Comments (4)
๐ Hello @Shassk, thank you for your interest in YOLOv3 ๐! Please visit our โญ๏ธ 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 ๐ Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training โ 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.
Requirements
Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:
git clone https://github.com/ultralytics/yolov3 # clone
cd yolov3
pip install -r requirements.txt # install
Environments
YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all YOLOv3 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv3 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
Introducing YOLOv8 ๐
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 ๐!
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
from yolov3.
@Shassk hello! Thanks for reaching out with your observations. The size discrepancy you're seeing in the YOLOv3 ONNX model compared to v5 and v8 is indeed unusual. Here are a few things to consider:
-
Model Complexity: YOLOv3 has a different architecture compared to v5 and v8, which might inherently lead to different model sizes. However, the difference should not be as drastic as you've described.
-
Export Settings: When exporting to ONNX, ensure that you're using the same settings, such as
--simplify
, which can reduce the model size by eliminating redundant operations. -
Training Configuration: Double-check your training configuration. Differences in layer configurations or model depth can lead to larger models.
-
Pruning: If model size is a critical factor, consider applying model pruning techniques before exporting to ONNX. This can help reduce the size and complexity of the model.
-
Optimization: Post-training optimization techniques can also be applied to the ONNX model to reduce its size.
If you've ensured all the above and the issue persists, it might be worth looking into the specifics of how the ONNX model is being saved. Sometimes, additional metadata or training information can bloat the file size.
For further assistance, please refer to our documentation or consider opening an issue with detailed information about your training configuration and export process. We're here to help! ๐
from yolov3.
It's not so much about export size as it is about primary trained model size in .pt
format โ 200 MB is not what I expected. But sure, I will create a new issue with all the data.
from yolov3.
@Shassk apologies for the confusion, and thank you for your patience. A 200 MB .pt
file for YOLOv3 is indeed larger than typical. Here are a few quick checks you can do:
-
Model Architecture: Verify that the model architecture in the
.yaml
file matches the expected YOLOv3 architecture without unintended modifications. -
Weights: Ensure that the model isn't accidentally saving additional weights or data that it shouldn't be.
-
Optimizer State: The
.pt
file includes both the model weights and the optimizer state. A large optimizer state could inflate the file size. -
Precision: Check if the model is being saved with higher precision (e.g., float64) than necessary (float32 is standard).
If these checks don't reveal any issues, please do open a new issue with the details of your training setup, and we'll take a closer look to help resolve this. Your feedback is invaluable in improving the tools we provide to the community. ๐
from yolov3.
Related Issues (20)
- About the instructions and code comments HOT 3
- A hopelessly long try to replicate the YOLOv3 kernel HOT 2
- Change in the anchor boxes HOT 10
- โ๏ธClosed per Code of Conduct HOT 1
- no anchor_grid in V9.6.0 yolov3.pt HOT 5
- Convert YOLOv3 dataset format to YOLOv8 HOT 3
- What's the difference between it and Yolov3 by Joseph Redmon ? HOT 7
- Integrating YOLOv8 into YOLOv3 Ultralytics HOT 2
- Seeking Advice on Equivalent YOLOv5 Variant to Standard YOLOv3 HOT 1
- Training requires much more VRAM than v5/v8 and results in ~200 MB models comparing to <15 MB models of v5/v8 HOT 5
- how to train your yolov8?
- Need info regarding yolov3-tiny anchors, dataset creation and loss function. HOT 5
- Cannot compute loss function from best model HOT 1
- yolov3_ros input topic channel problem HOT 5
- Issue with training YOLOv3-tiny from scratch HOT 4
- yolov3.pt HOT 4
- ๅ ณไบ่ฐ็จๆจ็ไปฃ็ ๅ้ๅฐ็ไธไธไบ้ฎ้ข HOT 8
- Bug of incomplete information display HOT 2
- No module named 'ultralytics.yolo' HOT 2
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