github-actions[bot]
commented
1 year ago 👋 Hello @kalikhademi, 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>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. 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
kalikhademi
Search before asking
YOLOv5 Component
Training
Bug
I have used this tutorial to train yolov3 on custom dataset. I have used pretrained weights and yolov3.yaml in the repo as cfg file. When I run train.py on one GPU, I recieve the folllowing error: from n params module arguments
main(opt)
File "/Object Detection/yolov3/train.py", line 535, in main
train(opt.hyp, opt, device, callbacks)
File "/Object Detection/yolov3/train.py", line 127, in train
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
File "/Object Detection/yolov3/models/yolo.py", line 187, in init
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
File "/Object Detection/yolov3/models/yolo.py", line 354, in parsemodel
m = nn.Sequential((m(args) for _ in range(n))) if n > 1 else m(*args) # module
File "/Object Detection/yolov3/models/yolo.py", line 54, in init
self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
RuntimeError: shape '[3, -1, 2]' is invalid for input of size 3
0 -1 1 928 models.common.Conv [3, 32, 3, 1]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 20672 models.common.Bottleneck [64, 64]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 164608 models.common.Bottleneck [128, 128]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 8 2627584 models.common.Bottleneck [256, 256]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 8 10498048 models.common.Bottleneck [512, 512]
9 -1 1 4720640 models.common.Conv [512, 1024, 3, 2]
10 -1 4 20983808 models.common.Bottleneck [1024, 1024]
11 -1 1 5245952 models.common.Bottleneck [1024, 1024, False]
12 -1 1 525312 models.common.Conv [1024, 512, 1, 1]
13 -1 1 4720640 models.common.Conv [512, 1024, 3, 1]
14 -1 1 525312 models.common.Conv [1024, 512, 1, 1]
15 -1 1 4720640 models.common.Conv [512, 1024, 3, 1]
16 -2 1 131584 models.common.Conv [512, 256, 1, 1]
17 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
18 [-1, 8] 1 0 models.common.Concat [1]
19 -1 1 1377792 models.common.Bottleneck [768, 512, False]
20 -1 1 1312256 models.common.Bottleneck [512, 512, False]
21 -1 1 131584 models.common.Conv [512, 256, 1, 1]
22 -1 1 1180672 models.common.Conv [256, 512, 3, 1]
23 -2 1 33024 models.common.Conv [256, 128, 1, 1]
24 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
25 [-1, 6] 1 0 models.common.Concat [1]
26 -1 1 344832 models.common.Bottleneck [384, 256, False]
27 -1 2 656896 models.common.Bottleneck [256, 256, False]
/Object Detection/yolov3/data/miap.yaml Namespace(weights='yolov3_byol.pt', cfg='yolov3.yaml', data='/Object Detection/yolov3/data/miap.yaml', hyp='data/hyps/hyp.scratch-low.yaml', epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket='', cache=None, image_weights=False, device='', multi_scale=False, single_cls=False, optimizer='SGD', sync_bn=False, workers=8, project='runs/train', name='exp', exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias='latest', save_dir='runs/train/exp18') Traceback (most recent call last): File "/Object Detection/yolov3/train.py", line 642, in
Environment
YOLOv3 Os:Ubuntu Single A100 GPu Python 3.10, torch 2.0.1, cuda 11.7
Minimal Reproducible Example
No response
Additional
python train.py --data coco128.yaml --weights yolov3.pt --cfg yolov3.yaml --img 640
Are you willing to submit a PR?