zh/modes/predict/

[giscus[bot]]

zh/modes/predict/

了解如何在各种任务中使用YOLOv8 预测模式。了解不同的推理源，如图像、视频和数据格式。

https://docs.ultralytics.com/zh/modes/predict/

[DaixinX]

I finished my training.when I detect my images，how can i chang the box color?

[pderrenger]

@DaixinX hello! After you've completed training your YOLOv8 model and you're ready to detect objects in your images, you can customize the bounding box color by modifying the visualization code. In the YOLOv8 repository, the color of the bounding boxes is typically set within the plotting functions.

To change the box color, you would need to locate the section of the code responsible for drawing the boxes and adjust the color parameter accordingly. This is usually defined by an RGB color value. Keep in mind that direct code modification might be necessary, as there may not be an out-of-the-box argument or parameter to change the box color directly from the command line interface.

If you need further guidance on where to find and how to modify these settings, please refer to the documentation at https://docs.ultralytics.com, which provides comprehensive information on using and customizing the YOLOv8 model for various tasks. 😊🎨

[piaoyaoi]

if iuse model.predict(stream=True),how to stop predict

[glenn-jocher]

@piaoyaoi hey there! 😊 To stop predictions when using model.predict(stream=True), you can simply break out of the loop processing the stream. For example, you can use a conditional statement to check for a specific condition and then use break to exit the loop. Here's a quick example:

from ultralytics import YOLO

# Load your model
model = YOLO('yolov8n.pt')

# Assuming you're processing a list of images in a loop
for image in image_list:
    results = model(image, stream=True)
    # Process your results here
    
    # Condition to stop predictions, could be anything based on your requirement
    if some_condition:
        break

If you're processing video frames or a continuous stream, you can similarly include a condition to exit based on your specific needs, such as a keyboard interrupt or a certain number of frames processed. Hope this helps! Let us know if you have any more questions.

[piaoyaoi]

i processing a rtmp stream in my code such like this
results = model.predict(source=self.rtmp_url, vid_stride=30,
stream=True)
for r in results:
cpu_res = r.cpu()

[glenn-jocher]

Hey there! 👋 It looks like you're processing an RTMP stream with stream=True, which is great for handling video streams efficiently. To stop the predictions, you can simply use a condition to break out of the loop. For example, you could check for a specific keyboard input or another stopping condition. Here's a quick example using a keyboard interrupt:

import cv2
from ultralytics import YOLO

# Load your model
model = YOLO('yolov8n.pt')

# Assuming you're processing the stream in a loop
try:
    results = model.predict(source=self.rtmp_url, vid_stride=30, stream=True)
    for r in results:
        cpu_res = r.cpu()
        # Your processing here
        
        # Example condition to break the loop (e.g., pressing 'q')
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
except KeyboardInterrupt:
    print("Stopped by user.")

This way, you can gracefully exit the prediction loop by pressing 'q' or using a KeyboardInterrupt (Ctrl+C in most terminals). Hope this helps! Let me know if you have any more questions. 😊

[piaoyaoi]

Thank you very much for your prompt reply

[glenn-jocher]

Hey there! 👋 To stop predictions when using model.predict(stream=True) with an RTMP stream, you can incorporate a condition within your loop to break out of it. If you're running this in a script, one common approach is to catch a keyboard interrupt. Here's a quick example:

from ultralytics import YOLO

# Load your model
model = YOLO('yolov8n.pt')

try:
    results = model.predict(source=self.rtmp_url, vid_stride=30, stream=True)
    for r in results:
        cpu_res = r.cpu()
        # Your processing logic here
        
        # Example condition to break (this is just a placeholder, adapt as needed)
        if some_condition_to_stop:
            break

except KeyboardInterrupt:
    print("Prediction stopped by user.")

In this example, some_condition_to_stop should be replaced with your actual condition for stopping the predictions. If you're running this in a more interactive environment (like a Jupyter notebook), you might need a different approach to signal a stop condition, such as setting a variable in another cell that your loop checks on each iteration. Hope this helps! 😊

[gcpctt]

hello，I want to make my predictions output format like this,
[{
"image_id" : int,

"category_id"    : int,

"bbox"              : [x,y,width,height],

"score"             : float,

}]
how can I get these Parameters?

[pderrenger]

Hello! 😊 To format your predictions as specified, you can access the required parameters directly from the Results object returned by the model's prediction. Here's a quick example on how to achieve this:

from ultralytics import YOLO

# Load your model
model = YOLO('yolov8n.pt')  # or your custom model

# Run inference
results = model('path/to/your/image.jpg')

# Extract desired info
formatted_predictions = [{
    "image_id": idx,  # Assuming you have a way to assign image IDs
    "category_id": int(cls),
    "bbox": [float(x), float(y), float(width), float(height)],
    "score": float(score)
} for idx, (cls, (x, y, width, height), score) in enumerate(zip(results.boxes.cls, results.boxes.xywh, results.boxes.conf))]

# Now formatted_predictions contains the desired output format

This code snippet assumes you're processing images sequentially and assigning image_id accordingly. The category_id is derived from the class predictions, bbox from the bounding box coordinates, and score from the confidence scores. Adjust the image_id assignment as per your requirements. Happy coding! 🚀

[nwpu-caikun]

I trained a data label of my own, person-with-bag, how do I count all the number of that label in a video?

code like this:

model = YOLO("D:\ultralytics-main\person-with-bag-best.pt")
cap = cv2.VideoCapture("D:\ultralytics-main\Resource\b1-0208-0809.mp4")
while cap.isOpened():
success, frame = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break

results = model(frame)
annotated_frame = results[0].plot()


num_objects = len(results[0].boxes) if results and results[0].boxes else 0
video_writer.write(annotated_frame)

[glenn-jocher]

@nwpu-caikun hello! 😊 To count all instances of your custom label "person-with-bag" in a video, you need to filter your results by the specific class and sum them up over all frames. Here's how you can modify your code to achieve that:

import cv2
from ultralytics import YOLO

# Load your custom trained model
model = YOLO("D:\\ultralytics-main\\person-with-bag-best.pt") 
cap = cv2.VideoCapture("D:\\ultralytics-main\\Resource\\b1-0208-0809.mp4")

# Initialize the counter
label_count = 0

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        break

    # Run inference
    results = model(frame)
    annotated_frame = results[0].plot()

    # Filter detections by your custom label and count
    for det in results[0].boxes:
        if det.cls == model.names.index('person-with-bag'):
            label_count += 1

    video_writer.write(annotated_frame)

print(f"Total 'person-with-bag' count: {label_count}")

Please make sure to replace 'person-with-bag' with the exact name of your class in the model's names attribute. This code assumes that you have a video writer setup (video_writer.write(annotated_frame)) to output the processed video. Let me know if this helps or if you have any further questions! 🚀

[nwpu-caikun]

I try the code you give me ,but when I run the code ,there is a error , the information :Traceback (most recent call last): &nbsp; File "D:\ultralytics-main\Task\PersonCount.py", line 55, in <module&gt; &nbsp; &nbsp; if det.cls == model.names.index('person'): AttributeError: 'dict' object has no attribute 'index' 908637049 ***@***.*** &nbsp;

…

------------------&nbsp;原始邮件&nbsp;------------------ 发件人: "ultralytics/ultralytics" ***@***.***&gt;; 发送时间:&nbsp;2024年3月19日(星期二) 晚上6:05 ***@***.***&gt;; ***@***.******@***.***&gt;; 主题:&nbsp;Re: [ultralytics/ultralytics] zh/modes/predict/ (Discussion #7982) @nwpu-caikun hello! 😊 To count all instances of your custom label "person-with-bag" in a video, you need to filter your results by the specific class and sum them up over all frames. Here's how you can modify your code to achieve that: import cv2 from ultralytics import YOLO # Load your custom trained model model = YOLO("D:\\ultralytics-main\\person-with-bag-best.pt") cap = cv2.VideoCapture("D:\\ultralytics-main\\Resource\\b1-0208-0809.mp4") # Initialize the counter label_count = 0 while cap.isOpened(): success, frame = cap.read() if not success: break # Run inference results = model(frame) annotated_frame = results[0].plot() # Filter detections by your custom label and count for det in results[0].boxes: if det.cls == model.names.index('person-with-bag'): label_count += 1 video_writer.write(annotated_frame) print(f"Total 'person-with-bag' count: {label_count}") Please make sure to replace 'person-with-bag' with the exact name of your class in the model's names attribute. This code assumes that you have a video writer setup (video_writer.write(annotated_frame)) to output the processed video. Let me know if this helps or if you have any further questions! 🚀 — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***&gt;

[glenn-jocher]

Hi there! It looks like you encountered an AttributeError while trying to use model.names.index('person'). This is likely because model.names is a list, but it seems det.cls might not be directly referencing an individual item within it.

To resolve this issue, ensure that det.cls returns an integer representing the class ID, and then compare it with the index obtained from model.names.index('person'). Here's a quick fix:

# Assuming det.cls returns an integer class ID
if det.cls == model.names.index('person'):
    # your code here

If det is a dictionary that contains the class information, make sure to correctly access the class ID from it. For example, if det has a key 'class' or similar that stores the class ID, you would access it like this:

# Correctly access class ID from 'det' dictionary
if det['class'] == model.names.index('person'):
    # your code here

If you're still facing issues, could you provide more details about what det contains? That'll help us give you more accurate advice. 😊

[nwpu-caikun]

I used your official code when counting the number of person in a one-hour video. In the final result I got the in_count number&nbsp; 751 and out_count number&nbsp; 1334. But the maximum tag number of person in the video is 5547 , does the maximum tag number have anything to do with the total number of in_count plus the out_count number ? pic are blow : &nbsp; the main code is here: while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break tracks = model.track(im0, persist=True, show=False,classes= 0) im0 = counter.start_counting(im0, tracks) print("in_count:{}".format(counter.in_counts)) print("out_count:{}".format(counter.out_counts)) video_writer.write(im0) 908637049 ***@***.*** &nbsp;

…

------------------&nbsp;原始邮件&nbsp;------------------ 发件人: "ultralytics/ultralytics" ***@***.***&gt;; 发送时间:&nbsp;2024年3月19日(星期二) 晚上6:05 ***@***.***&gt;; ***@***.******@***.***&gt;; 主题:&nbsp;Re: [ultralytics/ultralytics] zh/modes/predict/ (Discussion #7982) @nwpu-caikun hello! 😊 To count all instances of your custom label "person-with-bag" in a video, you need to filter your results by the specific class and sum them up over all frames. Here's how you can modify your code to achieve that: import cv2 from ultralytics import YOLO # Load your custom trained model model = YOLO("D:\\ultralytics-main\\person-with-bag-best.pt") cap = cv2.VideoCapture("D:\\ultralytics-main\\Resource\\b1-0208-0809.mp4") # Initialize the counter label_count = 0 while cap.isOpened(): success, frame = cap.read() if not success: break # Run inference results = model(frame) annotated_frame = results[0].plot() # Filter detections by your custom label and count for det in results[0].boxes: if det.cls == model.names.index('person-with-bag'): label_count += 1 video_writer.write(annotated_frame) print(f"Total 'person-with-bag' count: {label_count}") Please make sure to replace 'person-with-bag' with the exact name of your class in the model's names attribute. This code assumes that you have a video writer setup (video_writer.write(annotated_frame)) to output the processed video. Let me know if this helps or if you have any further questions! 🚀 — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***&gt;

[pderrenger]

Hello!

The maximum tag number you mentioned (5547) likely represents the total number of individual detection instances across the entire video, including all the frames where a person was detected. This number can be significantly higher than your in_count and out_count numbers because it accounts for each time a person is detected in any frame, regardless of whether they are entering, exiting, or simply being present without movement.

The in_count (751) and out_count (1334) specifically tally the number of times a person enters or exits the scene, which are distinct events that can be far fewer than the total detections. That's why these counts don't sum up to match the maximum tag number. Here's a rough illustration to conceptualize it:

• Total detections (maximum tag number) = Every time a person is detected in any frame.
• In_count + Out_count = Number of specific events (entering or exiting).

Your tracking logic appears correct. To analyze further, ensure your counting logic aligns with the scenario you are monitoring, and consider scenarios where individuals might be counted multiple times or missed based on how they move within the video frame.

Let me know if this clarifies things or if you have any more questions! 😊

[rrz888]

hello，when I predict a 2MB mp4 video，the model returns me a 50MB avi video, how can I get a smaller video and how can I specify the return type like .mp4? thx

[glenn-jocher]

@rrz888 hello! 😊 To reduce the output video size and specify the file format as .mp4, you can adjust the output settings when saving the video. Unfortunately, the default settings might save videos in a format like .avi which can result in larger file sizes. Here's a concise code example to save your video as .mp4 using OpenCV, which should help reduce the size:

import cv2

# Assuming 'results' contain your model predictions
# Define codec and create VideoWriter object (for .mp4 file)
fourcc = cv2.VideoWriter_fourcc(*'mp4v') # For .mp4 files
out = cv2.VideoWriter('output.mp4', fourcc, 20.0, (width, height))

for frame in results:
    # process your frame with YOLOv8 model, example:
    # frame = model.predict(frame)
    
    # Write the frame into the file 'output.mp4'
    out.write(frame)

# When everything done, release the video writer object
out.release()

Remember to replace (width, height) with your video's actual dimensions. This method provides a .mp4 output which usually results in a smaller file size compared to .avi.

Hope this helps! Let us know if you have any more questions.

[rrz888]

here are part of my codes:

results = model.predict('temp.mp4',stream=True, save=False, imgsz=320)
for frame in results:
out.write(frame)
out.release()

but I got an error:
cv2.error: OpenCV(4.9.0) 👎 error: (-5:Bad argument) in function 'write'

Overload resolution failed:

• image is not a numpy array, neither a scalar
• Expected Ptrcv::UMat for argument 'image'

I change the parameter：

out.write(frame.orig_img)

and I still can't get the right .mp4 video

[glenn-jocher]

@rrz888 hey there! 😊 It looks like you're encountering an issue where the frames returned by the predict() generator method aren't in a format recognized directly by OpenCV's write() function. The model.predict() method in streaming mode indeed returns a generator of Results objects, and each Result object contains various attributes including orig_img, which should be the original image frame as processed by the model. However, the fact that directly writing frame.orig_img did not resolve the issue is intriguing.

One cause of this issue might be related to the specific format or encoding of the frames. To ensure compatibility with OpenCV's write() function, you might need to explicitly ensure the frames are converted to the correct format. Since you mentioned getting the error even after changing to out.write(frame.orig_img), it might be worthwhile to verify that frame.orig_img is indeed a numpy.ndarray and in the correct shape and data type expected by OpenCV.

Here’s a revised approach to ensure the frames are suitable for OpenCV:

import cv2
from ultralytics import YOLO

# Assuming 'results' is a generator of Results objects obtained from model.predict(...)
model = YOLO('yolov8n.pt')
results = model.predict('temp.mp4', stream=True, save=False, imgsz=320)

# Specify the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for .mp4 files
out = cv2.VideoWriter('output.mp4', fourcc, 30, (640, 480))  # Adjust frame size (640, 480) as per your needs

for result in results:
    frame = result.orig_img  # Assuming orig_img is the correctly formatted frame
    if not isinstance(frame, np.ndarray):
        print("The frame is not in the expected format.")
        continue
    if frame is not None:
        out.write(frame)  # Write the frame to the video file

out.release()  # Release the VideoWriter

Make sure to replace (640, 480) with the actual dimensions of your input frames. Additionally, ensure you have initialized the VideoWriter with the correct frame size and fps.

For returning a video in .mp4 format with a smaller size, the code snippet uses the mp4v codec and writes the output to an .mp4 file, which generally yields a more compressed video than .avi.

If further adjustment to the video size is needed, you could consider additional compression steps post-creation or adjust the frame size and fps parameters to lower values, which can influence the output file's size.

Hope this helps clear things up! 🚀 Do let me know if you need further assistance.

[DogearQAQ]

I am using the model to finish a image classification task, how can i get the predictive result and put it in a list?

[pderrenger]

@DogearQAQ hi there! To get the predictive result of your image classification task into a list, you can access the probs attribute from the Results object returned by your prediction call. Here’s an example to do just that:

from ultralytics import YOLO

# Load your model
model = YOLO('path/to/your/model.pt')  # Replace 'path/to/your/model.pt' with your model's path

# Predict and store results
results = model('path/to/your/image.jpg')  # Replace 'path/to/your/image.jpg' with your image's path

# Extracting probabilities to a list
probs_list = results[0].probs.numpy().tolist()

print(probs_list)  # This prints the list of probabilities

This will give you a list containing the probabilities of each class for your image. Swap out 'path/to/your/model.pt' and 'path/to/your/image.jpg' with your actual model and image paths. Happy classifying! 😊

[DogearQAQ]

I found that there are only five classifications in the probs attribute. In other words, it seems like i can only see at most five classifications in my results, but if i have six classifications, how can i see them at once?

[pderrenger]

@DogearQAQ hey there! 👋 If you're looking to see more than five classifications in your results, you might want to check if your model and config settings are correctly set up to output more classes. By default, the probs attribute provides the class probabilities, and its length should match the number of classes your model is trained to predict.

However, the YOLO model typically displays the most confident class predictions (often top-5 for efficiency and clarity in visualizations), but you can adjust how many classes you want to see or manipulate the output as required programmatically.

If you're specifically looking to see all class probabilities for a given prediction, you can directly access the probabilities and manipulate or display them as needed. For a classification task, suppose you have six classes, and you want to see the probability of each class; you can access the probs attribute like so:

from ultralytics import YOLO

model = YOLO('your_model.pt')  # Load your model
results = model('your_image.jpg')  # Predict
probs = results[0].probs  # Get the probabilities

# If you want to print all probabilities
print(probs.numpy())  # Assuming you have more than five classes

This will give you the probabilities for all classes. Make sure your model is trained and configured to predict all six classes in your task. Happy coding! 🚀

[DogearQAQ]

When I run the program, words below will come out automatically, how can i make them disappear？
'image 1/1 D:\Download\pycharm\python-program\shichuang\ultralytics_main\Resized_Datasets\train\lh\left_head4997.(8).jpg: 640x640 lh 1.00, sp 0.00, sd 0.00, fh 0.00, rh 0.00, 179.5ms
Speed: 1.8ms preprocess, 179.5ms inference, 0.0ms postprocess per image at shape (1, 3, 640, 640)'

[pderrenger]

@DogearQAQ Hey there! 😊 To suppress the verbose output during prediction, you can utilize the verbose parameter. Setting verbose=False when calling your prediction function should help keep your console output clean. Here's how you could go about it:

from ultralytics import YOLO

# Load your model
model = YOLO('your_model.pt', verbose=False)  # This should suppress the verbose prediction logs

# Let's predict!
results = model('your_image.jpg')

# Now your console should stay neat and only display what you explicitly print out. ✨

Hope this helps you maintain a clutter-free console! If you need further adjustments, feel free to reach out! 🚀

[nwpu-caikun]

Can you give some demo code for deployment? For example,
1 on a cloud server (ali yun or aws) use fastAPI
2 on some edge computing device like NVIDIA's jeston.

[pderrenger]

Hey there! 😊 Here's a quick example for deploying with FastAPI on a cloud server like AWS and for deploying on an edge device such as NVIDIA Jetson.

Deploying with FastAPI on AWS:

First, install FastAPI and Uvicorn:

pip install fastapi uvicorn

Next, write your FastAPI app:

from fastapi import FastAPI, File, UploadFile
from ultralytics import YOLO
import numpy as np
from PIL import Image
import io

app = FastAPI()
model = YOLO('yolov8n.pt')  # Load your YOLO model
 @nwpu-caikun.post("/predict/")
async def predict(file: UploadFile = File(...)):
    image_bytes = await file.read()
    image = Image.open(io.BytesIO(image_bytes))
    image = np.array(image)
    results = model(image)
    return {"results": str(results)}

Run your FastAPI app:

uvicorn main:app --reload

Deploying on NVIDIA Jetson:

For NVIDIA Jetson, ensure you have the correct PyTorch version installed that is compatible with the Jetson platform. Then, deploy your YOLO model as you would normally do on any other device. Here's an example Python script for inference:

from ultralytics import YOLO
import cv2

model = YOLO('yolov8n.pt')

# Assuming you have a video stream or webcam
cap = cv2.VideoCapture(0) # Change 0 to your video source

while True:
    ret, frame = cap.read()
    if not ret:
        break
    results = model(frame)
    annotated_frame = results[0].plot()
    cv2.imshow('YOLOv8 Jetson Inference', annotated_frame)
    if cv2.waitKey(1) == ord('q'):  # Press q to quit
        break

cap.release()
cv2.destroyAllWindows()

For AWS deployment, remember to set up your server instance and place your FastAPI app there. As for the NVIDIA Jetson, ensure you've set up your device with the correct dependencies. Cheers!

[ntklw]

how can i change the save_dir and the save_crop_dir?

[pderrenger]

To change the save_dir and save_crop_dir when using the YOLOv8 CLI or in your Python code, you can utilize the --save-dir and project arguments respectively. For cropped images, utilizing the --save-crop argument along with specifying a directory using the project argument should do the trick. Here's how you can do it in both Python and CLI:

Python:

from ultralytics import YOLO

# Load your model
model = YOLO('yolov8n.pt')

# For prediction, specifying save directory and enabling cropped images save
results = model(['im1.jpg', 'im2.jpg'], project='path/to/save/results', save_crop=True)

CLI:

To change the directory where results are saved and enable saving cropped images:

yolo predict model=yolov8n.pt source='im1.jpg' project='path/to/save/results' save_crop

Remember, setting project customizes where your results (including both full and cropped images) are stored, while --save-crop activates saving detected objects as individual cropped images within the project directory. Hope this helps! 🚀

[yaoshisa]

How to add a segmentation head for lane detection based on the target detection model.

[pderrenger]

Hi there! Adding a segmentation head for lane detection to your YOLO-based detection model involves a few steps, including modifying the model architecture and adjusting the training process to include your segmentation task. Here’s a brief guide on how you might approach this:

1. Model Architecture: Include a new segmentation head in your model architecture that outputs a segmentation mask in addition to the existing detection outputs. This could be a separate convolutional layer designed to produce pixel-wise classification (segmentation mask).

2. Training Data: Ensure your training data includes segmentation labels for lanes. These labels are usually in the form of binary masks that indicate the presence of lanes.

3. Loss Function: Modify your loss function to include a term for segmentation loss, e.g., Binary Cross-Entropy loss for the lane segmentation task, alongside the detection loss.

4. Training: During training, you’ll need to feed both the detection and segmentation labels and calculate the loss for both heads of the network.

A simplistic code snippet for adding a segmentation head might look something like this (note, this is a high-level example):

import torch
import torch.nn as nn

class CustomYOLO(nn.Module):
    def __init__(self, yolov8_base):
        super(CustomYOLO, self).__init__()
        self.yolov8_base = yolov8_base  # Your pre-trained YOLO model base
        self.segmentation_head = nn.Conv2d(in_channels=2048, out_channels=1, kernel_size=1)  # Example segmentation head
        
    def forward(self, x):
        detection_features = self.yolov8_base(x)
        segmentation_output = self.segmentation_head(detection_features)
        return detection_features, segmentation_output

Remember, this is a high-level approach and the actual implementation will require careful architectural design and tuning. 🛠️

Keep diving into the Ultralytics docs for more insights on YOLO's prediction modes that could guide you in custom project applications involving tasks like detection and segmentation: 模型预测与Ultralytics YOLO.

Happy coding! 🚀

[yaoshisa]

How to choose the information to save in txt

[pderrenger]

Hey there! 👋

To customize the information you'd like to save in a .txt file during predictions with YOLOv8, you can use the save_txt option along with specifying the save_conf flag if you also want to include confidence scores for each detection. Here's a quick example on how to do it in Python:

from ultralytics import YOLO

# Load your model
model = YOLO('yolov8n.pt')  

# Perform predictions, enabling save_txt and save_conf
results = model(['your_image.jpg'], save_txt=True, save_conf=True)

# The .txt files are saved in the same directory as the images

This will create a .txt file for each image in the prediction, where each line corresponds to a detection in the format [class] [x_center] [y_center] [width] [height] [confidence].

I hope this helps! Let us know if you have any other questions. 😊

[yaoshisa]

Hello，Where does predictor return the results？

[pderrenger]

@yaoshisa hello! The YOLOv8 model predictions are returned as a list of Results objects when you call the model with stream=False or as a generator of Results objects when using stream=True. You can iterate through these to access various outputs like bounding boxes, masks, keypoints, and probabilities. Here's a quick example for both cases:

from ultralytics import YOLO

# Load the model
model = YOLO('yolov8n.pt')  

# For a list of Results objects
results = model(['im1.jpg', 'im2.jpg'])  
for result in results:
    print(result.boxes)  # Access bounding box outputs

# For a generator of Results objects (useful for processing videos or large datasets)
results_stream = model(['im1.jpg', 'im2.jpg'], stream=True)  
for result in results_stream:
    print(result.boxes)  

Each Results object has attributes like .boxes, .masks, .keypoints, and .probs that you can use to get the specific results. Let us know if you need more help! 😊

[yaoshisa]

Hello，How to customize the return result of the model？

[glenn-jocher]

Hello! Customizing the return result of a YOLOv8 model in Python is quite straightforward. You can access specific output attributes from the results object directly after inference. Here's a quick example showing how to access and print only the bounding boxes and their confidence scores:

from ultralytics import YOLO

# Load the pretrained model
model = YOLO('yolov8n.pt')

# Run inference
results = model('path/to/your/image.jpg')

# Access the bounding boxes
boxes = results[0].boxes.xyxy[0]  # xyxy format
confidences = results[0].boxes.conf[0]  # Confidence scores

# Print results
print("Bounding Boxes:", boxes.numpy())
print("Confidences:", confidences.numpy())

This way, you can tailor the output to fit your project's requirements. If you need more detailed customization, feel free to explore the Results class in the documentation for a full list of accessible attributes. 😊

[yaoshisa]

How to display the normal video playback speed in real time when detecting video files in yolov8?

[glenn-jocher]

To maintain the normal playback speed of a video during detection in YOLOv8, you can ensure your system has the adequate hardware to process the frames as they are ingested. If latency is observed, consider optimizing your model for speed by reducing the imgsz parameter, choosing a lighter model version (e.g., yolov8n instead of yolov8x), or by utilizing half-precision inference with the half parameter if your hardware supports it. Here's how you might adjust the code for improved speed:

from ultralytics import YOLO
import cv2

# Load the model with half precision
model = YOLO('yolov8n.pt', half=True) 

# Open the video
cap = cv2.VideoCapture("path/to/video.mp4")

while cap.isOpened():
    ret, frame = cap.read()
    if ret:
        # Make predictions
        results = model(frame)
        
        # Visualize and display the frame
        annotated_frame = results[0].plot()
        cv2.imshow('YOLOv8 Real-time Detection', annotated_frame)
        
        if cv2.waitKey(1) & 0xFF == ord('q'):  # Press 'q' to exit
            break
    else:
        break

cap.release()
cv2.destroyAllWindows()

Note: This example enables half-precision inference which can significantly speed up the processing time on compatible GPUs, helping maintain real-time performance. The actual improvement depends on your specific hardware setup. 🚀

[yooyo-Q]

when i use the "stream=True"，the code return an error "FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
ckpt = torch.load(file, map_location="cpu")

进程已结束，退出代码为 0"

what can i do to deal with this bug?

[glenn-jocher]

@younkun the warning you're seeing is related to the use of torch.load with weights_only=False, which is a security concern when loading untrusted models. To address this, you can set weights_only=True when loading your model. This change will ensure that only the model weights are loaded, mitigating the risk of executing arbitrary code. Please update your code to reflect this change and try running it again. If the issue persists, ensure you are using the latest version of the Ultralytics package. For more details, you can refer to the PyTorch security documentation.

[LJ-Hao]

How to change different batch size when I predict video with yolov8?

[pderrenger]

@LJ-Hao to change the batch size when predicting videos with YOLOv8, you can use the batch argument in the model.predict() method. For example, you can set batch=4 to process four frames at a time. Ensure you are using the latest version of the Ultralytics package for the best results. For more detailed information, you can refer to the Ultralytics documentation on prediction.

[Kiana-QvQ]

你好，我在以安装最新版yolo和torch11.8使用时发现预测十分卡顿，实时延迟可能有3到5秒，代码如下：

-- coding: utf-8 --

import torch
print(torch.cuda.is_available())
if torch.cuda.is_available():
print(torch.cuda.get_device_name(0))
print(torch.version.cuda)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

import cv2
from ultralytics import YOLO
model = YOLO("model/v11/yolo11n.pt")

cap = cv2.VideoCapture(1)

while cap.isOpened():
success, frame = cap.read()

if success:
    model.predict(source=frame, show=True)
if cv2.waitKey(1) & 0xFF == ord('q'):
    break

请问如何解决，谢谢

[glenn-jocher]

@Darling2333 to address the delay in prediction, try setting stream=True in your model.predict() call to optimize memory usage. Additionally, ensure your GPU drivers are up-to-date for better performance. For further details, please refer to the Ultralytics documentation.

[accidentalid]

can I save my predict result as .xml files?

[glenn-jocher]

@accidentalid currently, Ultralytics YOLO does not support saving prediction results directly as .xml files. You might consider converting the results to XML format using a custom script after obtaining them in a supported format.

[johnfans]

我很好奇，为啥同时import opencv和YOLO时，cv2.imread就能读取网络路径和包含中文路径的图片，但是只加载opencv时就不行，谁能告诉我这是什么原理，真的很好奇

[glenn-jocher]

@johnfans this behavior likely occurs because when the Ultralytics YOLO package is used, it internally handles file paths, potentially converting non-standard paths (such as those containing Chinese characters) to a standard format compatible with OpenCV. Without YOLO, OpenCV's native cv2.imread may struggle with such paths unless additional encoding/decoding steps are implemented. Let us know if you have further questions!

[LwpZoe]

Hello, I would like to ask, my task is instance segmentation. At present, I get the predicted result is the original image superimposed label image, but the result I want is just the RGB label image, no need to superimpose the original image. Where do I need to make changes to make the result a segmented label image?

[glenn-jocher]

@LwpZoe to obtain only the RGB label image without the original image overlay, you can disable the overlay during visualization. Use the plot() method with boxes=False and masks=True to only display the segmentation masks. Alternatively, you can directly access result.masks.data for raw mask data and process it as needed. Refer to the Results section for more details.

[LwpZoe]

Found plot () in yolo-segment-main/ultralytics/engine/results.py, modified content:
Def plot (
Self,
Conf = True,
line_width = None,
font_size = None,
Font = "Arial.ttf",
Pil = False,
Img = None,
im_gpu = None,
kpt_radius = 5,
kpt_line = False,
Labels = False,
Boxes = False,
Masks = True,
Probs = True,
Show = True,
Save = True,
Filename = 'result',
color_mode = "class",
):
Make predictions using the command line:
Yolo segment predict model = runs/segment/train/weights/best.pt source=yolo-seg-main/datasets/test
The result is still the original image overlay mask and box, why?
Does changing plot () require retraining to get best.pt in order to predict?

[glenn-jocher]

To display only the segmentation mask without overlays, modify the plot() function parameters by setting boxes=False, labels=False, and ensuring masks=True. You don't need retraining to adjust visualization settings, as the weights remain unaffected. For further details, refer to the Results.plot() documentation.

[maoxiuyuan]

hello, i encountered a problemduring the prediction process with YOLOv8n_improved model.
the log output is repeat as a illusrated below:
YOLOv8n_iRMB2 summary: 311 layers, 2742868 parameters, 0 gradients, 10.6 GFLOPs
YOLOv8n_iRMB2 summary: 311 layers, 2742868 parameters, 0 gradients, 10.6 GFLOPs
Ultralytics YOLOv8.0.29 Python-3.8.19 torch-2.3.1 CUDA:0 (NVIDIA GeForce RTX 4060 Laptop GPU, 8188MiB)
Ultralytics YOLOv8.0.29 Python-3.8.19 torch-2.3.1 CUDA:0 (NVIDIA GeForce RTX 4060 Laptop GPU, 8188MiB)
the repeated log output has different colors,one is red and the other is black
how can solve this problem ? i m glad to hear your reply

[glenn-jocher]

@maoxiuyuan thank you for reaching out. The repeated log output with different colors may occur due to redundant initialization of the model or multiple logger instances. Ensure that the model is not being loaded or executed multiple times unintentionally in your code. If the issue persists, please share a minimal reproducible example for further investigation.

[Sam-505]

    In the process of inference for long video streams, why is there a problem that the inference speed in the first few seconds is very fast and the inference speed in the back is very slow?
    How can this problem be solved? The inference process is as follows:

0: 640x640 1 Bottom, 1 Side, 11.3ms
Speed: 2.5ms preprocess, 11.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)
...
...
(≈ 3 seconds )
0: 640x640 1 Bottom, 1 Side, 115.2ms
Speed: 3.6ms preprocess, 115.2ms inference, 11.2ms postprocess per image at shape (1, 3, 640, 640)
...
...
(n seconds )

[glenn-jocher]

For long video streams, use stream=True in your model.predict() call to enable memory-efficient generators that prevent slowdowns from accumulated memory usage. This is documented in our Predict Mode guide: https://docs.ultralytics.com/modes/predict/#streaming-for-loop. The speed degradation typically occurs when processing frames without streaming, as all results remain in memory.

[Tigores]

你好，我想问一下为什么我在推理相同的图像时，设置不同的imgsz参数，会有不同的结果。在一张图像上，有大船、小船、人。imgsz设置的值有640（默认）、1280、1980。640只能检测到大船；1280会检测到大船和小船，并且大船的置信度更高；1980还能检测到人。我很疑惑，模型的输入尺寸不是在训练的时候就已经固定了吗，这是否意味着推理的图像预处理阶段，不管我如何调整imgsz参数，模型都会将输入图像采样到640。但是现在出现的不同的结果，是为什么呢？下面是我的代码：
from ultralytics import YOLO
model = YOLO("/data/dxh/ultralytics/weight/yolov8s.pt")
results = model("/data/dxh/ultralytics/datasets/test/invade_ship_cls/invade_result", imgsz=1280, save=True)

[glenn-jocher]

@Tigores 您好！YOLO模型在推理时会自动将输入图像缩放到您指定的imgsz参数值（而非固定为训练尺寸），更大的分辨率能保留更多细节从而提升小目标检测能力。具体原理可参考Predict模式文档中的图像尺寸与精度关系说明，这是预期行为而非异常现象。

[yuanshuai666]

Dear Author,
Could you please advise how to perform inference on an ONNX model using Ultralytics YOLOv8?
Could you also provide a code example?

[glenn-jocher]

@yuanshuai666 to perform inference with an ONNX model using Ultralytics YOLO11, simply load the model directly and run predictions as you would with a PyTorch model. Here's a minimal example:

from ultralytics import YOLO

model = YOLO('yolo11n.onnx')  # Load exported ONNX model
results = model.predict(source='image.jpg')  # Run inference

The inference workflow remains identical across model formats. For more details on model export and deployment, see the Ultralytics YOLO Export documentation.

[yuanshuai666]

Dear Author,
How can we avoid accuracy loss when converting a .pt model to an engine model?
Some objects can be detected with the .pt model, but after converting to the engine model, they can no longer be recognized.

[glenn-jocher]

@yuanshuai666 to minimize accuracy loss when converting YOLO models to TensorRT engines, ensure:

1. Input image sizes match between original and converted models (imgsz parameter)
2. Floating-point precision consistency (avoid unnecessary FP16/INT8 quantization during export)
3. Use latest Ultralytics and TensorRT versions for optimal compatibility

For detailed export guidance see the Ultralytics YOLO Export documentation. If issues persist, please share a minimum reproducible example using our Bug Report template.

[fatcar2002]

Hello, help
The weights of my training can't be predicted.
The training code is:
model = YOLO("yolov11n.pt")

Train the model

results = model.train(data="vvv.yaml",
epochs=100,
imgsz=640,
workers=0,
batch=16)
vvv.yaml is custom for me:
path: .. /datasets/coco # dataset root dir
train: images/train # train images (relative to 'path') 4 images
val: images/val # val images (relative to 'path') 4 images
test: # test images (optional)

Classes

names:
0: body
1: head
2: gbody
3: ghead
The best.ph generated after training. I renamed him and put it in another project:
self.model = YOLO("D:yolo11/best1.pt",task='detect')

results = self.model.predict(image,
save=False,
half=False,
conf=0.5,
imgsz=1120,
stream=True,
verbose=True,
iou=0.3
But he doesn't work. The target could not be detected.
But the strange thing is, if I only train an epoch and it can recognize the target normally, what's my problem?
Thanks!

[fatcar2002]

I got it. It is important that the trian folder does not contain irrelevant pictures.
I thought it was the lable file name corresponding to the image file name, but it seems to be the corresponding file order.

[glenn-jocher]

@fatcar2002 Thanks for sharing your solution! You're right about the dataset organization being crucial. In Ultralytics YOLO, label files should precisely match image filenames (just with .txt extension instead of .jpg/.png) and should be placed in a "labels" directory at the same level as the "images" directory. Any mismatch in filenames or irrelevant images without proper labels can indeed cause training issues that affect prediction performance.

For optimal training results, ensure your dataset follows this structure:

dataset/
├── images/
│   ├── train/
│   ├── val/
│   └── test/ (optional)
└── labels/
    ├── train/
    ├── val/
    └── test/ (optional)

Glad you were able to resolve your issue! Let us know if you have any other questions about training or prediction with YOLO models.

[d5423197]

Hi there,

If no boxes/masks detected by the model, what will be the output of model inference. (results = model(["image1.jpg", "image2.jpg"]) #)

I want to deal with the edge case.

[hanchaoyuan]

hello! I used the YOLO11-pose model to estimate the key points of people in the automotive production workshop. However, the model often identified vehicles, hoists and other equipment as humans and calculated the key points, resulting in a relatively high false alarm rate. My question is: How can we prevent the model from identifying other objects and reduce the false alarm rate?

[glenn-jocher]

For workshop false positives with pose models, the quickest lever is raising the prediction threshold (and optionally increasing imgsz for better person discrimination), but the most reliable fix is fine-tuning on your own workshop data with “hard negative” images (frames with vehicles/hoists but no people) so the model learns your background; see the inference args in the predict mode docs.

yolo pose predict model=yolo11n-pose.pt source=path/to/video.mp4 conf=0.6 imgsz=960

If you paste your exact command, plus a couple representative frames (people + false positives) here in the discussion, we can suggest tighter settings or a training setup (and for new work, try a YOLO26 pose checkpoint as it’s generally stronger out of the box).