Como melhorar as soluções de visão computacional após uma falha?

After a failure in a computer vision solution, a comprehensive analysis of the failure's root cause is essential. This involves examining misclassified examples, identifying patterns in the mistakes, and refining the model's architecture, training data, and preprocessing techniques accordingly. Iterative testing, fine-tuning, and incorporating additional data sources can progressively enhance the solution's performance, while maintaining a strong feedback loop with domain experts aids in addressing nuanced challenges and improving overall system reliability.

- Verify that the training set sufficiently represents the data the model will see once deployed. - Log all important information available during training - values of metrics and loss on training and validation set. If something goes wrong, it will help you pinpoint the problem. - Use tools like Weights & Biases to compare the value of metrics between training sessions. - Once you have finished training, benchmark your models on a test set. - Model deployment is not the end of your work. Monitor the performance of the model. Look for potential data drift. - When the model is in production, give users the opportunity to give you feedback and let you know that the prediction provided was incorrect.

Acquire more relevant data, refine algorithms, and preprocess images for clarity. Opt for advanced models, augment data diversity, and ensure accurate labeling. Fine-tune existing models and continually update the solution to adapt to changing conditions.

]This article outlines the approach in case of failure for any data science / machine learning project. In my opinion, it is fundamental to establish the causes of failure (in real world there will more than one cause), In general causes can be grouped as Data related, Model itself, Deployment environment & Use related. Using Pareto's principle, fishbone diagrams, five whys frameworks will be helpful to classify and prioritize the causes. Then address them accordingly. Well, Computer vision solutions don't work in isolation & are not one time activity. One must constantly analyze the deployment / production environment, user expectations and continuously fine tune the model to provide the consistent user experience.

1. Error Analysis: Analyze the specific errors that occurred during the failure. 2. Data Analysis: Examine the data used for training and testing. Ensure the data is diverse, representative of real-world scenarios, and covers edge cases. 3. Model Evaluation 4. Data Augmentation 5. Annotator Feedback: If available, involve human annotators to review misclassified samples and provide feedback. 6. Feedback Loop: Continuously monitor the system's performance in real-world scenarios. 7. Documentation: Keep a record of the failures, improvements, and changes made to the model. 8. Collaboration: Involve cross-functional teams, including domain experts, data scientists, and software engineers

- Remove any data entries with inaccurate labels. Using them during training will hurt your model performance. - Ensure you maintain a balanced distribution among classes. Aim to acquire additional images for classes with fewer samples. If obtaining authentic data proves challenging, employ data augmentation methods to generate synthetic data. - Use vector analysis and embeddings to locate image clusters that pose more challenges to the model. Try to understand why.

Data is the fuel of computer vision absolutely it's true,but database issues is very important too. Thanks you very much for your article

- Think about the metrics you need to optimize. If your job is to detect tumors on MRI images, prediction quality is key. Speed comes second. - Be aware of the speed-accuracy trade-off. Small models are usually faster but less accurate. Larger models provide better quality predictions but can be orders of magnitude slower. - Experiment with different architectures, hyperparameters, and data sets. - Keep logs of your training sessions and always benchmark your model on a test set. Analyze this data and conclude what you can improve.

- The issue you are trying to solve can limit the deployment scenarios you can use. For example, if the model is to operate locally aboard a drone, your only option is an edge device like Jetson Nano. - You should consider target hardware as early as the model selection stage. - The view angle of the camera can have a significant impact on the quality of the prediction. Models pre-trained on the COCO dataset do well at detecting people or cars seen from the side but not from above. - Verify whether the model can be easily converted to CoreML format if you plan to run it on an iPhone, TF Light if on Android phones, or TF.js if in a browser. - Consider using optimized runtimes like ONNX, OpenVINO, or TensoRT.

Rather than solely focusing on adapting to the deployment environment, an alternative approach could involve implementing a "serving" strategy. By adopting this approach, you aim to achieve a higher level of generalization that ensures consistent performance across various devices. This method not only simplifies the process of model updates but also obviates the need to manage individual devices or environments. Embracing a serving strategy allows you to concentrate on maintaining one optimized model that caters to all scenarios, irrespective of the specific device or platform.

Beyond the model accuracy, the success of computer vision systems hinges on camera setup and environmental conditions. Camera resolution, lens quality, and lighting directly affect input quality. Poor field of view or focus can skew results, while inconsistent lighting might create distortions. Regular calibration, camera positioning, and frame rate adjustments are also essential. In short, an optimized camera setup is as crucial as a well-tuned model for effective computer vision applications.

In my experience, for facial recognition projects before you collect data you have to ensure you obtain signed non-disclosure agreements and the participants are willing to share their personal data via a consent form. As you conduct more experiments you might need to factor extra time to collect new biometric data as more people will need to be invited to complete 30 minute sessions of testing and complete scorecard responses. This new data could be used in a deployed model. If you use Amazon Rekognition pretrained models, features could be better detected with FaceDetail object that includes a bounding box with information such as quality and attributes for gender, age, emotion and face occlusion.

After a failure in a computer vision solution, there are several steps you can take to improve it. First, analyze the root cause of the failure by reviewing the data, model architecture, and training process. Collect additional relevant and diverse training data to address any data gaps. Consider experimenting with different model architectures, hyperparameters, and augmentation techniques to enhance performance. Regularly update and fine-tune the model as new data becomes available, and ensure continuous monitoring and evaluation to catch and rectify any potential issues early on.