the training process. That's when I decided to switch over to Visual Studio Code (VS Code). This move was essential. VS Code offers a more robust environment, with better debugging tools and more control over the hardware resources. This allowed me to fine-tune the process and solve those RAM usage issues.

Debugging and Beyond: Current Status and Future Steps

I'm now in the process of debugging the code in Visual Studio Code. This involves a lot of trial and error, but it's crucial to refine the model. Debugging is a fundamental part of the machine-learning pipeline. I'm carefully reviewing the code, checking for errors, and fine-tuning the model's parameters to optimize its performance. I'm focusing on the model's architecture, training parameters, and data preprocessing steps. I'm using VS Code's debugging tools to step through the code, inspect variables, and identify any issues.

Once the debugging is complete, the next steps include: 1) Training the Model: With the code running smoothly, the next step is to train the model using our dataset. This is where the model will learn to recognize skull patterns. The training process involves feeding the dataset to the model and adjusting its parameters to minimize errors. I'll use the training, validation, and testing datasets to measure its accuracy, precision, and recall. 2. Evaluation: After training, I’ll evaluate the model's performance using our test dataset. This involves running the model on the test data and measuring how well it can predict skull types. The evaluation metrics will provide insights into the model's performance and help me to identify areas for improvement. I will then analyze the results, looking at accuracy, precision, and recall metrics to understand how well the model can distinguish between different skull types. This helps us ensure the model isn't just learning to fit the training data but can generalize to new, unseen data. 3. Iterative Refinement: The process is iterative, meaning I'll be constantly refining the model. I will review the results, analyze the errors, and make adjustments to the model architecture, training parameters, or dataset. This iterative approach is crucial for achieving the best results.

So, that's where we're at, guys! I'm super pumped about the potential of this new model. I'll keep you posted as I continue to refine the model and hopefully get some much-improved results on skull detection. Stay tuned for the next update, where I'll share the performance metrics and any adjustments I've made. Wish me luck! Keep your eyes peeled for the next update, and I'll share the performance metrics and any adjustments I've made. Thanks for following along!