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Hello, I have some questions about the YOLOv5 code. Could you please help me answer them? #12964
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👋 Hello @enjoynny, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution. If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results. RequirementsPython>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started: git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
StatusIf this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit. Introducing YOLOv8 🚀We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀! Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects. Check out our YOLOv8 Docs for details and get started with: pip install ultralytics |
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Here are my questions:
In dataloader.py, why does the following occur:
if rect and shuffle: LOGGER.warning('WARNING⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') shuffle = False
self.rect = False if image_weights else rect
In these codes, why must the use of the rect strategy be prohibited when using either the shuffle or image_weights strategies?
In train.py, there are three questions regarding the following code:
if RANK != -1:
loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.
It's unclear where it specifies that the losses from all GPUs should be aggregated onto the primary GPU to form the total loss.
What is the significance of loss *= WORLD_SIZE?
Even if opt.quad is true, isn't loss already the total loss? Why multiply it by 4 instead of directly using the total loss for backpropagation?
In val.py, there is this line of code: preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None). Here are my questions:
The model returns two values, but when I look at the return statement in yolo.py (return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)), it seems to return (torch.cat(z, 1), x). I understand that z represents various confidence scores for the bounding boxes, but why do we need torch.cat(z, 1)? Additionally, x is the output from line 53 of yolo.py, which corresponds to the CNN layers. However, this model is not the complete model; why is x considered the training output and used for calculating errors?
Additional
No response
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