Wie wendet man Computer-Vision- und KI-Modelle auf neue Szenarien an?

- Identify key metrics. Do you prioritize model speed or prediction quality? - Identify key factors affecting the model when it will be deployed. For example, whether you will have internet access. If not, the model will have to run on an edge device and this will limit the number of architectures you can use. - Try to verify your solution on a simplified version of the problem. Instead of 50 classes, try first building a model that detects 2.

Computer vision models are different than normal AI models, since there's more factors involved. Here's some factors that affect the problem: - Data Diversity. You need diverse data to generalize for a use case. You may even need to split up training scenarios for each CV use case. - Labeling Strategy. Accurate data labeling is crucial. Think of ways to automate labeling, but also ways to include humans in the loop. Labeling also needs clearly defined labeling guidelines. - Equipment. Not all cameras are the same. Assess and take stock of cameras being used in the problem. - Real World Conditions. Understanding the environments the cameras or images are taken in. Light, dirt, time of day matter.

Beyond the immediate problem and data, you must understand the broader context of how and where your model will make inferences. Determining the environment for model inference is crucial. Will the model make inferences on-device (at the edge) or rely on cloud infrastructure? Understanding this will affect everything from the model's complexity to its required processing power. Additionally, you must be clear on the service level agreements associated with the model. What are the expectations regarding accuracy, latency, throughput, and the overall footprint of the model? Factoring in these operational and deployment considerations early on will save significant time and resources as the project progresses.

Hey there Applying computer vision and AI to new scenarios involves adapting pre-trained models, fine-tuning them on relevant data, and iteratively refining. It's about innovation through tech evolution. Cheers, Arjun Kapadia

To apply computer vision and AI models to new scenarios, you need to first understand the problem you want to solve and the data you have. Once you understand the problem, you can start to explore different techniques and models. It's important to be creative and experiment until you find a solution that works.

While selecting a model, there are various factors to be considered, these include: 1. Use case/downstream task: Start by asking questions like what problem you are trying to solve. 2. Dataset: Consider the dataset on which you are planning to train the model. Can you find a model trained on a similar dataset? If yes, use transfer learning for better performance and efficient training. 3. Complexity of the problem: Simple problems can be solved via a simple model. Not everything requires a transformer or a large model. 4. Hardware for training and deployment: Smaller the number of parameters in the model, the faster will be the inference time. Also, it will take up lesser space. 5. Understand failure modes in the model

Start with a simple model first. Create a simple CNN or even a logistic regression if the task is simple. There needs to be a baseline to build a foundation for the next model or to test scaling. Going for a large model right away can lead to problem and cost overruns. Pretrained models are great in certain circumstances, but you need to really check them against use cases. To choose the right model, you need to run multiple experiments against a base model. Save those using experiment tracking and model versioning. Then compare metrics. This really helps.

- When choosing a model, look for the easiest way to solve the problem. - Consider how the model will be deployed. For example, if the project will run on an edge device, the model must be lightweight and not require too much computing power. - Study the community around the model. Make sure the project is still supported. - Last but not least, model license. See if you can use the model in your project. Many licenses prohibit the use of the model in a closed-source project.

When choosing a model, it's crucial to prioritize a robust dataset over selecting the best model, which should align with the intended application and designated hardware. Compatibility with various platforms, ease of integration, and support from actively maintained projects are all essential considerations. Also, reviewing licensing terms and community activity and support can provide valuable insights.

Combining the strengths and weaknesses of every model with an insightful EDA may be the best path to move on. Always check with library has the best forum or community due to the fact that your problem may have been solved earlier. Try to push your dataset to the limit in terms of bytes (arrays, tensors, matrixes, type of numbers, among others) without sacrificing information.

Human in the loop can really help here. AI models can automatically detect obvious noise or outliers in images on predefined rules - but they are terrible at context. While AI can initially clean, label, augment, and split data? You need human oversight to correct subtle errors and precisely labeling. Having humans in the loop, also address model edge cases and provide iterative feedback. If you prepare the data solely using AI models, it may generate exponential noise without a human as a guardrail.

- Ensure the high quality of the data you use for training. This is more important than choosing a SOTA model. - Sew Outliers. Sift out incorrectly labeled data entries. Using them during training will reduce the quality of the final model. - Pay attention to the proper balance of classes. Try to get more photos for classes that are underrepresented. If you can't get real data, use augmentation techniques to produce artificial ones. - Split your data into training, validation, and test sets. Train your model using the training set. Validate and tune your model using the validation set. Once you're satisfied with your model's performance on the validation set, evaluate its performance using the test set.

Data augmentation is another consideration during training, especially when working with limited datasets. You can synthetically expand your training data by introducing minor alterations and transformations to your images—like rotations, shifts, and zooms. This diversifies the data input to your model and aids in generalization, reducing the chances of overfitting. Moreover, regularization techniques such as dropout or L2 regularization can be incorporated into your model architecture to prevent it from fitting noise in the data.

An often underestimated step of the process, data preparation is key -also for computer vision. I would not be surprised if a good project require 80% of data preparation and testing/tuning for preliminary performance assessment before going in production.

The key to applying computer vision and AI models to new scenarios is to prepare the data carefully. You need to clean, label, augment, and split your data to ensure that it is of high quality and representative of the new scenario.

After training, evaluating the model in real-world scenarios or with unseen data is a must to ensure its robustness. Look into early stopping during training, which halts the process if the model's performance doesn't improve after a set number of epochs. This can save time and computational resources. Additionally, regular checkpoints can save the model's state at different stages, allowing you to revert to the best version if needed. Fine-tuning the model based on the initial results can lead to better performance. Lastly, always be open to iterating on your model. Retraining and adjusting your model can keep it at peak performance as new data becomes available or the problem domain evolves.

- When training a model, log helpful information acquired during training and measure the results. - Use loggers like Weights & Biases to easily store and compare statistics from different training runs. - Compare model performance on the training and validation sets during training. If the loss value for a validation set begins to rise at some point, it may be a sign that your model has started to overfit. You've been training too long. - After completing the training, benchmark your model on a test set containing images the model has never seen before. Pay attention to potential data leakage. - Use appropriate metrics. Check that the model works equally well for different classes with datasets.

Best advice I got during my learning: 🔹 Gather a large dataset of images or videos that represent the new scenario. 🔹 Label the data with the desired output. Train a computer vision or AI model on the labeled data. 🔹 Test the model on a held-out dataset to evaluate its performance. 🔹 Deploy the model to production and monitor its performance.

- Deployment strategy is one of the factors you should consider at the model selection stage. For example, if you know that the model will run on an edge device, you must choose a small and fast enough model, sometimes at the expense of its accuracy. - Look for strategies to make your model more efficient. You can try quantization, pruning, or faster runtimes like TensorRT. - When the model goes into production, monitor it. Often, we find that small changes like different lighting conditions can cause drastic deterioration in the performance of Computer Vision models. - Improve your model over time. Collect data examples where your model has performed poorly. Manually verify them and use them in the next iteration of training.

Transitioning computer vision and AI models to new scenarios necessitates a systematic approach that balances innovation with methodical adaptation. As you delve into uncharted territory, consider the nuances of the novel scenario and how they align with the capabilities of your chosen models. It's imperative to meticulously analyze potential disparities between the original training environment and the new scenario, accounting for variations in lighting, context, or object appearances. This pre-emptive analysis guides your selection of appropriate pre-processing techniques and data augmentation strategies.

Expert advice that all need to follow: ◉ Gather data: Collect data from the new scenario to train your model. ◉ Clean data: Clean the data to remove noise and errors. ◉ Choose the right model: Select a model that is appropriate for the new scenario. ◉ Train the model: Train the model on the cleaned data. ◉ Deploy the model: Deploy the model to the new scenario. ◉ Monitor the model: Monitor the model's performance and make adjustments as needed.

Environmental conditions are so critical. Even dirt on a lens matters. The best way sometimes is to actually look at the cameras and environment they're in yourself. You may pick up on environmental factors that aren't obvious in your image data. This helps for labeling, reducing image noise, or even pointing the way when you do EDAs or decide on preprocessing. Its a cheap simple way that's solved a lot of computer vision problems for me.