Code coverage is a metric that represents the percentage of your codebase that is executed when your tests run. It provides insight into how well your tests exercise your code and can be crucial in identifying untested parts of your application. A high code coverage percentage is often associate...
Hello @glenn-jocher Thank you for the explanation. This is the code I am using to test the quantized model. import cv2 import matplotlib.pyplot as plt import numpy as np # import tensorflow as tf # from ultralytics.utils.ops import scale_coords import tflite_runtime.interpreter as tflite...
Thank you for your interest in contributing toUltralytics open-sourceYOLO projects. Your participation is crucial in shaping the future of our software and fostering a community of innovation and collaboration. Whether you're improving code, reporting bugs, or suggesting features, your contributions m...
Regarding your question about the phases of work from input to output for each model, we can provide you with a general explanation. At a high level, the input (video or image) is first processed by the backbone layers to extract features from the input. These features are then passed thr...
For a more comprehensive explanation, we recommend referring to the earlier post, where the intricate details of theYOLOv8 architectureare thoroughly explained. Benchmark Results Across YOLO lineage Once more, the Ultralytics team has conductedbenchmarkingof YOLOv8 using the COCO dataset, revealing no...
For a more comprehensive explanation, we recommend referring to the earlier post, where the intricate details of theYOLOv8 architectureare thoroughly explained. Benchmark Results Across YOLO lineage Once more, the Ultralytics team has conductedbenchmarkingof YOLOv8 using the COCO dataset, revealing no...
Any berries with solidity < 0.95 (see below for explanation) are flagged and reported, as these are typically berries with incorrect segmentation. Any berries with an area > 3 standard deviations from the mean area of the image are similarly flagged. BerryPortraits object identification ...
For a detailed explanation of the arguments, refer to the modelTrainingpage. What types of data and annotations are available in the Argoverse dataset? The Argoverse dataset includes various sensor data types such as high-resolution camera images, LiDAR point clouds, and HD map data. Annotations ...
Explanation of folder correspondences (Consistent with YOLOv8, hyperparameters unchanged) (TODO: Detailed explanation) Summary of secondary innovation points and code implementation (TODO) Paper illustrations: Principle diagrams, network structure diagrams, flowcharts: PPT (Personal choice, can also use ...
By following these steps in TensorBoard, you can monitor the data augmentation techniques and ensure they are being properly applied during model training. I hope this explanation is helpful! Let me know if you have any further questions or need additional assistance. @glenn-jocher 👍...