yolov5 | YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite | Machine Learning library
kandi X-RAY | yolov5 Summary
kandi X-RAY | yolov5 Summary
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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Top functions reviewed by kandi - BETA
- 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
yolov5 Examples and Code Snippets
Lines of Code : 124License : Permissive (MIT)
Copyimport torch
import numpy as np
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords, letterbox
from utils.torch_utils import select_device
import cv2
from random import randint
class Detector(obj 
License : No LicenseCopy# 小数据建议：copy 大数据建议：move
for i in range(len(img_txt_cg_train)):
shutil.copy(fimg+str(img_txt_cg_train[i]),new_dataset_train)
shutil.copy(flable+str(label_txt_cg_train[i]),new_dataset_trainl)
for j in range(len(img_txt_cg_test)):
shutil.cop dataset
│
├─Annotations
│ train_29635.xml
│ train_29641.xml
│ train_30090.xml
│ ...
│
├─ImageSets
│ └─Main
│ train.txt
│ test.txt
│ valid.txt
│ img_train.txt
│ img_test.txt
│ img_valid.txt
│
├─JPEGImage 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 acv2.imshow("window", img)
cv2.waitKey(1) # perform GUI housekeeping tasks
# 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\modefrom pathlib import Path
import sys
sys.path.append(str(Path(__file__, "..", "targetTrack", "yolov5").resolve()))
if 0 in results.pandas().xyxy[0]['class']:
results.save()
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5/runs/train/exp/weights/last.pt', force_reload=True)
nc: 3
classes: ['cat', 'dog', 'car']
0 0.156 0.321 0.254 0.198
2 0.574 0.687 0.115 0.301
Community Discussions
Trending Discussions on yolov5
QUESTION
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 :
...ANSWER
Answered 2022-Apr-14 at 07:17This code may helps you.
QUESTION
im trying to bind the Object Tracking with Deep Sort in my Project and i need to get the boxes, scores, classes, nums.
Loading Pretrained Yolov5 model:
...ANSWER
Answered 2022-Apr-05 at 08:26The output from the model is a torch tensor and has no xyxy method. You need to extract the values manually. Either you can go through each detection one by one:
QUESTION
I'm trying to take the output of a yolov5s.onnx model and and run NMSBoxes on it. But I keep getting this error:
...ANSWER
Answered 2022-Apr-04 at 21:29I had the same issue. It seemed to be related to the cuda configuration as it works fine on the cpu. I did not figure out exactly what was wrong but I worked around the issue by using fastNMS: enter link description here
QUESTION
I'm trying to make a currency recognition model and I did so using a dataset on kaggle and colab using yolov5 and I exactly carried out the steps explained on yolov5 github. At the end, I downloaded a .pt file which has the weights of the model and now I want to use it in python file to detect and recognize currency . How to do this?
I am a beginner in computer vision and I am totally confused about what to do. I am searching over and over but I don't reach anything.
...ANSWER
Answered 2022-Apr-01 at 17:19If you want to read trained parameters from .pt file and load it into your model, you could do the following.
QUESTION
I’m currently working on object detection using yolov5. I trained a model with a custom dataset which has 3 classes = [‘Car’,‘Motorcycle’,‘Person’]
I have many questions related to yolov5.
All the custom images are labelled using Roboflow.
question1 : As you can see from the table that my dataset has mix of images with different sizes. Will this be a problem in training? And also assume that i’ve trained the model and got ‘best.pt’. Will that model work efficiently in any dimensions of images/videos.
question 2:
Is this directory model correct for training. Even i have ‘test’ directory but it seems that the directory is not at all used. The images in the ‘test’ folder is useless. ( I know that i’m asking dumb questions, please bare with me.)
Is it ok if place all my images like this
And should i need a ‘test’ folder?
question3: What is the ‘imgsz’ in detect.py? Is it downsampling the input source?
I’ve spent more than 3 weeks in yolo. I love it but i find some parts difficult to grasp. kindly provide suggestion for this questions. Thanks in advance.
...ANSWER
Answered 2022-Mar-25 at 14:06"question1 : As you can see from the table that my dataset has mix of images with different sizes. Will this be a problem in training? And also assume that i’ve trained the model and got ‘best.pt’. Will that model work efficiently in any dimensions of images/videos."
- As long as you've resized/normalized all of your images to be the same square size, then you should be fine. YOLO trains on square images. You can use a platform like Roboflow to process your images so they not only come out in the right structure (for your images and annotation files) but also resize them while generating your dataset so they are all the same size. http://roboflow.com/ - you just need to make a public workspace to upload your images to and you can use the platform free. Here's a video that covers custom training with YOLOv5: https://www.youtube.com/watch?v=x0ThXHbtqCQ
Roboflow's python package can also be used to extract your images programmatically: https://docs.roboflow.com/python
"Is this directory model correct for training. Even i have ‘test’ directory but it seems that the directory is not at all used. The images in the ‘test’ folder is useless. ( I know that i’m asking dumb questions, please bare with me.)"
- Yes that directory model is correct from training. Its what I have whenever I run YOLOv5 training too.
You do need a test folder if you want to run inference against the test folder images to learn more about your model's performance.
The 'imgsz' parameter in detect.py is for setting the height/width of the images for inference. You set it at the value you used for --img when you ran train.py.
For example: Resized images to 640 by 640 when generating your images for training? Use (640, 640) for the 'imgsz' parameter (that is the default value). And that would also mean you set --img to 640 when you ran train.py
YOLOv5's Github: Tips for Best Training Results https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results
Roboflow's Model Production Tips: https://docs.roboflow.com/model-tips
QUESTION
Please i need you help concerning my yolov5 training process for object detection!
I try to train my object detection model yolov5 for detecting small object ( scratch). For labelling my images i used roboflow, where i applied some data augmentation and some pre-processing that roboflow offers as a services. when i finish the pre-processing step and the data augmentation roboflow gives the choice for different output format, in my case it is yolov5 pytorch, and roboflow does everything for me splitting the data into training validation and test. Hence, Everything was set up as it should be for my data preparation and i got at the end the folder with data.yaml and the images with its labels, in data.yaml i put the path of my training and validation sets as i saw in the GitHub tutorial for yolov5. I followed the steps very carefully tought.
The problem is when the training start i get nan in the obj and box column as you can see in the picture bellow, that i don't know the reason why, can someone relate to that or give me any clue to find the solution please, it's my first project in computer vision.
This is what i get when the training process starts
This the last message error when the training finish
The training continue without any problem but the map and precision remains 0 all the process !!
Ps : Here is the link of tuto i followed : https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
...ANSWER
Answered 2021-Dec-04 at 09:38Running my code in colab worked successfully and the resulats were good. I think that the problem was in my personnel laptop environment maybe the version of pytorch i was using '1.10.0+cu113', or something else ! If you have any advices to set up my environnement for yolov5 properly i would be happy to take from you guys. many Thanks again to @alexheat
QUESTION
I am using a NN to detect 4 types of objects (chassis, front-spoiler, hubcap, wheel) in the live feed of my webcam. When one is detected, I want to display an image with information about it (chassis.png, front-spoiler.png, hubcap.png, wheel.png). When I run my NN and hold one of the items in front of the webcam, the opencv windows freezes and doesnt display anything. What is the reason for that?
...ANSWER
Answered 2022-Mar-15 at 15:33I had a similar issue. Adding cv2.waitKey after cv2.imshow helped in my case:
QUESTION
I am using YOLOv5s for object detection on custom datasets, there are multiple objects in given video, sometimes label text and bounding box thickness looks very bad. how can I customize these things?
...ANSWER
Answered 2022-Mar-15 at 08:55when using detect.py, pass in the following arguments to adjust the labels and bounding boxes:
QUESTION
I was able to run the Flask app with yolov5 on a PC with an internet connection. I followed the steps mentioned in yolov5 docs and used this file: yolov5/utils/flask_rest_api/restapi.py,
But I need to achieve the same offline(On a particular PC). Now the issue is, when I am using the following:
...ANSWER
Answered 2022-Feb-25 at 15:38If you want to run detection offline, you need to have the model already downloaded.
So, download the model (for example yolov5s.pt) from https://github.com/ultralytics/yolov5/releases and store it for example to the yolov5/models.
After that, replace
QUESTION
How to deploy Custom trained YOLOV5 model to azure using azure functions? I couldn’t find any online resources
Complete Scenario:
There is a sharepoint app where user will upload the videos, once the new video is uploaded, it should trigger the flow to azure function, this azure function should be able to predict the objects in the frame with the custom trained yolov5 model
...ANSWER
Answered 2022-Feb-25 at 09:17We are not sure about the Deployment of YOLO5 in Azure Function.
Follow the below steps, it will work for any ML model using Azure Function
Prerequisites:- Install Azure CLI
- Install Azure Function Core Tools
Using CLI create a python function
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Install yolov5
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.
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