yolov5 | YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite | Machine Learning library
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- 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)
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import 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(object): def __init__(self): self.img_size = 640 self.threshold = 0.4 self.max_frame = 160 self.init_model() def init_model(self): self.weights = 'weights/yolov5m.pt' self.device = '0' if torch.cuda.is_available() else 'cpu' self.device = select_device(self.device) model = attempt_load(self.weights, map_location=self.device) model.to(self.device).eval() model.half() # torch.save(model, 'test.pt') self.m = model self.names = model.module.names if hasattr( model, 'module') else model.names self.colors = [ (randint(0, 255), randint(0, 255), randint(0, 255)) for _ in self.names ] def preprocess(self, img): img0 = img.copy() img = letterbox(img, new_shape=self.img_size)[0] img = img[:, :, ::-1].transpose(2, 0, 1) img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(self.device) img = img.half() # 半精度 img /= 255.0 # 图像归一化 if img.ndimension() == 3: img = img.unsqueeze(0) return img0, img def plot_bboxes(self, image, bboxes, line_thickness=None): tl = line_thickness or round( 0.002 * (image.shape[0] + image.shape[1]) / 2) + 1 # line/font thickness for (x1, y1, x2, y2, cls_id, conf) in bboxes: color = self.colors[self.names.index(cls_id)] c1, c2 = (x1, y1), (x2, y2) cv2.rectangle(image, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize( cls_id, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(image, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText(image, '{} ID-{:.2f}'.format(cls_id, conf), (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) return image def detect(self, im): im0, img = self.preprocess(im) pred = self.m(img, augment=False)[0] pred = pred.float() pred = non_max_suppression(pred, self.threshold, 0.3) pred_boxes = [] image_info = {} count = 0 for det in pred: if det is not None and len(det): det[:, :4] = scale_coords( img.shape[2:], det[:, :4], im0.shape).round() for *x, conf, cls_id in det: lbl = self.names[int(cls_id)] x1, y1 = int(x[0]), int(x[1]) x2, y2 = int(x[2]), int(x[3]) pred_boxes.append( (x1, y1, x2, y2, lbl, conf)) count += 1 key = '{}-{:02}'.format(lbl, count) image_info[key] = ['{}×{}'.format( x2-x1, y2-y1), np.round(float(conf), 3)] im = self.plot_bboxes(im, pred_boxes) return im, image_info
import os def pre_process(data_path): file_name = os.path.split(data_path)[1].split('.')[0] return data_path, file_name
import cv2 def predict(dataset, model, ext): global img_y x = dataset[0].replace('\\', '/') file_name = dataset[1] print(x) print(file_name) x = cv2.imread(x) img_y, image_info = model.detect(x) cv2.imwrite('./tmp/draw/{}.{}'.format(file_name, ext), img_y) return image_info
from core import process, predict def c_main(path, model, ext): image_data = process.pre_process(path) image_info = predict.predict(image_data, model, ext) return image_data[1] + '.' + ext, image_info if __name__ == '__main__': pass
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.copy(fimg+str(img_txt_cg_test[j]),new_dataset_test) shutil.copy(flable+str(label_txt_cg_test[j]),new_dataset_testl) for q in range(len(img_txt_cg_valid)): shutil.copy(fimg+str(img_txt_cg_valid[q]),new_dataset_valid) shutil.copy(flable+str(label_txt_cg_valid[q]),new_dataset_validl)
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 │ ├─data │ ├─train │ ├─test │ └─valid │ ├─JPEGImages │ train_29635.jpg │ train_29641.jpg │ train_30090.jpg │ ... │ └─worktxt train_29635.txt train_29641.txt train_30090.txt ...
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 │ ├─JPEGImages │ train_29635.jpg │ train_29641.jpg │ train_30090.jpg │ ... │ └─worktxt train_29635.txt train_29641.txt train_30090.txt ...from pathlib import Path
import sys
sys.path.append(str(Path(__file__, "..", "targetTrack", "yolov5").resolve()))
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5/runs/train/exp/weights/last.pt', force_reload=True)
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.
# 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')
nc: 3
classes: ['cat', 'dog', 'car']
0 0.156 0.321 0.254 0.198
2 0.574 0.687 0.115 0.301
cv2.imshow("window", img)
cv2.waitKey(1) # perform GUI housekeeping tasks
if 0 in results.pandas().xyxy[0]['class']:
results.save()
Trending Discussions on yolov5
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 :
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 07:17This 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.
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:
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
model.eval()
Getting the Prediction:
result = model(img)
print(result.shape)
print(result)
torch.Size([8, 6])
tensor([[277.50000, 379.25000, 410.50000, 478.75000, 0.90625, 2.00000],
[404.00000, 205.12500, 498.50000, 296.00000, 0.88623, 2.00000],
[262.50000, 247.75000, 359.50000, 350.25000, 0.88281, 2.00000],
[210.50000, 177.75000, 295.00000, 261.75000, 0.83154, 2.00000],
[195.50000, 152.50000, 257.75000, 226.00000, 0.78223, 2.00000],
[137.00000, 146.75000, 168.00000, 162.00000, 0.55713, 2.00000],
[ 96.00000, 130.12500, 132.50000, 161.12500, 0.54199, 2.00000],
[ 43.56250, 89.56250, 87.68750, 161.50000, 0.50146, 5.00000]], device='cuda:0')
tensor([[277.50000, 379.25000, 410.50000, 478.75000, 0.90625, 2.00000],
[404.00000, 205.12500, 498.50000, 296.00000, 0.88623, 2.00000],
[262.50000, 247.75000, 359.50000, 350.25000, 0.88281, 2.00000],
[210.50000, 177.75000, 295.00000, 261.75000, 0.83154, 2.00000],
[195.50000, 152.50000, 257.75000, 226.00000, 0.78223, 2.00000],
[137.00000, 146.75000, 168.00000, 162.00000, 0.55713, 2.00000],
[ 96.00000, 130.12500, 132.50000, 161.12500, 0.54199, 2.00000],
[ 43.56250, 89.56250, 87.68750, 161.50000, 0.50146, 5.00000]], device='cuda:0')
so now my question is how do i get the boxes, scores, classes, nums in each variables? I need that for the Object Tracking
I tried it once with the example on Pytorch Documentation: result.xyxy[0]
but in my Case I get an Error:
Tensor has no attribute xyxy
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:
import torch
det = torch.rand(8, 6)
for *xyxy, conf, cls in det:
print(*xyxy)
print(conf)
print(cls)
or you can slice the detections tensor by:
xyxy = det[:, 0:4]
conf = det[:, 4]
cls = det[:, 5]
print(xyxy)
print(conf)
print(cls)
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:
Traceback (most recent call last):
File "python_detection.py", line 132, in
class_ids, confidences, boxes = wrap_detection(inputImage, outs[0])
File "python_detection.py", line 88, in wrap_detection
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.25, 0.45)
TypeError: Can't convert vector element for 'scores', index=0
Everywhere I look, people are using the exact same code as me. Which makes sense, since this code was mostly copied from a tutorial. So I don't know what I'm doing so wrong that keeps giving me this error.
Here's the full function:
def wrap_detection(input_image, output_data):
class_ids = []
confidences = []
boxes = []
rows = output_data.shape[0]
image_width, image_height, _ = input_image.shape
x_factor = image_width / INPUT_WIDTH
y_factor = image_height / INPUT_HEIGHT
for r in range(rows):
row = output_data[r]
confidence = row[4]
if confidence >= 0.4:
classes_scores = row[5:]
_, _, _, max_indx = cv2.minMaxLoc(classes_scores)
class_id = max_indx[1]
if (classes_scores[class_id] > .25):
confidences.append(confidence)
class_ids.append(class_id)
x, y, w, h = row[0].item(), row[1].item(), row[2].item(), row[3].item()
left = int((x - 0.5 * w) * x_factor)
top = int((y - 0.5 * h) * y_factor)
width = int(w * x_factor)
height = int(h * y_factor)
box = np.array([left, top, width, height])
boxes.append(box)
'''
Print the raw output
'''
# Save output
np.set_printoptions(threshold=sys.maxsize)
file = open("python_raw_model_output.txt", "w+")
for i in range(len(boxes)):
file.write(str(boxes[i]) + " " + str(confidences[i]) + " " + str(class_ids[i]))
file.write("\n")
file.close()
# NMS on the lists
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.25, 0.45)
result_class_ids = []
result_confidences = []
result_boxes = []
for i in indexes:
result_confidences.append(confidences[i])
result_class_ids.append(class_ids[i])
result_boxes.append(boxes[i])
return result_class_ids, result_confidences, result_boxes
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.
import torch
# Model
model=torch.load('E:\_best.pt')
# Images
imgs=['E:\Study\currency.jpg']
# Inference
results = model(imgs)
# Results
results.print()
results.save() # or .show()
results.show()
results.xyxy[0] # img1 predictions (tensor)
results.pandas().xyxy[0]
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.
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.
before calling load_state_dict(), be sure that the .pt file contains only model parameters, otherwise, error occurs. This can be checked by print(torch.load(file)).
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?
def displayImg(path):
img = cv2.imread(path)
cv2.namedWindow("window", cv2.WND_PROP_FULLSCREEN)
cv2.setWindowProperty("window",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
cv2.imshow("window", img)
# ----------------LIVE DETECTIONS ---------------
imagePath = "picture.jpg"
frontSpoilerImageOpen = False
chassisImageOpen = False
hubcapImageOpen = False
wheelImageOpen = False
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5/runs/train/exp5/weights/last.pt', force_reload=True)
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
results = model(frame)
try:
detectedItem = results.pandas().xyxy[0].iloc[0, 6]
if detectedItem == "front-spoiler" and not frontSpoilerImageOpen:
frontSpoilerImageOpen = False
chassisImageOpen = False
hubcapImageOpen = False
wheelImageOpen = False
displayImg(os.path.join("imagesToDisplay", "front-spoiler.png"))
frontSpoilerImageOpen = True
elif detectedItem == "chassis" and not chassisImageOpen:
frontSpoilerImageOpen = False
chassisImageOpen = False
hubcapImageOpen = False
wheelImageOpen = False
displayImg(os.path.join("imagesToDisplay", "chassis.png"))
chassisImageOpen = True
elif detectedItem == "hubcap" and not hubcapImageOpen:
frontSpoilerImageOpen = False
chassisImageOpen = False
hubcapImageOpen = False
wheelImageOpen = False
displayImg(os.path.join("imagesToDisplay", "hubcap.png"))
hubcapImageOpen = True
elif detectedItem == "wheel" and not wheelImageOpen:
frontSpoilerImageOpen = False
chassisImageOpen = False
hubcapImageOpen = False
wheelImageOpen = False
displayImg(os.path.join("imagesToDisplay", "wheel.png"))
wheelImageOpen = True
except Exception as e:
print(e)
ANSWER
Answered 2022-Mar-15 at 15:33I had a similar issue. Adding cv2.waitKey after cv2.imshow helped in my case:
cv2.imshow("window", img)
cv2.waitKey(1) # perform GUI housekeeping tasks
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:
--line-thickness 1
--hide-labels True
--hide-conf True
For the --line-thickness argument, pass in an integer value to adjust the thickness, for labels and confidence, they are set to False by default. Setting them to True will hide them.
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:
model = torch.hub.load("ultralytics/yolov5", "yolov5", force_reload=True)
It tries to download model from internet. And throws an error.
Urllib.error.URLError:
How to get the same results offline.
Thanks in advance.
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
# model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) # force_reload to recache
with
model = torch.hub.load(r'C:\Users\Milan\Projects\yolov5', 'custom', path=r'C:\Users\Milan\Projects\yolov5\models\yolov5s.pt', source='local')
With this line, you can run detection also offline.
Note: When you start the app for the first time with the updated torch.hub.load, it will download the model if not present (so you do not need to download it from https://github.com/ultralytics/yolov5/releases).
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
_# 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 --template "HTTP trigger" --authlevel anonymous
Edit the__init__.py file for your business logic to modify your model.
Add the required packages in requirement.txt. After that install the packages using
pip install -r requirements.txt
Test your function locally. using func start
Deploy local project code to the Function App created on Azure, using
func azure functionapp publish ****
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
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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