tasks/classify/

[giscus[bot]]

tasks/classify/

Learn about YOLOv8 Classify models for image classification. Get detailed information on List of Pretrained Models & how to Train, Validate, Predict & Export models.

https://docs.ultralytics.com/tasks/classify/

[William-Yao0993]

I have a pretrained model that can do detection and segmentation with my custom dataset, Can the model continue be trained for classification purpose, and predicts by passing the task argurment? Or its better to train and save a new model for classify purpose?

[pderrenger]

@William-Yao0993 for a pretrained model that is already capable of detection and segmentation on your custom dataset, you can indeed continue training it for classification purposes. This is feasible because YOLOv8 supports multi-task learning, allowing a single model to perform various computer vision tasks, including classification.

When you continue training your model for classification, you can utilize the task argument to specify the classification task during training and prediction. This approach can be efficient as it leverages the existing learned features from your model, potentially reducing the training time and resources needed for the new task.

However, if you prefer a more specialized model or if the classification task is significantly different from the detection and segmentation tasks, you might opt to train a new model dedicated to classification. This could lead to better performance on the classification task since the model would be fine-tuned specifically for that purpose.

Ultimately, the decision depends on your specific requirements, such as the level of performance you need and the computational resources you have available. If you have the capacity, you might want to experiment with both approaches and compare the results.

[William-Yao0993]

That is a really good answer. Thanks for your help.

[pderrenger]

@William-Yao0993 you're welcome! If you have any more questions or need further assistance as you work on your project, feel free to reach out. Good luck with your training and predictions! 😊🚀

[sh3mmy]

@pderrenger i'm trying to do something similar, i've trained a classification network and now i would like to retrain the network to do both classify and detection. how would i go about this? do i need a different data set?

im using colab so i attempted to retrain it like this:

!yolo task=detect mode=train model=/content/drive/MyDrive/runs/classify/train7/weights/best.pt data = {DATA}

but i got this error:

WARNING ⚠️ conflicting 'task=detect' passed with 'task=classify' model. Ignoring 'task=detect' and updating to 'task=classify' to match model.

[pderrenger]

It seems like you're trying to retrain a classification model to also perform detection. To achieve this, you'll need a dataset that includes both class labels and bounding box annotations. The error you encountered indicates that there's a conflict between the task specified in the command (task=detect) and the task inferred from the model (task=classify).

To resolve this, you should ensure that your model is compatible with detection tasks. This typically requires a model architecture that supports both classification and detection, and a dataset prepared with the necessary annotations for detection.

If you're using a model trained only for classification (like yolov8n-cls.pt), it won't be directly applicable for detection tasks without modification. You might need to train a new model with a suitable architecture (like yolov8n.pt) on a dataset that includes the required detection annotations.

For more guidance, please refer to the Ultralytics documentation on training models for detection tasks.

[William-Yao0993]

@pderrenger i'm trying to do something similar, i've trained a classification network and now i would like to retrain the network to do both classify and detection. how would i go about this? do i need a different data set?

im using colab so i attempted to retrain it like this:

!yolo task=detect mode=train model=/content/drive/MyDrive/runs/classify/train7/weights/best.pt data = {DATA}

but i got this error:

WARNING ⚠️ conflicting 'task=detect' passed with 'task=classify' model. Ignoring 'task=detect' and updating to 'task=classify' to match model.

I think you need to modify the dataset structure to be compatible with Detection as @pderrenger stated and might need to use .load() to transfer weights from YOLO pretrained or customed model for detection, because from my experience, I transferred weights from 'yolov8-seg' when transfer detection to segmentation model, it should be similar to classify. Please Correct me if I am wrong.

[glenn-jocher]

Yes that's correct, you can use mode.load() to transfer weights from another checkpoint. Weights can transfer across tasks as well to a certain extent, i.e. you can transfer many detection model weights to a pose model for example.

from ultralytics import YOLO

# Create a new detection model and transfer the weights from a classification model
detect_model = YOLO('yolov8n.yaml').load('path/to/classification/model.pt')

# Now you can train for detection
detect_model.train(data='path/to/data.yaml', epochs=3)

[Vinrub]

i want solo object detection code for video then want to merge my object half identification code status = False
img1_yCrCb_r = cv2.cvtColor(right_light_img1, cv2.COLOR_BGR2YCrCb)
img1_yCrCb_l = cv2.cvtColor(left_light_img1, cv2.COLOR_BGR2YCrCb)

img2_yCrCb_r = cv2.cvtColor(right_light_img2, cv2.COLOR_BGR2YCrCb)
img2_yCrCb_l = cv2.cvtColor(left_light_img2, cv2.COLOR_BGR2YCrCb)

img1_Y_r,img1_Cr_r,img1_Cb_r = cv2.split(img1_yCrCb_r)
img1_Y_l, img1_Cr_l, img1_Cb_l = cv2.split(img1_yCrCb_l)

img2_Y_r,img2_Cr_r,img2_Cb_r = cv2.split(img2_yCrCb_r)
img2_Y_l, img2_Cr_l, img2_Cb_l = cv2.split(img2_yCrCb_l)


ret, thresh1_r = cv2.threshold(img1_Cr_r, 200, 255, cv2.THRESH_BINARY)
cv2.imshow("thresh1_r",thresh1_r)
cv2.waitKey(0)
ret, thresh1_l = cv2.threshold(img1_Cr_l, 200, 255, cv2.THRESH_BINARY)

ret2, thresh2_r = cv2.threshold(img2_Cr_r, 200, 255, cv2.THRESH_BINARY)
ret, thresh2_l = cv2.threshold(img2_Cr_l, 200, 255, cv2.THRESH_BINARY)
cv2.imshow("thresh1_r",thresh1_r)
cv2.waitKey(0)
non1_r = cv2.countNonZero(thresh1_r)
non1_l = cv2.countNonZero(thresh1_l)

non2_r = cv2.countNonZero(thresh2_r)
non2_l = cv2.countNonZero(thresh2_l)

non1 = non1_r+non1_l
non2 = non2_r + non2_l

difference = abs(non1 - non2)

print(difference)

[glenn-jocher]

@Vinrub hey there! 👋 It looks like you're interested in performing object detection on video frames and then integrating some custom processing based on the detection results. Here's a simplified approach to get you started with object detection using a YOLOv8 model. After detection, you can incorporate your specific processing logic.

First, ensure you have the ultralytics package installed:

pip install ultralytics

Here's a basic example to run object detection on a video stream using YOLOv8:

from ultralytics import YOLO
import cv2

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

# Open your video
cap = cv2.VideoCapture('path/to/your/video.mp4')

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

    # Run detection
    results = model(frame)

    # Extract detected objects (for example, filtering by confidence and class)
    for *box, conf, cls in results.pred[0]:
        if conf > 0.5:  # Example confidence threshold
            # Your custom processing here
            pass

    # Example: Show the frame with detections
    cv2.imshow('Frame', np.squeeze(results.render()))
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

For the second part of your question, regarding merging object identification code, it seems you're processing color channels and applying thresholds. You can integrate this logic after the detection step, using the detected object's bounding box coordinates to isolate regions of interest in your frames for further processing.

Remember, the key is to adapt the detection loop to your specific needs, whether it's processing certain object classes differently or applying custom logic based on detection results. If you have more detailed requirements or need further assistance, feel free to ask! 🚀

[mansoormirza]

I trained a yolov8-l classification model where I was perform classification on some images, few months back it was working perfectly fine, was classifying accurately, but few days ago, when I ran the model again on an image I got an error, something like this

This is the code, where I am loading a trained model .pt file

from ultralytics import YOLO
import numpy as np

model = YOLO('/content/drive/MyDrive/best.pt')
results = model('/content/image.jpg')

And Getting error like this:

AttributeError Traceback (most recent call last)
in <cell line: 6>()
4 model = YOLO('/content/drive/MyDrive/best.pt')
5
----> 6 results = model('/content/image.jpg')
7
8

9 frames
/usr/local/lib/python3.10/dist-packages/PIL/Image.py in getattr(self, name)
527 deprecate("Image categories", 10, "is_animated", plural=True)
528 return self._category
--> 529 raise AttributeError(name)
530
531 @Property

AttributeError: shape

Cant figure out this error, Kindly If anyone could help with this.

[pderrenger]

Hello! It looks like you're encountering an AttributeError: shape when trying to run predictions with a YOLOv8 classification model. This type of error is often related to how the data is processed or accessed, but without the complete error message, it's a bit challenging to pinpoint the exact issue.

That said, the code snippet you've provided for loading and running the model seems correct. Make sure the image path is correct and accessible. Also, ensure there haven't been updates to the Ultralytics YOLO package that might require changes in how models are loaded or executed. A good first step is to check if your Ultralytics YOLO version is up to date:

pip install ultralytics --upgrade

Additionally, make sure your .pt model file hasn't been corrupted or altered. If possible, try re-downloading or re-training the model to ensure its integrity.

If you're still facing the issue, could you share a bit more detail about the complete error stack or any changes made to the environment or code since it last worked? That could give us better clues towards a solution. 🕵️‍♂️

Let us know how it goes, and we'll be here to assist further!

[skfirebox]

i have 73 bad images and 4000 good images, can yolov8 work for this or not (For classification problem), if yes , then what i will do ?
also tell me how i run yolov8 in my system for classification problem on Custom Dataset?

[glenn-jocher]

@skfirebox hello! Absolutely, YOLOv8 can work on your classification problem with a dataset comprising 73 bad images and 4000 good images. Given the imbalance, you may consider using techniques like oversampling for the minority class or applying class weights during training for better performance. 📊

For setting up YOLOv8 for classification on your custom dataset, you'll be using a YOLOv8 Classify model, like the yolov8n-cls.pt which is pretrained on ImageNet. Here's a quick guide:

1. Ensure your dataset is prepared according to the YOLO classification dataset format.
2. Install the Ultralytics YOLO package if you haven't already:

   pip install ultralytics

3. Load and train your model:

   from ultralytics import YOLO
   
   # Load a pretrained classification model
   model = YOLO('yolov8n-cls.pt')
   
   # Train the model on your dataset
   model.train(data='path/to/your_dataset.yaml', epochs=100, imgsz=224)

   Replace 'path/to/your_dataset.yaml' with the path to your dataset configuration file.

Or via CLI:

yolo classify train data=path/to/your_dataset.yaml model=yolov8n-cls.pt epochs=100 imgsz=224

Remember to customize epochs, imgsz, or any other relevant parameters as needed to suit your dataset better.

For further details on classification with Ultralytics YOLO, including how to validate, predict, and export your models, refer to the YOLOv8 classification documentation.

All the best with your classification project! If you have more questions, feel free to ask. 🚀

[supj8]

What are the main differences in the model architecture of Classify and Detect?

[pderrenger]

@supj8 hey there! 😊 Great question about the differences in model architecture between Classify and Detect tasks in YOLOv8.

The main difference lies in their objectives and output:

• Classify (YOLOv8-cls models): Focuses on classifying the entire image into one of the predefined classes. It's simpler since the output is a single class label and a confidence score. It's excellent when you only need to know the category of the whole image without the specifics of object locations. These models are pretrained on the ImageNet dataset, reflecting their focus on broad image classification.

• Detect (YOLOv8 models without the -cls suffix): Aims at detecting objects within an image, drawing bounding boxes around them, and classifying each detected object. It's more complex because the output includes locations (bounding boxes), class labels, and confidence scores for multiple objects within the same image. These models are typically pretrained on the COCO dataset, which is rich in object localization and classification data.

In essence, Classify models are about "what is in this picture?" while Detect models answer "what and where are the objects in this picture?"

Here's a little Python snippet to illustrate loading a classification model:

from ultralytics import YOLO

model = YOLO('yolov8n-cls.pt')  # Load a pretrained classification model

And for detection:

from ultralytics import YOLO

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

Hope this helps clarify things! Let me know if you have any more questions. 🌟

[supj8]

Thanks for the response! Specifically though, are you able to tell me the difference at a fundamental level? For example, I have seen in the .yaml files than Classify does not have the SPPF module in the backbone. What are the other differences in the neck and head? Or is there somewhere I can read about this instead of inspecting the source code? Thanks!

[pderrenger]

@supj8 hey there! 😄 Absolutely, I can shed some light on that. Beyond the SPPF module difference in the backbone for Classify compared to Detect models, there are other architectural variations primarily focused around the model's objective - classification vs. object detection.

Classify models (e.g., YOLOv8-cls), designed purely for image classification, have a more streamlined structure since their main job is to assign a label to the entire image. This often means a simpler "head" architecture, as there's no need for the additional complexity required to predict bounding boxes or object masks.

On the other hand, Detect models include additional layers and structures in the "neck" and "head" to handle the localization (where the objects are) and classification of multiple objects within an image. This can include various feature pyramid networks (FPN) or panoptic feature pyramid networks (PFPN) in the neck to merge feature maps from different layers, and more complex head structures for bounding box regression, object classification, and sometimes even segmentation.

For a deep dive without delving into source code, the Ultralytics YOLO Documentation is a fantastic place to start! Check out the sections on Image Classification for insights on Classify models and Object Detection for Detect models. These pages provide an overview of what each model type is tailored for, including some hints on their internal workings.

Hope this helps clear things up a bit! If you've got more questions, feel free to toss them my way! 🚀

[eladoni1]

Hi there!
Quick briefing - the images are of size Width = 1216, Height = 292.

TL;DR - After debug into train.py & predict.py, realized the images are resized + cropped strangely from what I gathered in the docs/forums, even with specified imgsz and rect=True.

Full explanation -

Training was :
model.train(data=..., imgsz=1216, rect=True)

Expectations :
Was expecting the dataset images to be resized BY THE LARGER side, so 1216 will remain 1216, and so is the smaller side.

(Even when I tried to do imgsz as a tuple - (h,w) aka (292,1216), a warning showed up, telling me the imgsz will be automatically selected to 1216, as it should have been an integer.. not a tuple.)

What actually happened :
What happened is the smaller side (height, 292) resized to 1216, then the original, bigger side (width, 1216), was resized to 5063 (same scalar the smaller side was multiplied).

Meaning, what previously was image of size 1216x292, turned into image of size 5063x1216 (This is due to Resize transformation of pytorch)

And its not over - the data taken was only the INNER 1216x1216 - less than 1/4 of the actual data.. (This is due to CenterCrop transformation of pytorch)

So, basically, I am asking - Is there a quote from the docs I have missed that said classify must be square sized? Or is there another way (except from padding to 1216x1216 - adding 924 black pixels to the height), to make classify work with rectangular images?

Thank you so much, and sorry if I wasnt clear.. hopefully it was.

[glenn-jocher]

Hey there! 🙌 Thanks for the detailed message.

It seems like you've encountered an interesting scenario with image sizes and the rect=True parameter. In YOLOv8, when using rectangular training (rect=True), the model does expect square input sizes for simplicity and efficiency. The behavior you've noticed, where the smaller dimension gets resized to match the specified imgsz, and then the image is center-cropped, is by design for handling non-square inputs.

For your specific case with rectangular images (1216x292), instead of resizing or padding the images manually to make them square, a recommended approach is to work with the default behavior but ensure your imgsz is set to the dimension that avoids data loss. If further customization is necessary, it might involve custom modifications to the data preprocessing pipeline.

Unfortunately, there isn't a built-in switch for direct handling of non-square images without cropping or resizing as you described. This is an area for potential improvement in future updates!

For now, perhaps consider either adjusting your pipeline to accommodate this behavior or implementing a custom preprocessing step that ensures your images maintain their aspect ratio without significant data loss. Padding might be an unavoidable step for cases like these.

If there's anything else we can help with or if you have suggestions, feel free to reach out. Thanks for diving deep into this, and we're here to support your projects! 🚀

[Hzw12138]

Hi, I have identified a small issue in the example provided for training in the classification task, which is 'results = model.train(data='mnist160', epochs=100, imgsz=64)'. The parameter 'imgsz' should actually be 640.

[glenn-jocher]

@Hzw12138 hey there! 😊 Thanks for pointing out the discrepancy in the image size parameter. The imgsz value in the training example indeed serves as a placeholder, meant to be adjusted according to specific model requirements and dataset characteristics. For typical classification tasks using models like yolov8n-cls, the input size is often set to lower dimensions tailored to the task, differing from object detection model sizes like 640. If training on MNIST160, which has smaller images, an imgsz of 64 or similarly scaled dimensions might be appropriate depending on the dataset specifics.

For larger or different image datasets, adjusting imgsz to match your model and data requirements would be the way to go. For instance, using:

results = model.train(data='your_dataset', epochs=100, imgsz=224) 

or even 640, if that's what works best for you. Hope this clears things up! Let us know if there's anything else we can help with. Happy training!

[nepulepu]

When I try to train my custom data, it raised Runtime Error

    raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
RuntimeError: Dataset 'path/to/dataset/data.yaml' error  [WinError 3] The system cannot find the path specified: 'path\\to\\dataset\\data.yaml\\train'

while in my yaml file is

train: ../train/images
val: ../valid/images
test: ../test/images

nc: 4
names: ['a', 'b', 'c', 'd']

why it did not go into train folder?

[glenn-jocher]

Hi there! It looks like the system cannot find the specified path based on the error you've encountered. This often happens when there's a discrepancy between the paths defined in your dataset's YAML file and the actual file paths on your system.

Based on your data.yaml, your dataset structure should be something like:

- Your_project_folder/
  - train/
    - images/
  - valid/
    - images/
  - test/
    - images/
  data.yaml

And your data.yaml should look like this if the data.yaml is located in Your_project_folder:

train: ./train/images
val: ./valid/images
test: ./test/images

nc: 4
names: ['a', 'b', 'c', 'd']

Please ensure that:

1. The paths to your train, val, and test folders are correct relative to the location of your data.yaml file.
2. All specified directories exist in your filesystem.

If your YAML file is correctly set and you're still facing issues, it might be helpful to use absolute paths in the YAML file to troubleshoot the problem. Also, ensure that there are no typos in the path.

Hope this helps! Let us know if you have further issues. 😊

[nepulepu]

Hello,
Thanks for your response.
It works when I use the object detection model but if I use suffix '-cls' model it has error at

venv-path\lib\site-packages\ultralytics\engine\trainer.py

where the get_dataset function like this:

    def get_dataset(self):
        """
        Get train, val path from data dict if it exists.

        Returns None if data format is not recognized.
        """
        try:
            if self.args.task == "classify":
                data = check_cls_dataset(self.args.data)
            elif self.args.data.split(".")[-1] in {"yaml", "yml"} or self.args.task in {
                "detect",
                "segment",
                "pose",
                "obb",
            }:
                data = check_det_dataset(self.args.data)
                if "yaml_file" in data:
                    self.args.data = data["yaml_file"]  # for validating 'yolo train data=url.zip' usage
        except Exception as e:
            raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
        self.data = data
        return data["train"], data.get("val") or data.get("test")

is the dataset has different format and if it has, what format should i export at roboflow?
Thanks

[glenn-jocher]

@nepulepu hey there! 👋 It looks like you're facing an issue with dataset paths when using a classification model (-cls). The error message indicates a problem with finding the specified dataset path in your YAML file.

For classification tasks with YOLOv8, the dataset structure is a bit different from detection tasks. Make sure your dataset is prepared according to the YOLO classification dataset format guide.

If you're exporting your dataset from Roboflow for classification, you should export it as Classification (Folders) format, which organizes images into subfolders named after their classes.

Your data.yaml should look like this for classification tasks:

train: path/to/train/directory  # Directory containing class-named subdirectories with images
val: path/to/val/directory      # Same as above for validation data

nc: 4                           # Number of classes
names: ['a', 'b', 'c', 'd']     # Class names

Ensure the train and val paths are accurate and point to the directories containing your class-named subdirectories. 📁

Feel free to reach out again if you face any more issues!

[okurx]

Hello, ı hope your day is going well.
I am trying to make a race detection model with yolov8x classification. I have custom dataset and 6 class i have 5377 images for each train class folder and i have 654 images for each test class folder.

• /content/data/

  • train/
    • asian/
      -images/
    • latino/
      -images/
    • white/
      -images/
      .....

• /content/data/

  • test/
    • asian/
      -images/
    • latino/
      -images/
    • white/
      -images/
      .....

I run this code and ı get max 0.764 acc. I want to improve my acc value. I changed optimizer but it did not improve. What should ı do. Please help me :((
import os
from ultralytics import YOLO

Load a model

model = YOLO("yolov8x-cls.pt") # load a pretrained model

Train the model and save the results

results = model.train(data='/content/data', epochs=200, imgsz=224, scale=0)

[pderrenger]

Hello! Your project sounds exciting, and it's great to see you using YOLOv8x for race detection. Achieving a max accuracy of 0.764 is a good start, but I understand wanting to push that further. Here are a couple of recommendations that might help improve your model's accuracy:

1. Data Augmentation: Consider applying more robust data augmentation techniques. This could include varying lighting conditions, rotations, and flips. It can help make your model more robust to changes in input.

2. Learning Rate Schedule: Experimenting with different learning rate schedules might be beneficial. Sometimes, a dynamic learning rate (e.g., using ReduceLROnPlateau) could help in converging to a better minimum.

3. Model Complexity: You are using yolov8x-cls.pt, which is quite powerful. If your dataset is highly specialized, consider modifying the architecture to better suit your dataset's specificity. This might involve changing the model's depth or width for optimal performance.

4. Hyperparameters Tuning: Beyond the optimizer, tuning hyperparameters (like weight_decay, momentum, etc.) can have a substantial effect. Consider using automated tools like WandB or HyperOpt for systematic experimentation.

5. Regularization and Dropout: Ensuring you’re not overfitting with regularization techniques and potentially employing dropout in your network might help in achieving better generalization.

Remember, improving a model can sometimes be a matter of trial and error, so patience is key. Here’s a quick example of introducing a learning rate scheduler with your current setup:

from ultralytics import YOLO

# Load the model
model = YOLO("yolov8x-cls.pt")

# Define a scheduler (example)
scheduler = {'lr0': 0.01, 'lrf': 0.1, 'momentum': 0.937, 'weight_decay': 0.0005}

# Train the model with scheduler
results = model.train(data='/content/data', epochs=200, imgsz=224, scheduler=scheduler)

Please keep in mind that adjusting the learning rate scheduler directly in the train method like the example above is a simplified illustration. You might need to dive deeper into the training configuration or the codebase to effectively integrate more complex scheduling strategies.

Hope this helps! Keep experimenting, and I’m sure you’ll see improvements soon. 😊

[sourestdeeds]

How would i write a custom loss function to be used only during training, and the default loss for validation?

I tried this in tasks.py

def init_criterion(self):
        """Initialize the loss criterion for the ClassificationModel."""
        return v8ClassificationCustomLoss(self) if self.training else v8ClassificationLoss()

[pderrenger]

Hey there! 👋

It looks like you're on the right track with customizing the loss function based on the training or validation phase. Your approach in tasks.py is generally correct for switching between different loss functions during training and validation.

However, make sure that the self.training attribute is correctly toggled between True and False during the training and validation phases. This attribute should be managed by the training script, typically set by calling model.train() for training and model.eval() for validation.

Here's a quick example to ensure it's set correctly:

if self.training:
    print("Training mode: Using custom loss")
else:
    print("Validation mode: Using default loss")

Add this print statement inside your init_criterion method to debug and confirm that the switch is happening as expected.

If you continue to face issues, ensure that your custom loss functions (v8ClassificationCustomLoss and v8ClassificationLoss) are implemented correctly and are compatible with your model's output.

Happy coding! 😊

[mr-lz]

Hello, can a pretrained detection model be modified and applied to a classification task?

[pderrenger]

Hello! Yes, you can adapt a pretrained detection model like YOLOv8 for a classification task, but it involves modifying the model architecture to handle classification-specific outputs. For straightforward classification tasks, I recommend using a dedicated classification model like yolov8n-cls.pt, pretrained on ImageNet for better performance.

Here's how you can load and use a dedicated classification model:

from ultralytics import YOLO

# Load a pretrained classification model
model = YOLO('yolov8n-cls.pt')

# Predict on an image
results = model('path/to/image.jpg')
print(results)

This approach ensures that the model is optimized for accurate image classification right out of the box. Let me know if there's anything else you need! 😊

[dalmeidalorraine]

Rewrite: I have an image classification use case where the images could belong to class 1 or class 2, both class 1 and class 2 and neither. What's the best way to setup this classification using ultralytics image classifiers? Currently I have 4 folders class 1, class2, both, neither. Can you provide suggestions?

[dalmeidalorraine]

I have an image classification use case where images could belong to class 1, class 2, both class 1 and class 2, or neither. What's the best way to set up this classification using Ultralytics image classifiers? Currently, I have four folders: class 1, class 2, both, and neither. Should I stick with this setup, or would it be better to have just three folders: class 1, class 2, and neither? Any suggestions?

[pderrenger]

@dalmeidalorraine your setup with four folders (class 1, class 2, both, neither) is almost spot-on for handling the multi-label classification scenario you've described. However, it's crucial to approach this as a multi-label (not multi-class) task because each image might belong to multiple categories.

For handling such scenarios using Ultralytics image classifiers, you would typically define your dataset similarly to how you've organized it but ensure that each image can have multiple labels. Here's a small example to structure your data labels correctly:

1. Prepare your dataset: This could be a CSV file or similar, where each image path is listed along with its corresponding labels like so:

   path/to/image.jpg, class1, class2
   path/to/another/image.jpg, neither
   

   This can also be set up in a JSON or YAML format depending on what the model expects.

2. Set up your classification: When you load your model and dataset, make sure the model understands this is a multi-label classification task. This might involve some adjustments in your model configuration if your scenario is not typically a binary classification for each label.

3. Training the model: Use a suitable loss function that can handle multi-label scenarios, such as Binary Cross-Entropy, which is commonly used for multi-label classification tasks.

Here’s an example of how you might configure your dataset:

from ultralytics import YOLO
# Load the model
model = YOLO('yolov8n-cls.yaml')

# Setup for multi-label (dummy dataset paths)
data = {
  'train': '/path/to/train_images',
  'val': '/path/to/val_images',
  'nc': 4,  # number of classes
  'names': ['class1', 'class2', 'both', 'neither']
}

# Set up your training
results = model.train(data=data, epochs=100, imgsz=640)

The above code is an initial pointer; adjust the paths and parameters as needed for your setup. Make sure all labels for each image are properly marked in your dataset files. This will help the model learn to predict multiple classes simultaneously from your images. Keep the four categories as they represent separate labels clearly, distinguishing between individual and combined classes!

[dalmeidalorraine]

Hi, thanks for your response. Step 1 is not entirely clear to me. Could you please clarify how I can pass that as an input to the ultralytics classification model?

[pderrenger]

Hello! I'm glad to help clarify how you can input your data for image classification using Ultralytics models. 🌟

For image classification tasks, you typically need to prepare your dataset in a format that the model can process. This usually involves organizing your images into a structured directory or listing them in a file with their corresponding labels.

Here’s a quick example of how you might set up your data for a classification model:

1. Organize your images: Place your images in folders named after their respective classes. For example:

   /dataset
     /class1
       - img1.jpg
       - img2.jpg
     /class2
       - img3.jpg
       - img4.jpg
   

2. Prepare a dataset file: Create a .yaml file that specifies the paths to your training and validation datasets, along with class names. For example:

   path: /path/to/dataset  # dataset root dir
   train: train/images  # relative to 'path', train images
   val: val/images  # relative to 'path', validation images
   nc: 2  # number of classes
   names: ['class1', 'class2']  # class names

3. Load and train your model:

   from ultralytics import YOLO
   
   # Load a model
   model = YOLO("yolov8n-cls.pt")  # load a pretrained model
   
   # Train the model
   results = model.train(data="path/to/your/dataset.yaml")

This setup should help you get started with training your classification model on your dataset. If you need further assistance or have more specific requirements, feel free to ask! 😊

[wenching33]

Hi,
What is the preprocessing parameter of the default training process ?
I mean after resizing to (640,640), do you do (x/255 - mean)/std ?, what will be the mean, std value?

[glenn-jocher]

Hello!

For the default training process, the preprocessing steps after resizing the image to (640,640) include normalization. The images are normalized using the formula (x / 255 - mean) / std. The mean and std values used are typically:

• mean: [0.485, 0.456, 0.406]
• std: [0.229, 0.224, 0.225]

These values are standard for models pre-trained on the ImageNet dataset. This normalization helps in stabilizing the learning process and leads to faster convergence.

If you need to adjust these for your specific dataset, you can modify them in the dataset configuration or during the data loading phase. Happy coding! 😊

[wenching33]

Hi~
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
Are these in RGB order ? or BGR order ?
Thanks

[glenn-jocher]

Hi there! 👋

Yes, the mean and std values you mentioned ([0.485, 0.456, 0.406] for mean and [0.229, 0.224, 0.225] for std) are indeed in RGB order. This is the standard order used for models that are pretrained on the ImageNet dataset.

If you have any more questions or need further clarification, feel free to ask. Happy coding! 😊