Model Robustness: Building Reliable AI Models 

Computer vision engineers, data scientists, and machine learning engineers face a pervasive issue: the prevalence of low-quality images within datasets. You have likely encountered this problem through incorrect labels, varied image resolutions, noise, and other distortions. Poor data quality can lead to models learning incorrect features, misclassifications, and unreliable or incorrect outputs. In a domain where accuracy and reliability are paramount, this issue can significantly impede the progress and success of projects. This could result in wasted resources and extended project timelines. Take a look at the following image collage of Chihuahuas or muffins, for example: Chihuahua or muffin? My search for the best computer vision API How fast could you tell which images are Chihuahuas vs. muffins? Fast? Slow? Were you correct in 100% of the images? I passed the collage to GPT-4V because, why not? 😂 And as you can see, even the best-in-class foundation model misclassified some muffins as Chihuahuas! (I pointed out a few.) So, how do you make your models perform better? The sauce lies in the systematic approach of exploring, evaluating, and fixing the quality of images. Enter Encord Active! It provides a platform to identify, tag problematic images, and use features to improve the dataset's quality. This article will show you how to use Encord Active to explore images, visualize potential issues, and take next steps to rectify low-quality images. In particular, you will: Use a dog-food dataset from the Hugging Face Datasets library. Delve into the steps of creating an Encord Active project. Define and run quality metrics on the dataset. Visualize the quality metrics. Indicate strategies to fix the issues you identified. Ready? Let’s delve right in! 🚀 Using Encord Active to Explore the Quality of Your Images Encord Active toolkit helps you find and fix wrong labels through data exploration, model-assisted quality metrics, and one-click labeling integration. It takes a data-centric approach to improving model performance. With Encord Active, you can: Slice your visual data across metrics functions to identify data slices with low performance. Flag poor-performing slices and send them for review. Export your new data set and labels. Visually explore your data through interactive embeddings, precision/recall curves, and other advanced visualizations. Check out the project on GitHub, and hey, if you like it, leave a 🌟🫡. Demo: Explore the quality of 'dog' and 'food' images for ML models In this article, you will use Encord Active to explore the quality of the `sashs/dog-food` images. You’ll access the dataset through the Hugging Face Datasets library. You can use this dataset to build a binary classifier that categorizes images into the "dog" and "food" classes. The 'dog' class has images of canines that resemble fried chicken and some that resemble images of muffins, and the 'food' class has images of, you guessed it, fried chicken and muffins. The complete code is hosted on Colab. Open the Colab notebook side-to-side with this blog post. Interested in more computer vision, visual foundation models, active learning, and data quality notebooks? Check out the Encord Notebook repository Use Hugging Face Datasets to Download and Generate the Dataset Whatever machine learning, deep learning, or AI tasks you are working on, the Hugging Face Datasets library provides easy access to, sharing, and processing datasets, particularly those catering to audio, computer vision, and natural language processing (NLP) domains. The 🤗 datasets library enables an on-disk cache that is memory-mapped for quick lookups to back the datasets. Explore the Hugging Face Hub for the datasets directory You can browse and explore over 20,000 datasets housed in the library on the Hugging Face Hub. The Hub is a centralized platform for discovering and choosing datasets pertinent to your projects. In the search bar at the top, enter keywords related to the dataset you're interested in, e.g., "sentiment analysis," "image classification," etc. You should be able to: Filter datasets by domain, license, language, and so on. Find information such as the size, download number, and download link on the dataset card. Engage with the community by contributing to discussions, providing feedback, or suggesting improvements to the dataset. Load the ‘sashs/dog-food’ dataset Loading the `sashs/dog-food` dataset is pretty straightforward: Install the 🤗 Datasets library and download the dataset. To install Hugging Face Datasets, run the following command: pip install datasets Use the `load_dataset` function to load the 'sasha/dog-food' dataset from Hugging Face: dataset_dict=load_dataset('sasha/dog-food') `load_dataset` returns a dictionary object (`DatasetDict`). You can iterate through the train and test dataset split keys in the `DatasetDict` object. The keys map to a `Dataset` object containing the images for that particular split. You will explore the entire dataset rather than in separate splits. This should provide a comprehensive understanding of the data distribution, characteristics, and potential issues. To do that, merge the different splits into a single dataset using the `concatenate_datasets` function: dataset=concatenate_datasets([dfordindataset_dict.values()]) Perfect! Now, you have an entire dataset to explore with Encord Active in the subsequent sections. If you have not done that already, create a dataset directory to store the downloaded images. # Create a new directory "hugging_face_dataset" in the current working dir huggingface_dataset_path =  Path.cwd() / "huggingface_dataset" # Delete dir if it already exists and recreate if huggingface_dataset_path.exists():   shutil.rmtree(huggingface_dataset_path) huggingface_dataset_path.mkdir() Use a loop to iterate through images from the ‘sashs/dog-food’ dataset and save them to the directory you created: for counter, item in tqdm(enumerate(dataset)):   image = item['image'] image.save(f'./Hugging Face_dataset/{counter}.{image.format}') If your code throws errors, run the cell in the Colab notebook in the correct order. Super! You have prepared the groundwork for exploring your dataset with Encord Active. Create an Encord Active Project You must specify the directory containing your datasets when using Encord Active for exploration. You will initialize a local project with the image files—there are different ways to import and work with projects in Encord.  Encord Active provides functions and utilities to load all your images, compute embeddings, and, based on that, evaluate the embeddings using pre-defined metrics. The metrics will help you search and find images with errors or quality issues. Before initializing the Encord Active project, define a function, `collect_all_images`, that obtains a list of all the image files from the `huggingface_dataset_path` directory, takes a root folder path as input, and returns a list of `Path` objects representing image files within the root folder: def collect_all_images(root_folder: Path) ->  list[Path]:     image_extensions = {".jpg", ".jpeg", ".png", ".bmp"}     image_paths = []     for file_path in root_folder.glob("**/*"):         if file_path.suffix.lower() in image_extensions:             image_paths.append(file_path) return image_paths Remember to access and run the complete code in this cell. Initialize Encord Active project Next, initialize a local project using Encord Active's `init_local_project` function. This function provides the same functionality as running the `init` command in the CLI. If you prefer using the CLI, please refer to the “Quick import data & labels” guide. try:     project_path: Path = init_local_project(         files = image_files,         target = projects_dir,         project_name = "sample_ea_project",         symlinks = False,     ) except ProjectExistsError as e:     project_path = Path("./sample_ea_project") print(e)# A project already exists with that name at the given path. Compute image embeddings and analyze them with metrics Analyzing raw image data directly in computer vision can often be impractical due to the high dimensionality of images. A common practice is to compute embeddings for the images to compress the dimensions, then run metrics on these embeddings to glean insights and evaluate the images. Ideally, you compute the embeddings using pre-trained (convolutional neural network) models. The pre-trained models capture the essential features of the images while reducing the data dimensionality. Once you obtain the embeddings, run similarity, clustering, and classification metrics to analyze different aspects of the dataset. Computing embeddings and running metrics on them can take quite a bit of manual effort. Enter Encord Active! Encord Active provides utility functions to run predefined subsets of metrics, or you can import your own sets of metrics. It computes the image embeddings and runs the metrics by the type of embeddings. Encord Active has three different types of embeddings: Image embeddings - general for each image or frame in the dataset Classification embeddings - associated with specific frame-level classifications Object embeddings - associated with specific objects, like polygons or bounding boxes Use the  `run_metrics_by_embedding_type` function to execute quality metrics on the images, specifying the embedding type as `IMAGE`: run_metrics_by_embedding_type(     EmbeddingType.IMAGE,     data_dir=project_path,     use_cache_only=True ) The `use_cache_only=True` parameter cached data only when executing the metrics rather than recomputing values or fetching fresh data. This can be a useful feature for saving computational resources and time, especially when working with large datasets or expensive computations. Create a `Project` object using the `project_path` - you will use this for further interactions with the project: ea_project=Project(project_path) Exploring the Quality Of Images From the Hugging Face Datasets Library Now that you have set up your project, it’s time to explore the images! There are typically two ways you could visualize images with Encord Active (EA): Through the web application (Encord Active UI) Combining EA with visualization libraries to display those embeddings based on the metrics We’ll use the latter in this article. You will import helper functions and modules from Encord Active with visualization libraries (`matplotlib` and `plotly`). This code cell contains a list of the modules and helper functions. Pre-defined subset of metrics in Encord Active Next, iterate through the data quality metrics in Encord Active to see the list of available metrics, access the name attribute of each metric object within that iterable, and construct a list of these names: [metric.nameformetricinavailable_metrics] You should get a similar output: There are several quality metrics to explore, so let’s define and use the helper functions to enable you to visualize the embeddings. Helper functions for displaying images and visualizing the metrics Define the `plot_top_k_images` function to plot the top k images for a metric: def plot_top_k_images(metric_name: str, metrics_data_summary: MetricsSeverity, project: Project, k: int, show_description: bool = False, ascending: bool = True): metric_df = metrics_data_summary.metrics[metric_name].df metric_df.sort_values(by='score', ascending=ascending, inplace=True) for _, row in metric_df.head(k).iterrows(): image = load_or_fill_image(row, project.file_structure) plt.imshow(image) plt.show() print(f"{metric_name} score: {row['score']}") if show_description: print(f"{row['description']}") The function sorts the DataFrame of metric scores, iterates through the top `k` images in your dataset, loads each image, and plots it using Matplotlib. It also prints the metric score and, optionally, the description of each image. You will use this function to plot all the images based on the metrics you define. Next, define a `plot_metric_distribution` function that creates a histogram of the specified metric scores using Plotly: def plot_metric_distribution(metric_name: str, metric_data_summary: MetricsSeverity): fig = px.histogram(metrics_data_summary.metrics[metric_name].df, x="score", nbins=50) fig.update_layout(title=f"{metric_name} score distribution", bargap=0.2) fig.show() Run the function to visualize the score distribution based on the “Aspect Ratio” metric: plot_metric_distribution(“AspectRatio”,metrics_data_summary) Most images in the dataset have aspect ratios close to 1.5, a normal distribution. The set has only a few extremely small or enormous image proportions. Use EA’s `create_image_size_distribution_chart` function to plot the size distribution of your images: image_sizes = get_all_image_sizes(ea_project.file_structure) fig = create_image_size_distribution_chart(image_sizes) fig.show() As you probably expected for an open-source dataset for computer vision applications, there is a dense cluster of points in the lower-left corner of the graph, indicating that many images have smaller resolutions, mostly below 2000 pixels in width and height. A few points are scattered further to the right, indicating images with a much larger width but not necessarily a proportional increase in height. This could represent panoramic images or images with unique aspect ratios. You’ll identify such images in subsequent sections. Inspect the Problematic Images What are the severe and moderate outliers in the image set? You might also need insights into the distribution and severity of outliers across various imaging attributes. The attributes include metrics such as green values, blue values, area, etc. Use the `create_outlier_distribution_chart` utility to plot image outliers based on all the available metrics in EA. The outliers are categorized into two levels: "severe outliers" (represented in red “tomato”) and "moderate outliers" (represented in orange): available_metrics = load_available_metrics(ea_project.file_structure.metrics) metrics_data_summary = get_metric_summary(available_metrics) all_metrics_outliers = get_all_metrics_outliers(metrics_data_summary) fig = create_outlier_distribution_chart(all_metrics_outliers, "tomato", 'orange') fig.show() Here’s the result: "Green Values," "Blue Values," and "Area" appear to be the most susceptible to outliers, while attributes like "Random Values on Images" have the least in the ‘sashs/dog-food’ dataset. This primarily means there are lots of images that have abnormally high values of green and blue tints. This could be due to the white balance settings in the camera for the images or low-quality sensors. If your model trains on this set, it’s likely that more balanced images may perturb the performance. What are the blurry images in the image set? Depending on your use case, you might discover that blurry images can sometimes deter your model. A model trained on clear images and then tested or used on blurry ones may not perform well. If the blur could lead to misinterpretations and errors, which can have significant consequences, you might want to explore the blurry images to remove or enhance them. plot_top_k_images('Blur',metrics_data_summary,ea_project,k=5,ascending=False) Based on a "Blur score" of -9.473 calculated by Encord Active, here is the output with one of the five blurriest images: What are the darkest images in the image set? Next, surface images with poor lighting or low visibility. Dark images can indicate issues with the quality. These could result from poor lighting during capture, incorrect exposure settings, or equipment malfunctions. Also, a model might struggle to recognize patterns in such images, which could reduce accuracy. Identify and correct these images to improve the overall training data quality. plot_top_k_images('Brightness', metrics_data_summary, ea_project, k=5, ascending=True) The resulting image reflects a low brightness score of 0.164: What are the duplicate or nearly similar images in the set? Image singularity in the context of image quality is when images have unique or atypical characteristics compared to most images in a dataset. Duplicate images can highlight potential issues in the data collection or processing pipeline. For instance, they could result from artifacts from a malfunctioning sensor or a flawed image processing step. In computer vision tasks, duplicate images can disproportionately influence the trained model, especially if the dataset is small. Identify and address these images to improve the robustness of your model. Use the “Image Singularity” metric to determine the score and the images that are near duplicates: plot_top_k_images('Image Singularity', metrics_data_summary, ea_project, k=15, show_description=True) Here, you can see two nearly identical images with similar “Image Singularity” scores: The tiny difference between the singularity scores of the two images—0.01299857 for the left and 0.012998693 for the right—shows how similar they are. Check out other similar or duplicate images by running this code cell. Awesome! You have played with a few pre-defined quality metrics. See the complete code to run other data quality metrics on the images. Next Steps: Fixing Data Quality Issues Identifying problematic images is half the battle. Ideally, the next step would be for you to take action on those insights and fix the issues. Encord Active (EA) can help you tag problematic images, which may skew model performance downstream. Post-identification, various strategies can be employed to rectify these issues. Below, I have listed some ways to fix problematic image issues. Tagging and annotation Once you identify the problematic images, you can tag them within EA. One of the most common workflows we see from our users at Encord is identifying image quality issues at scale with Encord Active, tagging problematic images, and sending them upstream for annotation with Annotate. Re-labeling Incorrect labels can significantly hamper model performance. EA facilitates the re-labeling process by exporting the incorrectly labeled images to an annotation platform like Encord Annotate, where you can correct the labels. Active learning Use active learning techniques to improve the quality of the dataset iteratively. You can establish a continuous improvement cycle by training the model on good-quality datasets and then evaluating the model on low-quality datasets to suggest datasets to improve. Active learning (encord.com) Check out our practical guide to active learning for computer vision to learn more about active learning, its tradeoffs, alternatives, and a comprehensive explanation of active learning pipelines. Image augmentation and correction Image augmentation techniques enhance the diversity and size of the dataset to improve model robustness. Consider augmenting the data using techniques like rotation, scaling, cropping, and flipping. Some images may require corrections like brightness adjustment, noise reduction, or other image processing techniques to meet the desired quality standards. Image quality is not a one-time task but a continuous process. Regularly monitoring and evaluating your image quality will help maintain a high-quality dataset pivotal for achieving superior model performance. Key Takeaways In this article, you defined the objective of training a binary classification model for your use case. Technically, you “gathered” human labels since the open 'sashs/dog-food' dataset was already labeled on Hugging Face. Finally, using Encord Active, you computed image embeddings and ran metrics on the embeddings. Inspect the problematic images by exploring the datasets based on the objective quality metrics. Identifying and fixing the errors in the dataset will set up your downstream model training and ML application for success. If you are interested in exploring this topic further, there’s an excellent article from Aliaksei Mikhailiuk that perfectly describes the task of image quality assessment in three stages: Define an objective Gather the human labels for your dataset Train objective quality metrics on the data

In Machine Learning, the efficacy of a model is not just about its ability to make predictions but also to make the right ones. Practitioners use evaluation metrics to understand how well a model performs its intended task. They serve as a compass in the complex landscape of model performance. Accuracy, precision, and recall are important metrics that view the model's predictive capabilities. Accuracy is the measure of a model's overall correctness across all classes. The most intuitive metric is the proportion of true results in the total pool. True results include true positives and true negatives. Accuracy may be insufficient in situations with imbalanced classes or different error costs. Precision and recall address this gap. Precision measures how often predictions for the positive class are correct. Recall measures how well the model finds all positive instances in the dataset. To make informed decisions about improving and using a model, it's important to understand these metrics. This is especially true for binary classification. We may need to adjust these metrics to understand how well a model performs in multi-class problems fully. Understanding the difference between accuracy, precision, and recall is important in real-life situations. Each metric shows a different aspect of the model's performance. Classification Metrics Classification problems in machine learning revolve around categorizing data points into predefined classes or groups. For instance, determining whether an email is spam is a classic example of a binary classification problem. As the complexity of the data and the number of classes increases, so does the intricacy of the model. However, building a model is only half the battle. Key metrics like accuracy, precision, and recall from the confusion matrix are essential to assess its performance. Metrics provide insights into how well the model achieves its classification goals. They help identify improvement areas to show if the model aligns with the desired outcomes. Among these metrics, accuracy, precision, and recall are foundational. The Confusion Matrix The confusion matrix is important for evaluating classification models. It shows how well the model performs. Data scientists and machine learning practitioners can assess their models' accuracy and areas for improvement with a visual representation. Significance At its core, the confusion matrix is a table that compares the actual outcomes with the predicted outcomes of a classification model. It is pivotal in understanding the nuances of a model's performance, especially in scenarios where class imbalances exist or where the cost of different types of errors varies. Breaking down predictions into specific categories provides a granular view of a more informed decision-making process to optimize models. Elements of Confusion Matrix True Positive (TP): These are the instances where the model correctly predicted the positive class. For example, they are correctly identifying a fraudulent transaction as fraudulent. True Negative (TN): The model accurately predicted the negative class. Using the same example, it would be correctly identifying a legitimate transaction as legitimate. False Positive (FP): These are instances where the model incorrectly predicted the positive class. In our example, it would wrongly flag a legitimate transaction as fraudulent. False Negative (FN): This is when the model fails to identify the positive class, marking it as negative instead. In the context of our example, it would mean missing a fraudulent transaction and deeming it legitimate. Visual Representation and Interpretation The diagonal from the top-left to the bottom-right represents correct predictions (TP and TN), while the other represents incorrect predictions (FP and FN). You can analyze this matrix to calculate different performance metrics. These metrics include accuracy, precision, recall, and F1 score. Each metric gives you different information about the model's strengths and weaknesses. What is Accuracy in Machine Learning? Accuracy is a fundamental metric in classification, providing a straightforward measure of how well a model performs its intended task. Accuracy represents the ratio of correctly predicted instances to the total number of instances in the dataset. In simpler terms, it answers the question: "Out of all the predictions made, how many were correct?" Mathematical Formula Where: TP = True Positives TN = True Negatives FP = False Positives FN = False Negatives Significance Accuracy is often the first metric to consider when evaluating classification models. It's easy to understand and provides a quick snapshot of the model's performance. For instance, if a model has an accuracy of 90%, it makes correct predictions for 90 of every 100 instances. However, while accuracy is valuable, it's essential to understand when to use it. In scenarios where the classes are relatively balanced, and the misclassification cost is the same for each class, accuracy can be a reliable metric. Limitations Moreover, in real-world scenarios, the cost of different types of errors might vary. For instance, a false negative (failing to identify a disease) might have more severe consequences than a false positive in a medical diagnosis. Diving into Precision Precision is a pivotal metric in classification tasks, especially in scenarios with a high cost of false positives. It provides insights into the model's ability to correctly predict positive instances while minimizing the risk of false alarms. Precision, often referred to as the positive predictive value, quantifies the proportion of true positive predictions among all positive predictions made by the model. It answers the question: "Of all the instances predicted as positive, how many were positive?" Mathematical Formula Where: TP = True Positives FP = False Positives Significance Precision is important when false positives are costly.  In certain applications, the consequences of false positives can be severe, making precision an essential metric. For instance, in financial fraud detection, falsely flagging a legitimate transaction as fraudulent (a false positive) can lead to unnecessary investigations, customer dissatisfaction, and potential loss of business. Here, high precision ensures that most flagged transactions are indeed fraudulent, minimizing the number of false alarms. Limitations Precision focuses solely on the correctly predicted positive cases, neglecting the false negatives. As a result, a model can achieve high precision by making very few positive predictions, potentially missing out on many actual positive cases. This narrow focus can be misleading, especially when false negatives have significant consequences. What is Recall? Recall, also known as sensitivity or true positive rate, is a crucial metric in classification that emphasizes the model's ability to identify all relevant instances. Recall measures the proportion of actual positive cases correctly identified by the model. It answers the question: "Of all the actual positive instances, how many were correctly predicted by the model?" Mathematical Formula: Where: TP = True Positives FN = False Negatives Significance Recall is important in scenarios where False Negatives are costly. Example: Similarly, a high recall ensures that most threats are identified and addressed in a security system designed to detect potential threats. While this might lead to some false alarms (false positives), the cost of missing a genuine threat (false negatives) could be catastrophic. Both examples emphasize minimizing the risk of overlooking actual positive cases, even if it means accepting some false positives. This underscores the importance of recall in scenarios where the implications of false negatives are significant. Limitations The recall metric is about finding all positive cases, even with more false positives. A model may predict most instances as positive to achieve a high recall. This leads to many incorrect positive predictions. This can reduce the model's precision and result in unnecessary actions or interventions based on these false alarms. 💡 Recommended: The 10 Computer Vision Quality Assurance Metrics Your Team Should be Tracking.  The Balancing Act: Precision and Recall Precision and recall, two commonly used metrics in classification, often present a trade-off that requires careful consideration based on the specific application and its requirements. The Trade-off Between Precision and Recall There's an inherent trade-off between precision and recall. Improving precision often comes at the expense of recall and vice versa. For instance, a model that predicts only the most certain positive cases will have high precision but may miss out on many actual positive cases, leading to low recall. This balance is crucial in fraud detection, where missing a fraudulent transaction (low recall) is as critical as incorrectly flagging a legitimate one (low precision). Precision vs. Recall The Significance of the Precision-Recall Curve The precision-recall curve is a graphical representation that showcases the relationship between precision and recalls for different threshold settings. It helps visualize the trade-off and select an optimal threshold that balances both metrics.  It is especially valuable for imbalanced datasets where one class is significantly underrepresented compared to others. In these scenarios, traditional metrics like accuracy can be misleading, as they might reflect the predominance of the majority class rather than the model's ability to identify the minority class correctly.  The precision-recall curve measures how well the minority class is predicted. The measurement checks how accurately we make positive predictions and detect actual positives. The curve is an important tool for assessing model performance in imbalanced datasets. It helps choose an optimal threshold that balances precision and recall effectively. The closer this curve approaches the top-right corner of the graph, the more capable the model is at achieving high precision and recall simultaneously, indicating a robust performance in distinguishing between classes, regardless of their frequency in the dataset. Precision Recall Curve Importance of Setting the Right Threshold for Classification Adjusting the classification threshold directly impacts the shape and position of the precision-recall curve. A lower threshold typically increases recall but reduces precision, shifting the curve towards higher recall values. Conversely, a higher threshold improves precision at the expense of recall, moving the curve towards higher precision values.  The precision-recall curve shows how changing thresholds affect precision and recall balance. This helps us choose the best threshold for the application's specific needs. Precision vs. Recall: Which Metric Should You Choose? The choice between precision and recall often hinges on the specific application and the associated costs of errors. Both metrics offer unique insights, but their importance varies based on the problem. Scenarios Where Precision is More Important Than Recall Precision becomes paramount when the cost of false positives is high. For instance, consider an email marketing campaign. If a company has many email addresses and pays a high cost for each email, it is important to ensure that the recipients are likely to respond. High precision ensures that most emails are sent to potential customers, minimizing wasted resources on those unlikely to engage. Scenarios Where Recall is More Important Than Precision Recall takes precedence when the cost of missing a positive instance (false negatives) is substantial. A classic example is in healthcare, specifically in administering flu shots. If you don't give a flu shot to someone who needs it, it could have serious health consequences. Also, giving a flu shot to someone who doesn't need it has a small cost. In such a scenario, healthcare providers might offer the flu shot to a broader audience, prioritizing recall over precision. Real-World Examples Illustrate the Choice Between Precision and Recall Consider a weekly website with thousands of free registrations. The goal is to identify potential buyers among these registrants. While calling a non-buyer (false positive) isn't detrimental, missing out on a genuine buyer (false negative) could mean lost revenue. Here, high recall is desired, even if it compromises precision. In another scenario, imagine a store with 100 apples, of which 10 are bad. A method with a 20% recall might identify only 18 good apples, but if a shopper only wants 5 apples, the missed opportunities (false negatives) are inconsequential. However, a higher recall becomes essential for the store aiming to sell as many apples as possible. Classification Metrics: Key Takeaways Evaluation Metrics: Accuracy, precision, and recall remain foundational in assessing a machine learning model's predictive capabilities. These metrics are especially relevant in binary and multi-class classification scenarios, often involving imbalanced datasets. Accuracy: Provides a straightforward measure of a model's overall correctness across all classes but needs to be more accurate in imbalanced datasets, where one class (the majority class) might dominate. Change: Mentioned "majority class" to address "imbalanced datasets." Precision vs. Recall: Precision, highlighting the true positives and minimizing false positives, contrasts with recall, which focuses on capturing all positive instances and minimizing false negatives. The choice depends on the application's specific needs and the cost of errors. Confusion Matrix: Categorizes predictions into True Positives, True Negatives, False Positives, and False Negatives, offering a detailed view of a model's performance. This is essential in evaluating classifiers and their effectiveness. Precision-Recall Curve: Showcases the relationship between precision and recall for different threshold settings, which is crucial for understanding the trade-off in a classifier's performance. Classification Threshold: Adjusting this threshold in a machine learning model can help balance precision and recall, directly impacting the true positive rate and precision score. Context is Key: The relevance of precision, recall, and accuracy varies based on the nature of the problem, such as in a regression task or when high precision is critical for the positive class.

Labelbox is a popular data labeling platform, offering tools for various industries and use cases.  Labelbox labels data like images, text, and documents, making it a good choice for AI and machine learning projects. Key features include data labeling, quality assurance, integration with machine learning frameworks and data management tools, and an intuitive interface.  Yet, Labelbox does come with its own set of constraints, including issues with native video rendering, restricted DICOM compatibility, and a pricing structure that may not adapt effectively to scalability. For these reasons, we will explore alternatives to Labelbox.  Encord Encord is a leading alternative platform to build annotation workflows, curate visual data, find and fix data errors, and monitor model performance. Key Features and Benefits of Encord: Encord is a state-of-the-art AI-assisted labeling and workflow tooling platform enriched by micro-models, ideal for various annotation and labeling use cases, QA workflows, and training computer vision models. Specifically designed for computer vision applications, Encord offers native support for a wide array of annotation types, such as bounding box, polygon, polyline, instance segmentation, keypoints, classification, and much more. Encord provides use-case-specific annotations, ranging from native DICOM and NIfTI annotations for medical imaging to specialized features catering to SAR (Synthetic Aperture Radar) data in geospatial applications. Integrated MLOps workflows for computer vision and machine learning teams — to detect edge cases and gaps in your training data and generate augmented data to improve label quality. Streamlined collaboration, annotator management, and quality assurance workflows facilitate precise tracking of annotator performance and elevate label quality. Robust security functionality — label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. An advanced Python SDK and API access, coupled with effortless export capabilities in JSON and COCO formats, enhance flexibility and integration with external systems. Auto-find and fix dataset biases and errors like outliers, duplication, and labeling mistakes. Integrated tagging for data and labels, including outlier tagging. Employs quality metrics (data, label, and model) to assess and improve ML pipeline performance across data curation, data labeling, and model training. iMerit iMerit is a data labeling service provider known for its annotations and management solutions. Unlike traditional labeling platforms, iMerit offers a service-based approach to data annotation. iMerit Key Features and Benefits Customizable solution for annotation, analysis, categorization, segmentation needs. Get insights from metrics such as the annotator's working hours, the number of objects per hour and more. iMerit also provides a free trial for it’s users, but has no mention of it’s pricing plan on it’s website. iMerit’s user interface may be less intuitive and user-friendly for beginners.  TELUS International TELUS International, formerly Playment, is a Labelbox alternative that focuses on specialized data labeling services, offering features tailored to specific use cases, ensuring user comfort. TELUS International Key Features and Benefits TELUS International allows the creation of custom data labeling workflows, ensuring that even the most specialized projects can be accommodated. The platform has review and feedback loops to maintain the accuracy of annotations. CX support in 50+ languages across all traditional and digital channels. Integration with other tools and platforms, allows workflow management and collaboration.  These features allow to accommodate the growing needs of businesses, ensuring that the platform can handle increasing data volumes and complexity.  There are limited integration options with other third-party software and systems, which may hinder the ability to streamline processes across different platforms.  Potential challenges in adapting to the training data platform's interface and functionalities, requiring additional training datasets and support for users to fully utilize its capabilities.  CVAT CVAT, or Computer Vision Annotation Tool, is an open-source platform tailored for data annotation, particularly in the field of computer vision. It stands out as a community-driven solution for data labeling. CVAT's Key Features and Benefits It's a fantastic choice for startups, research projects, and academic initiatives, thanks to its open-source nature.  CVAT is a cost-effective and highly adaptable alternative to Labelbox Being open-source, CVAT encourages community contributions and customization. It's a collaborative tool, making it accessible for a wide range of users, from newbies to pro. The process of dataset curation, annotation, training, and dataset improvement is the heart of data-centric AI. CVAT has capabilities for bounding boxes, polygons, and keypoint labeling. Users can adapt CVAT to their specific needs, through custom plugins, tailored workflows, or support for new data types. While CVAT offers a wide range of annotation tools, it does not have all the advanced features that some users may require for their specific annotation tasks.