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True Positive, False Positive ,False Negative, True Negative #12555
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@Shanky71 true Positive (TP): Correctly predicted object by YOLOv5 💯 For more details, check out the YOLOv5 docs at https://docs.ultralytics.com/yolov5/ 📚 |
@glenn-jocher what about the misclassification and wrong bounding box? Under which category will it lie. I'm a bit confused about that |
@Shanky71 Misclassification and wrong bounding boxes typically fall under false positives, as they represent incorrect predictions made by the model. For more details, you can refer to the YOLOv5 documentation at https://docs.ultralytics.com/yolov5/ 📚 |
@glenn-jocher I am confused how you are defining the wrong bounding boxes as the yolov5 with generate multiple bounding box. Why so much difference ? |
@Shanky71 During evaluation, misclassification and wrong bounding boxes can be part of false positives. The difference in percentages can arise from the nuanced definitions and evaluations of misclassification, wrong bounding boxes, and false positives. The high IOU value of 0.995 indicates an accurate localization between annotations and YOLOv5 detections, emphasizing the model's ability to generate precise bounding boxes. Keep in mind that percentages can vary based on specific use cases, dataset characteristics, and evaluation methodologies. For in-depth analysis, it's recommended to refer to the YOLOv5 documentation, and consider engaging the YOLO community for diverse perspectives. If you have further questions, the Ultralytics team and the YOLO community are here to help! |
@glenn-jocher Thank you for your kind help. So does it mean there is no way to find the % for wrong bounding box and misclassification separately? |
@Shanky71 That's correct! In object detection, the traditionally used metrics like True Positive, False Positive, False Negative, and True Negative do not directly provide separate percentages for wrong bounding boxes and misclassifications. These nuances are typically subsumed within the false positive category. To understand these aspects separately, custom evaluation methodologies and metrics may need to be developed based on specific use cases and requirements. For further details and insights, feel free to explore the YOLOv5 documentation at https://docs.ultralytics.com/yolov5/ and engage with the YOLO community. We're here to support you! |
@glenn-jocher Thank you so much for clearing my doubts. Thanks for your kind help and time. |
You're welcome, @Shanky71! I'm glad I could help. If you have any more questions in the future, don't hesitate to ask. The YOLOv5 community and the Ultralytics team are here to support you. Good luck with your projects! 😊👍 |
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@glenn-jocher Hey I have a small doubt
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Hello @Shanky71! Let's address your questions one by one:
Remember, the YOLOv5 community and the Ultralytics team are always here to help. If you have further questions or need clarification, feel free to reach out! |
@glenn-jocher Thanks Glenn for the help. Just a quick clarification, is it possible to draw the PR curve for all classes instead of individual class separately without training the data again. Also you have given a results.csv can it be used for it? If yes can you help me out through this? Also can you help me in understanding the importance of mAP@0.5 and mAP@0.5:0.95? Which one is more useful and how can we understand them so that we can differentiate between them. |
Certainly, @Shanky71! Let's dive into your queries:
Which one is more useful? It depends on your application:
Both metrics are important and provide valuable insights into different aspects of the model's performance. For a comprehensive evaluation, it's advisable to consider both. If you have more questions or need further assistance, feel free to reach out. The YOLOv5 community and the Ultralytics team are here to support you! |
Hey @glenn-jocher, |
Hello @Shanky71, Skipping validation during training and only validating after training completes would indeed save time and computational resources during the training process. However, there are several reasons why performing validation at regular intervals during training is beneficial and often crucial:
Skipping validation and using the validation set as a test set at the end of training would not provide these benefits. The test set should ideally be a completely separate dataset that the model has never seen during training or hyperparameter tuning. Its purpose is to evaluate the final model's performance and generalize ability to new, unseen data. If computational resources are a concern, you might consider validating less frequently (e.g., every few epochs instead of after every single epoch) or using a smaller subset of the validation data for quicker evaluations. However, completely skipping validation until the end of training is generally not recommended for the reasons mentioned above. I hope this clarifies your doubts! If you have further questions, feel free to ask. |
Yeah it clears my doubt , thanks for the quick help @glenn-jocher . Performance metrics (Precision, Recall etc.) should be considered after training/validation/test? Which one will describe my model well? |
You're welcome, @Shanky71! I'm glad to hear that your doubts are cleared. Regarding performance metrics like Precision, Recall, and others, here's how and when they should be considered:
Which one will describe my model well? Metrics from both validation and test phases are important, but for different reasons:
Always ensure that your test dataset is well-curated and representative of the real-world scenarios where the model will be deployed. This ensures that the test metrics accurately reflect the model's effectiveness and usability in practical applications. If you have any more questions or need further assistance, feel free to reach out. Happy to help! |
Hey @glenn-jocher Thanks for your valuable time and help. |
You're welcome, @Shanky71! I'm here to help. If you've divided your data into only training and testing sets, without a separate validation set, it's crucial to maintain the integrity of the testing process to ensure that the evaluation of your model is unbiased and reflects its true performance. Here are some guidelines:
Remember, the goal of the test set is to simulate how well your model performs on data it has never encountered, mimicking real-world conditions as closely as possible. Keeping the test set strictly for final evaluation helps maintain the integrity of this simulation. If you have any more questions or need further clarification, feel free to ask. Good luck with your model development! |
Hey @glenn-jocher How should I modify it such that it such that only training can be done without validation and I can just test it once. Also I am interested in obtaining the metrics like precision , recall, map@0.5 etc for the test images |
Hello @Shanky71, Yes, you can use To obtain detailed evaluation metrics on a test set without using a validation set during training, you can follow these steps:
Remember, the distinction between validation and test datasets is in how they're used during the model development process. The validation set is used to tune the model and make decisions about hyperparameters, while the test set is used to evaluate the model's final performance. If you skip validation, be cautious not to inadvertently tune your model based on test set performance, as this would bias your evaluation. If you have further questions or need more assistance, feel free to reach out. Good luck with your testing! |
@glenn-jocher Thank you so much for helping me throughout the process. I really appreciate that. Just a small doubt here again. After doing all of this I tried running that, it was running on my CPU instead of my GPU. How can I make it run on GPU instead of CPU. I already used --device 0 in the code snippet still it's running on CPU? Any comments why it's happening? import utils it shows me CPU instead of GPU. How to change that YOLOv5 2024-3-6 Python-3.11.5 torch-2.1.2 CPU |
I'm glad to have been able to help you, @Shanky71. Regarding your issue with the model running on the CPU instead of the GPU, let's address that. The fact that YOLOv5 is defaulting to CPU despite specifying
If after checking these points you're still facing issues, it might be helpful to create a new environment and reinstall the necessary packages, ensuring compatibility between PyTorch, CUDA, and your system's drivers. Remember, successfully running on GPU significantly speeds up both training and inference processes, so it's worth ensuring everything is correctly set up. If you continue to encounter difficulties, consider seeking help with specific error messages or system configurations, as the issue might be more nuanced. I hope this helps you resolve the issue! If you have any more questions or need further assistance, feel free to ask. |
Hey @glenn-jocher , the training started but it ended abruptly. I am still not able to figure how to rectify it Starting training for 1 epochs...
Traceback (most recent call last): CPU: registered at C:\actions-runner_work\vision\vision\pytorch\vision\torchvision\csrc\ops\cpu\nms_kernel.cpp:112 [kernel] |
@Shanky71 it looks like you're encountering a Here are a few steps to troubleshoot and potentially resolve this issue:
Remember, the key to resolving such issues often lies in ensuring compatibility between PyTorch, torchvision, and CUDA. Keeping everything up-to-date and aligned with the versions supported by your hardware usually helps avoid such errors. If you need further assistance, feel free to ask. Good luck! |
Thank you so much @glenn-jocher . I was able to figure out the issue with your kind help. Thanks for helping throughout. |
You're very welcome, @Shanky71! I'm thrilled to hear that you were able to resolve the issue. Remember, the journey of learning and working with technologies like YOLOv5 is a collaborative effort, and I'm here to support you along the way. If you have any more questions or need further assistance in the future, don't hesitate to reach out. Best of luck with your projects, and keep up the great work! |
Hey @glenn-jocher Can you please tell me what happens in each epoch with the validation and training dataset? What is the input and output we get after each epoch |
Absolutely, @Shanky71! Let's break down what happens during each epoch in the training process, especially focusing on how training and validation datasets are utilized. This explanation is general and applies broadly to neural network training, including models like YOLOv5. Epoch OverviewAn epoch represents one complete pass through the entire training dataset. During an epoch, the model will see every example in the training set once. The goal of each epoch is to update the model's weights to minimize the loss function, thereby improving the model's predictions. Training PhaseInput: The input during the training phase of an epoch is the training dataset, which consists of labeled data points. In the context of YOLOv5 and object detection, these data points are images with corresponding bounding boxes and class labels for objects present in the images. Process:
Output: After each epoch, you typically get the average training loss, which indicates how well the model is fitting the training data. You might also track other metrics like accuracy, depending on your specific task. Validation PhaseInput: The input during the validation phase is the validation dataset. This dataset is separate from the training dataset and is not used to update the model's weights. Its purpose is to provide an unbiased evaluation of a model fit on the training dataset. Process:
Output: After the validation phase of an epoch, you receive metrics that indicate the model's performance on the validation set. These metrics are crucial for understanding the model's generalization ability and for tuning hyperparameters. Unlike the training phase, there's no weight update here. Importance of Training and Validation Phases
After each epoch, by comparing the training and validation metrics, you can get insights into how well your model is learning and generalizing. Ideally, both training and validation losses should decrease over time. If the validation loss starts to increase, it might be a sign of overfitting to the training data. I hope this clarifies the process for you! If you have any more questions, feel free to ask. |
Thank you so much @glenn-jocher . |
@Shanky71 you're welcome! I'm glad I could help. If you have any more questions in the future or need further assistance, don't hesitate to reach out. Happy coding, and best of luck with your projects! 😊 |
Hey @glenn-jocher Can you please answer this in context of yolov5. |
Hey @Shanky71! Great questions. In the context of YOLOv5: Hyperparameters are the configuration settings used to structure the training process. Examples include learning rate, batch size, and epochs. Unlike model parameters, hyperparameters are not learned from the data but set before training begins. Validation and Hyperparameter Tuning: The validation set helps in hyperparameter tuning by providing feedback on model performance on unseen data. Adjusting hyperparameters based on validation metrics can improve model generalization, preventing overfitting (model too complex, fitting training data too closely) and underfitting (model too simple, inadequate learning from training data). For example, if validation loss is higher than training loss, it might indicate overfitting. You might lower learning rate or increase regularization. If both losses are high, it could be underfitting, where you might increase model complexity or learning rate. In short, validation plays a critical role in selecting hyperparameters that balance the trade-off between model complexity and its ability to generalize well to unseen data. Hope this helps! 😊 |
Hey @glenn-jocher |
@Shanky71 hey there! 😊 The Precision-Recall (PR) curve is a fantastic tool for evaluating the performance of your object detection model, especially in contexts where classes are imbalanced. Precision tells us how many of the predicted positive cases were actually positive, while Recall (or sensitivity) measures how many of the actual positive cases were correctly identified by the model. From the PR curve, you can infer:
In summary, the PR curve provides insights into how well your model distinguishes between classes, especially in scenarios where one class dominance might skew traditional accuracy metrics. Hope this clarifies your query! |
Thank you so much @glenn-jocher for helping me in understanding the basic doubts about the model. I really appreciate your kind help. |
You're very welcome, @Shanky71! 😊 I'm glad I could assist. If you have any more questions or need further help down the line, feel free to reach out. Happy coding, and best of luck with your projects! |
Hey @glenn-jocher does precision depends on recall? the columns “precision” (for which recall?) and “recall” (for which precision?) Is there any missing information <style> </style>
Furthermore, if mAP, i.e., area under the curve depends on IoU, there must be another control varying P and R (otherwise, you would not have a precision-recall curve for a fixed IoU from which to calculate mAP@0.95] |
Hey there! It seems there's a bit of confusion about how precision, recall, and mAP relate to each other, especially in the context of YOLOv5. Let's clarify: Precision and Recall Relationship: Precision and recall are inversely related in many scenarios. Improving precision often reduces recall, and vice versa. This is because precision focuses on the quality of positive predictions, while recall focuses on identifying all actual positives, quality aside. They don't depend on each other per se but are affected by the decision threshold: lowering it increases recall and potentially lowers precision, while raising it does the opposite. "For which" Precision and Recall? When we talk about precision and recall without specifying, we're generally referring to these metrics computed over a range of decision thresholds, which gives us an aggregate understanding of model performance across different levels of certainty for its predictions. mAP and IoU: mAP (mean Average Precision) considers the entire precision-recall curve across different thresholds. The "at IoU" (e.g., mAP @Shanky71.5, mAP@0.5:0.95) specifies the Intersection over Union (IoU) threshold or range for considering detections as true positives. IoU measures the overlap between the predicted and actual bounding boxes. Different IoU thresholds affect which detections are considered correct, thus indirectly influencing precision and recall and the shape of their curve. For each class or at a collective level, as IoU thresholds vary, so do the associated precision and recall values for those conditions, leading to different mAP values. In essence, as you vary IoU thresholds, the criteria for a detection being considered "correct" change, which then modifies the precision-recall relationship and, by extension, the mAP for that IoU setting. I hope this helps clear things up! Feel free to ask if you have more questions. |
@glenn-jocher Thanks for your reply.
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Hey there! I'm here to help clarify your doubts. 😊
I hope this clears up your questions! If you need further clarification, feel free to ask. Happy detecting! |
Thank you so much @glenn-jocher for helping me in clearing my doubts. Thanks for your time😊. |
@Shanky71 you're welcome! 😊 I'm glad I could help. If you have any more questions or need further assistance, don't hesitate to reach out. Happy coding! |
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Hi @glenn-jocher
What does True Positive, False Positive,False negative and True Negative means in yolov5. I know it's a very basic question but several references are giving me several answers. Can you explain me with some example?
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