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yolov5 | YOLOv5 in PyTorch ONNX CoreML TFLite | Machine Learning library

 by   ultralytics Python Version: v6.1 License: GPL-3.0

 by   ultralytics Python Version: v6.1 License: GPL-3.0

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kandi X-RAY | yolov5 Summary

yolov5 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. yolov5 has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has medium support. You can download it from GitHub.
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. See the YOLOv5 Docs for full documentation on training, testing and deployment. Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7. Inference with YOLOv5 and PyTorch Hub . Models download automatically from the latest YOLOv5 release. detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect. The commands below reproduce YOLOv5 COCO results. Models and datasets download automatically from the latest YOLOv5 release. Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU (Multi-GPU times faster). Use the largest --batch-size possible, or pass --batch-size -1 for YOLOv5 AutoBatch. Batch sizes shown for V100-16GB. Get started in seconds with our verified environments. Click each icon below for details. Automatically track and visualize all your YOLOv5 training runs in the cloud with Weights & Biases. Label and export your custom datasets directly to YOLOv5 for training with Roboflow.
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kandi-support Support

  • yolov5 has a medium active ecosystem.
  • It has 25236 star(s) with 8996 fork(s). There are 284 watchers for this library.
  • There were 2 major release(s) in the last 12 months.
  • There are 248 open issues and 5062 have been closed. On average issues are closed in 25 days. There are 22 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of yolov5 is v6.1
yolov5 Support
Best in #Machine Learning
Average in #Machine Learning
yolov5 Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • yolov5 has 0 bugs and 0 code smells.
yolov5 Quality
Best in #Machine Learning
Average in #Machine Learning
yolov5 Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • yolov5 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • yolov5 code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
yolov5 Security
Best in #Machine Learning
Average in #Machine Learning
yolov5 Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • yolov5 is licensed under the GPL-3.0 License. This license is Strong Copyleft.
  • Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
yolov5 License
Best in #Machine Learning
Average in #Machine Learning
yolov5 License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • yolov5 releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • yolov5 saves you 1539 person hours of effort in developing the same functionality from scratch.
  • It has 6133 lines of code, 412 functions and 33 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
yolov5 Reuse
Best in #Machine Learning
Average in #Machine Learning
yolov5 Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed yolov5 and discovered the below as its top functions. This is intended to give you an instant insight into yolov5 implemented functionality, and help decide if they suit your requirements.

  • Train the model .
  • Load dataset statistics .
  • Calculate k - mean anchor anchors .
  • Non - Suppression Estimation
  • Generate a random perspective .
  • Loads a mosaic tile .
  • Initialize wandb .
  • Computes the precision for each class .
  • Helper function to plot images .
  • Build targets for the given parameters .

yolov5 Key Features

Train Custom Data  🚀 RECOMMENDED

Tips for Best Training Results  ☘️ RECOMMENDED

Weights & Biases Logging  🌟 NEW

Roboflow for Datasets, Labeling, and Active Learning  🌟 NEW

Multi-GPU Training

PyTorch Hub  ⭐ NEW

TFLite, ONNX, CoreML, TensorRT Export 🚀

Test-Time Augmentation (TTA)

Model Ensembling

Model Pruning/Sparsity

Hyperparameter Evolution

Transfer Learning with Frozen Layers  ⭐ NEW

TensorRT Deployment

COCO AP val denotes mAP@0.5:0.95 metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536.

GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32.

EfficientDet data from google/automl at batch size 8.

Reproduce by python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

default

copy iconCopydownload iconDownload
git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

How to read text file in python after that write something on same file

copy iconCopydownload iconDownload
with open('result.txt','r') as file:
    data = file.readlines() # return list of all the lines in the text file.

for a in range(len(data)): # loop through all the lines.
    if 'person' in data[a] and 'tv' in data[a]: # Check if person and tv in the same line.
        data[a] = data[a].replace('\n','') + ' status : High\n'
    elif 'person' in data[a] and 'laptop' in data[a]:# Check if person and laptop in the same line.
        data[a] = data[a] + ' status : Low\n'

with open('result.txt','w') as file: 
    file.writelines(data) # write lines to the file.

YOLOv5 get boxes, scores, classes, nums

copy iconCopydownload iconDownload
import torch

det = torch.rand(8, 6)

for *xyxy, conf, cls in det:
    print(*xyxy)
    print(conf)
    print(cls)
xyxy = det[:, 0:4]
conf = det[:, 4]
cls = det[:, 5]

print(xyxy)
print(conf)
print(cls)
-----------------------
import torch

det = torch.rand(8, 6)

for *xyxy, conf, cls in det:
    print(*xyxy)
    print(conf)
    print(cls)
xyxy = det[:, 0:4]
conf = det[:, 4]
cls = det[:, 5]

print(xyxy)
print(conf)
print(cls)

How to use pt file

copy iconCopydownload iconDownload
file = "model.pt"
model = your_model()
model.load_state_dict(torch.load(file))
# this will automatically load the file and load the parameters into the model.

Yolov5 object detection training

copy iconCopydownload iconDownload
cd yolov5

pip3 install -r requirements.txt

OpenCV Window Freezing when trying to combine with Neural Network Image Classification

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cv2.imshow("window", img)
cv2.waitKey(1)  # perform GUI housekeeping tasks

How to change the bounding box thickness and label text in yolov5?

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--line-thickness 1
--hide-labels True
--hide-conf True

How to use yolov5 api with flask offline?

copy iconCopydownload iconDownload
# model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True)  # force_reload to recache
model = torch.hub.load(r'C:\Users\Milan\Projects\yolov5', 'custom', path=r'C:\Users\Milan\Projects\yolov5\models\yolov5s.pt', source='local')
-----------------------
# model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True)  # force_reload to recache
model = torch.hub.load(r'C:\Users\Milan\Projects\yolov5', 'custom', path=r'C:\Users\Milan\Projects\yolov5\models\yolov5s.pt', source='local')
-----------------------
Downloading https://ultralytics.com/assets/Arial.ttf to /home/<local_user>/.config/Ultralytics/Arial.ttf...
Traceback (most recent call last):
  File "/home/<local_user>/Py_Prac_WSL/yolov5-flask-master/yolov5/utils/plots.py", line 56, in check_pil_font
    return ImageFont.truetype(str(font) if font.exists() else font.name, size)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 836, in truetype
    return freetype(font)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 833, in freetype
    return FreeTypeFont(font, size, index, encoding, layout_engine)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 193, in __init__
    self.font = core.getfont(
OSError: cannot open resource
/home/<your_pc_user>/.config/Ultralytics
C:\Windows\Fonts
path_hubconfig = 'absolute/path/to/yolov5'
path_trained_model = 'absolute/path/to/best.pt'
model = torch.hub.load(path_hubconfig, 'custom', path=path_trained_model, source='local')  # local repo
-----------------------
Downloading https://ultralytics.com/assets/Arial.ttf to /home/<local_user>/.config/Ultralytics/Arial.ttf...
Traceback (most recent call last):
  File "/home/<local_user>/Py_Prac_WSL/yolov5-flask-master/yolov5/utils/plots.py", line 56, in check_pil_font
    return ImageFont.truetype(str(font) if font.exists() else font.name, size)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 836, in truetype
    return freetype(font)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 833, in freetype
    return FreeTypeFont(font, size, index, encoding, layout_engine)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 193, in __init__
    self.font = core.getfont(
OSError: cannot open resource
/home/<your_pc_user>/.config/Ultralytics
C:\Windows\Fonts
path_hubconfig = 'absolute/path/to/yolov5'
path_trained_model = 'absolute/path/to/best.pt'
model = torch.hub.load(path_hubconfig, 'custom', path=path_trained_model, source='local')  # local repo
-----------------------
Downloading https://ultralytics.com/assets/Arial.ttf to /home/<local_user>/.config/Ultralytics/Arial.ttf...
Traceback (most recent call last):
  File "/home/<local_user>/Py_Prac_WSL/yolov5-flask-master/yolov5/utils/plots.py", line 56, in check_pil_font
    return ImageFont.truetype(str(font) if font.exists() else font.name, size)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 836, in truetype
    return freetype(font)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 833, in freetype
    return FreeTypeFont(font, size, index, encoding, layout_engine)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 193, in __init__
    self.font = core.getfont(
OSError: cannot open resource
/home/<your_pc_user>/.config/Ultralytics
C:\Windows\Fonts
path_hubconfig = 'absolute/path/to/yolov5'
path_trained_model = 'absolute/path/to/best.pt'
model = torch.hub.load(path_hubconfig, 'custom', path=path_trained_model, source='local')  # local repo
-----------------------
Downloading https://ultralytics.com/assets/Arial.ttf to /home/<local_user>/.config/Ultralytics/Arial.ttf...
Traceback (most recent call last):
  File "/home/<local_user>/Py_Prac_WSL/yolov5-flask-master/yolov5/utils/plots.py", line 56, in check_pil_font
    return ImageFont.truetype(str(font) if font.exists() else font.name, size)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 836, in truetype
    return freetype(font)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 833, in freetype
    return FreeTypeFont(font, size, index, encoding, layout_engine)
  File "/home/<local_user>/.local/share/virtualenvs/23_Jun-82xb8nrB/lib/python3.8/site-packages/PIL/ImageFont.py", line 193, in __init__
    self.font = core.getfont(
OSError: cannot open resource
/home/<your_pc_user>/.config/Ultralytics
C:\Windows\Fonts
path_hubconfig = 'absolute/path/to/yolov5'
path_trained_model = 'absolute/path/to/best.pt'
model = torch.hub.load(path_hubconfig, 'custom', path=path_trained_model, source='local')  # local repo

Deploying Yolov5 to azure

copy iconCopydownload iconDownload
_# Create and activate an environment_  
python3 -m venv .venv  
source .venv/bin/activate_
# Create a FunctionApp Project Locally_  
func init --worker-runtime python_
# Create a Function_  
func new --name <FunctionName> --template "HTTP trigger" --authlevel anonymous
pip install -r requirements.txt
func azure functionapp publish **<FuncitonAPP Name>**
-----------------------
_# Create and activate an environment_  
python3 -m venv .venv  
source .venv/bin/activate_
# Create a FunctionApp Project Locally_  
func init --worker-runtime python_
# Create a Function_  
func new --name <FunctionName> --template "HTTP trigger" --authlevel anonymous
pip install -r requirements.txt
func azure functionapp publish **<FuncitonAPP Name>**
-----------------------
_# Create and activate an environment_  
python3 -m venv .venv  
source .venv/bin/activate_
# Create a FunctionApp Project Locally_  
func init --worker-runtime python_
# Create a Function_  
func new --name <FunctionName> --template "HTTP trigger" --authlevel anonymous
pip install -r requirements.txt
func azure functionapp publish **<FuncitonAPP Name>**

Unable to find module/folder despite being in the same directory

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from pathlib import Path
import sys

sys.path.append(str(Path(__file__, "..", "targetTrack", "yolov5").resolve()))

c# to vb conversion issues - Is this line correct?

copy iconCopydownload iconDownload
Using myscorer As New YoloScorer(Of YoloCocoP5Model)("Assets/Weights/best.onnx")
    ' do something with myscorer
End Using

Community Discussions

Trending Discussions on yolov5
  • How to read text file in python after that write something on same file
  • YOLOv5 get boxes, scores, classes, nums
  • TypeError: Can't convert vector element for 'scores', index=0
  • How to use pt file
  • How to place the dataset for training Yolov5?
  • Yolov5 object detection training
  • OpenCV Window Freezing when trying to combine with Neural Network Image Classification
  • How to change the bounding box thickness and label text in yolov5?
  • How to use yolov5 api with flask offline?
  • Deploying Yolov5 to azure
Trending Discussions on yolov5

QUESTION

How to read text file in python after that write something on same file

Asked 2022-Apr-14 at 07:53

i am currently working with yolov5 code is here i slightly modified in code to save results in text file called output.txt

output.txt file :

0: 480x640 2 persons, 1 tv, 1: 480x640 5 persons, 1 cell phone, Done. (0.759s) Mon, 04 April 11:39:48
0: 480x640 2 persons, 1 laptop, 1: 480x640 4 persons, 1 oven, Done. (0.763s) Mon, 04 April 11:39:50

After that , i wanna validate text file line by line with following conditions

if person and tv detected in same row add status : High 
if person and laptop detected in same row add status : Low 

Expected output :

0: 480x640 2 persons, 1 tv, 1: 480x640 5 persons, 1 cell phone, Done. (0.759s) Mon, 04 April 11:39:48 status : High 
0: 480x640 2 persons, 1 laptop, 1: 480x640 4 persons, 1 oven, Done. (0.763s) Mon, 04 April 11:39:50 status : Low

Is it possible if yes how can i resolve that

Thanks in advance

ANSWER

Answered 2022-Apr-14 at 07:17

This code may helps you.

with open('result.txt','r') as file:
    data = file.readlines() # return list of all the lines in the text file.

for a in range(len(data)): # loop through all the lines.
    if 'person' in data[a] and 'tv' in data[a]: # Check if person and tv in the same line.
        data[a] = data[a].replace('\n','') + ' status : High\n'
    elif 'person' in data[a] and 'laptop' in data[a]:# Check if person and laptop in the same line.
        data[a] = data[a] + ' status : Low\n'

with open('result.txt','w') as file: 
    file.writelines(data) # write lines to the file.

Source https://stackoverflow.com/questions/71867691

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install yolov5

You can download it from GitHub.
You can use yolov5 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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