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10 months ago 👋 Hello @Yuanchihwei, 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.
Requirements
Python>=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 # installEnvironments
YOLOv5 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 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
glenn-jocher
mature-1111
Search before asking
Question
Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size 7/299 18.3G 0.06648 0.05584 0.03083 0.0004405 92 640: 100%|██████████| 260/260 [41:52<00:00, 9.66s/it] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 33/33 [02:55<00:00, 5.32s/ all 1038 4626 0.677 0.127 0.106 0.0427 0.673 0.119 0.101 0.037
Traceback (most recent call last): File "D:\yolov7-u7\yolov7-u7\seg\segment\train.py", line 681, in
main(opt)
File "D:\yolov7-u7\yolov7-u7\seg\segment\train.py", line 577, in main
train(opt.hyp, opt, device, callbacks)
File "D:\yolov7-u7\yolov7-u7\seg\segment\train.py", line 295, in train
for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------
File "D:\anaconda\Lib\site-packages\tqdm\std.py", line 1178, in iter
for obj in iterable:
File "D:\yolov7-u7\yolov7-u7\seg\utils\dataloaders.py", line 171, in iter
yield next(self.iterator)
^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\Lib\site-packages\torch\utils\data\dataloader.py", line 630, in next
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "D:\anaconda\Lib\site-packages\torch\utils\data\dataloader.py", line 1345, in _next_data
return self._process_data(data)
^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\Lib\site-packages\torch\utils\data\dataloader.py", line 1371, in _process_data
data.reraise()
File "D:\anaconda\Lib\site-packages\torch_utils.py", line 694, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "D:\anaconda\Lib\site-packages\torch\utils\data_utils\worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\Lib\site-packages\torch\utils\data_utils\fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\Lib\site-packages\torch\utils\data_utils\fetch.py", line 51, in
data = [self.dataset[idx] for idx in possibly_batched_index]