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9 months ago 👋 Hello @haoaZ, 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.
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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
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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
haoaZ
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YOLOv5 Component
Training, Detection
Bug
I want to improve ECA-attention, but there have same bug, which i cant not solve, i want your help@glenn-jocher . When i run yolo.py it work, but run train.py, there have been some issues. `class EfficientChannelAttention(nn.Module): # Efficient Channel Attention module def init(self, c, b=1, gamma=2): super(EfficientChannelAttention, self).init() t = int(abs((math.log(c, 2) + b) / gamma)) k = t if t % 2 else t + 1
`# parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple
anchors
anchors:
YOLOv5 backbone
backbone:
[from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 9, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3, [1024, False]], # 9 ]
YOLOv5 head
head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]], [-1, 1, EfficientChannelAttention, [512]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ])
sorted_indices为:tensor([ 27, 84, 107, ..., 539, 596, 706])
Traceback (most recent call last):
File "/home/wjh/learning/1/yolov5-5.0/train.py", line 543, in
train(hyp, opt, device, tb_writer)
File "/home/wjh/learning/1/yolov5-5.0/train.py", line 303, in train
pred = model(imgs) # forward
File "/home/wjh/.conda/envs/Yolov5/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, *kwargs)
File "/home/wjh/learning/1/yolov5-5.0/models/yolo.py", line 123, in forward
return self.forward_once(x, profile) # single-scale inference, train
File "/home/wjh/learning/1/yolov5-5.0/models/yolo.py", line 139, in forward_once
x = m(x) # run
File "/home/wjh/.conda/envs/Yolov5/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(input, **kwargs)
File "/home/wjh/learning/1/yolov5-5.0/models/common.py", line 411, in forward
sorted_x = torch.index_select(x, dim = 1, index =sorted_indices)
RuntimeError: INDICES element is out of DATA bounds, id=918 axis_dim=256
When i use cpu the follow problem appear：Epoch gpu_mem box obj cls total labels img_size 0%| | 0/11049 [00:00<?, ?it/s] out_flat:tensor([ 0.13989, 0.01097, 0.67497, ..., 0.14956, 0.13888, -0.00238], grad_fn=进程已结束,退出代码` The display exceeds the index, but I have checked the index during yolo. py runtime and everything is fine,
Environment
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Minimal Reproducible Example
No response
Additional
No response
Are you willing to submit a PR?