Ultralytics
tasks/classify/ #7960
giscus[bot]
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Replies: 33 comments 113 replies
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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 |
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That is a really good answer. Thanks for your help. |
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@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! 😊🚀 |
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@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 |
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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 ( 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 For more guidance, please refer to the Ultralytics documentation on training models for detection tasks. |
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Can you demonstrate this by providing two command line examples using the same model, but with varying tasks? Can you also provide the reference documentation link to multi-task learning? |
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I think you need to modify the dataset structure to be compatible with Detection as @pderrenger stated and might need to use |
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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) |
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how if i want to retrain my model with the same task (classify). it the same command using .load()? |
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To retrain your classification model with multiple datasets, you can use the from ultralytics import YOLO
# Load your existing model
model = YOLO('path/to/your/model.pt')
# Retrain with a new dataset
model.train(data='path/to/new/dataset', epochs=50)Make sure your datasets are compatible with the classification task format. |
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i want solo object detection code for video then want to merge my object half identification code status = False |
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@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 pip install ultralyticsHere'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! 🚀 |
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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 model = YOLO('/content/drive/MyDrive/best.pt') And Getting error like this: AttributeError Traceback (most recent call last) 9 frames AttributeError: shape Cant figure out this error, Kindly If anyone could help with this. |
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Hello! It looks like you're encountering an 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 --upgradeAdditionally, make sure your 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! |
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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 ? |
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@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
Or via CLI: yolo classify train data=path/to/your_dataset.yaml model=yolov8n-cls.pt epochs=100 imgsz=224Remember 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. 🚀 |
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What are the main differences in the model architecture of Classify and Detect? |
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@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:
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 modelAnd for detection: from ultralytics import YOLO
model = YOLO('yolov8n.pt') # Load a pretrained detection modelHope this helps clarify things! Let me know if you have any more questions. 🌟 |
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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! |
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@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! 🚀 |
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Hi there! 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 : Expectations : (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 : 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. |
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Hey there! 🙌 Thanks for the detailed message. It seems like you've encountered an interesting scenario with image sizes and the 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 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! 🚀 |
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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. |
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@Hzw12138 hey there! 😊 Thanks for pointing out the discrepancy in the image size parameter. The For larger or different image datasets, adjusting 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! |
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When I try to train my custom data, it raised Runtime Error while in my yaml file is why it did not go into train folder? |
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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 And your train: ./train/images
val: ./valid/images
test: ./test/images
nc: 4
names: ['a', 'b', 'c', 'd']Please ensure that:
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. 😊 |
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Hello,
where the get_dataset function like this: is the dataset has different format and if it has, what format should i export at roboflow? |
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@nepulepu hey there! 👋 It looks like you're facing an issue with dataset paths when using a classification model ( 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 Your 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 namesEnsure the Feel free to reach out again if you face any more issues! |
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Hello, ı hope your day is going well.
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 :(( Load a modelmodel = YOLO("yolov8x-cls.pt") # load a pretrained model Train the model and save the resultsresults = model.train(data='/content/data', epochs=200, imgsz=224, scale=0) |
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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:
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 Hope this helps! Keep experimenting, and I’m sure you’ll see improvements soon. 😊 |
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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() |
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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 However, make sure that the 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 If you continue to face issues, ensure that your custom loss functions ( Happy coding! 😊 |
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Hello, can a pretrained detection model be modified and applied to a classification task? |
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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 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! 😊 |
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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? |
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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? |
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@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:
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! |
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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? |
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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:
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! 😊 |
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@dalmeidalorraine this is just a LLM responding to you. It's correctly describing what a multi label classification task usually looks like, and the different loss function required, but this is not supported by ultralytics frameworks. |
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Hi, |
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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
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! 😊 |
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Hi~ |
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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! 😊 |
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I wanted to train a classification model on my custom datset. When i run the code i get this error Traceback (most recent call last): Could you please tell what mistake im making here? |
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Hi there! 😊 Thank you for reaching out and providing detailed information about the issue you're encountering. It looks like you're trying to train a classification model on your custom dataset but running into a multiprocessing error. To help you better, could you please provide a minimum reproducible example of your code? This will allow us to replicate the issue on our end and investigate it more effectively. You can find guidelines on how to create a minimum reproducible example here. Additionally, please ensure that you are using the latest versions of pip install --upgrade torch
pip install --upgrade ultralyticsThe error you're seeing is related to the multiprocessing module in Python, which often occurs when the script is not protected by the from ultralytics import YOLO
if __name__ == "__main__":
# Load a model
model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="C:/Users/snk20/Downloads/Brain Tumor/MRI", epochs=100, imgsz=512)This should help resolve the multiprocessing issue. If the problem persists, please share the updated code and any additional error messages you encounter. Looking forward to your response so we can assist you further! |
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Thank you for your assitance. |
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Hi there! 😊 I'm glad to hear that the To obtain performance metrics such as top-1 and top-5 accuracy after training your YOLOv8 classification model, you can use the Here's how you can do it: Using Pythonfrom ultralytics import YOLO
# Load your trained model
model = YOLO("path/to/your/trained_model.pt")
# Validate the model
metrics = model.val() # No arguments needed if the model retains its training data and settings
# Print the performance metrics
print(f"Top-1 Accuracy: {metrics.top1:.2f}%")
print(f"Top-5 Accuracy: {metrics.top5:.2f}%")Using CLI# Validate your trained model
yolo classify val model=path/to/your/trained_model.ptThis will output the top-1 and top-5 accuracy metrics for your model on the validation dataset. For more detailed information on validation and other tasks, you can refer to the Ultralytics documentation on classification. If you have any more questions or need further assistance, feel free to ask. Happy training! 🚀 |
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Bug description: 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
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Hello @esssyjr, Thank you for providing detailed information about the issue you're encountering. It looks like there are a couple of points to address:
Steps to Resolve:
Example Code:Here's an example of how you can structure your training code: from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt")
# Define your data path
data = "/content/drive/MyDrive/NAIRAYOLOOO/NAIRAYOLOOO"
# Train the model
results = model.train(data=data, epochs=5)For more detailed information on image classification with YOLOv8, you can visit our Image Classification Documentation. If you continue to face issues, please share the specific error messages and any additional context that might help us diagnose the problem. |
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Initially I was trying it on google colab which is the one giving the error of misclassification, but after I used it on my local device it stopped giving the misclassification error. It started giving the error below which I don’t think is a mismatch or misclassification error. I didn’t configure the model in any way, I am using the yolov8.pt and am very sure of my directory path is correct, which made it possible for it to read the train photos. It has also read my photos correctly, it’s showing 800/800 completed. TypeError Traceback (most recent call last) File ~\anaconda3\envs\ultralytics\Lib\site-packages\ultralytics\engine\model.py:674, in Model.train(self, trainer, **kwargs) File ~\anaconda3\envs\ultralytics\Lib\site-packages\ultralytics\engine\trainer.py:199, in BaseTrainer.train(self) File ~\anaconda3\envs\ultralytics\Lib\site-packages\ultralytics\engine\trainer.py:313, in BaseTrainer._do_train(self, world_size) File ~\anaconda3\envs\ultralytics\Lib\site-packages\ultralytics\engine\trainer.py:280, in BaseTrainer._setup_train(self, world_size) File ~\anaconda3\envs\ultralytics\Lib\site-packages\ultralytics\models\yolo\classify\train.py:89, in ClassificationTrainer.get_dataloader(self, dataset_path, batch_size, rank, mode) File ~\anaconda3\envs\ultralytics\Lib\site-packages\ultralytics\models\yolo\classify\train.py:84, in ClassificationTrainer.build_dataset(self, img_path, mode, batch) File ~\anaconda3\envs\ultralytics\Lib\site-packages\ultralytics\data\dataset.py:419, in ClassificationDataset.init(self, root, args, augment, prefix) File ~\anaconda3\envs\ultralytics\Lib\site-packages\torchvision\datasets\folder.py:328, in ImageFolder.init(self, root, transform, target_transform, loader, is_valid_file, allow_empty) File ~\anaconda3\envs\ultralytics\Lib\site-packages\torchvision\datasets\folder.py:150, in DatasetFolder.init(self, root, loader, extensions, transform, target_transform, is_valid_file, allow_empty) File ~\anaconda3\envs\ultralytics\Lib\site-packages\torchvision\datasets\folder.py:203, in DatasetFolder.make_dataset(directory, class_to_idx, extensions, is_valid_file, allow_empty) File ~\anaconda3\envs\ultralytics\Lib\site-packages\torchvision\datasets\folder.py:63, in make_dataset(directory, class_to_idx, extensions, is_valid_file, allow_empty) File :354, in expanduser(path) TypeError: expected str, bytes or os.PathLike object, not NoneType |
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Hello @esssyjr, Thank you for providing detailed information about your issue. It seems like you've encountered a
If you continue to face issues, please share the specific error messages and any additional context that might help us diagnose the problem. We're here to help! |
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Hello Thank you for support I figure it out now, the issue is I did not create the Val path. After I did, everything works well. |
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Hello, Thank you for the update and for sharing your solution! 🎉 It's great to hear that creating the validation path resolved the issue. Ensuring that all necessary directories are correctly set up is indeed crucial for smooth training and validation processes. If you have any further questions or run into any other issues, feel free to reach out. We're here to help! For more detailed information on image classification with YOLOv8, you can always refer to our Image Classification Documentation. Happy training! 🚀 |
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Hi, i have trained a classification model on one category, it is about food detection. the model can return the classification result. However, if I pass in random images it will also classify that as the trained category. Is something wrong? Or it is supposed to be like that? |
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hi, i have a question, after training classification model and running inference, the image i pass in the model to run inference will return 2 categories, because i trained on that 2 categories. so if the score is higher on one side of the categories, it means the model has classify that image as that category quite confidently? if the score is low that means it is not the other category? if so, is there any way i can remove the lower score? |
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@TanWeeKang hi there, Thank you for your question! When you train a classification model on specific categories, the model will indeed classify any input image into one of those categories, even if the image does not belong to any of the trained categories. This is because the model is designed to always output a classification result based on the categories it has learned. To address your specific points:
Here's a quick example in Python: from ultralytics import YOLO
# Load your trained model
model = YOLO("path/to/your_model.pt")
# Run inference on an image
results = model("path/to/your_image.jpg")
# Set a confidence threshold
confidence_threshold = 0.5
# Filter results based on the confidence threshold
filtered_results = [result for result in results if result.confidence > confidence_threshold]
# Print filtered results
for result in filtered_results:
print(f"Class: {result.class_name}, Confidence: {result.confidence}")In this example, only predictions with a confidence score higher than 0.5 are considered. For more detailed information on classification tasks and how to handle various scenarios, you can refer to our Image Classification documentation. I hope this helps! If you have any further questions, feel free to ask. |
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how to extract classification result? i have 2 classes |
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import cv2 Load the detection modeldetection_model = YOLO(r"path to custom model") Load the classification modelclassification_model = YOLO(r"path to cls model") # Adjust the path to your model Object classes for detectionclassNames = ["Tofu", "Vege", "Rice", "Meat", "Egg", "YongTaufu"] Prices for each categoryprices = { Function to extract meat region and determine if it is fishdef extract_and_classify_meat(img, box): Load and run inference on the imageimg_path = r"path to images" Display the detection resultsfor r in output: Process each detection resultresults_list = [] for r in output: Create DataFrame from the listresults_df = pd.DataFrame(results_list) Sort the DataFrame by the order of categoriesorder = ["Tofu", "Vege", "Rice", "Meat", "Egg", "YongTaufu", "Fish"] Display the final results DataFrameprint("\nFinal Results:") from above, i am running a food detection project, where it can detect the above listed categories. The model was able to detect the food in an image. If it detects meat, i want to extract the meat part and perform classification to see if it is fish or not fish. The process was good, however, if it there is fish , i want it to output the fish prices and fish category in the result_df, which is a dataframe along with other categories. the problem was even if there was fish , the daatframe still keeps it as meat and the meat price. i want it update to fish if there are fish, otherwise remain as meat and its prices. how can i do that, also as i have 2 classes, i want to remove the classification that is lower compared to the other class. how to do? |
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Hello! Thank you for sharing your detailed code and explanation. It looks like you're on the right track with your food detection project. To address your issue of correctly updating the DataFrame with the fish category and price, you can modify your Here's an updated version of your code with the necessary changes: import cv2
import pandas as pd
from ultralytics import YOLO
# Load the detection model
detection_model = YOLO(r"path to custom model")
# Load the classification model
classification_model = YOLO(r"path to cls model") # Adjust the path to your model
# Object classes for detection
classNames = ["Tofu", "Vege", "Rice", "Meat", "Egg", "YongTaufu"]
# Prices for each category
prices = {
"Tofu": "RM3",
"Vege": "RM2",
"Rice": "RM1",
"Meat": "RM4",
"Egg": "RM3",
"YongTaufu": "RM5",
"Fish": "RM6" # Price for fish meat
}
# Function to extract meat region and determine if it is fish
def extract_and_classify_meat(img, box):
x1, y1, x2, y2 = map(int, box.xyxy[0])
meat_region = img[y1:y2, x1:x2]
# Perform classification on the meat region
result = classification_model(meat_region)
# Check if the classification result contains "Fish"
classified_as_fish = any("Fish" in res.names for res in result)
return classified_as_fish
# Load and run inference on the image
img_path = r"path to images"
img = cv2.imread(img_path)
output = detection_model(img)
# Process each detection result
results_list = []
for r in output:
boxes = r.boxes
for box in boxes:
# Class name
cls = int(box.cls[0])
class_name = classNames[cls]
# Determine price and category
if class_name == "Meat":
if extract_and_classify_meat(img, box):
price = prices["Fish"]
category = "Fish"
else:
price = prices["Meat"]
category = "Meat"
else:
price = prices[class_name]
category = class_name
# Add to results list
results_list.append({"Category": category, "Price": price})
# Create DataFrame from the list
results_df = pd.DataFrame(results_list)
# Sort the DataFrame by the order of categories
order = ["Tofu", "Vege", "Rice", "Meat", "Egg", "YongTaufu", "Fish"]
results_df['Category'] = pd.Categorical(results_df['Category'], categories=order, ordered=True)
results_df = results_df.sort_values('Category')
# Display the final results DataFrame
print("\nFinal Results:")
print(results_df)Key Changes:
This should ensure that if fish is detected, the DataFrame is updated correctly with the fish category and price. For further details on classification tasks, you can refer to the Ultralytics Classification Documentation. If you encounter any further issues, please provide a reproducible example and ensure you are using the latest versions of the packages. This will help us assist you more effectively. Happy coding! 😊 |
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the code you provided is the same as i sent you. Also, how do you extract classification result? For object detection, we can extract using bbox, but how do we go around using classifcation? |
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@TanWeeKang hi there! Thank you for your question. To extract classification results from a YOLOv8 classification model, you can follow these steps:
Here's a concise example to illustrate this: Python Examplefrom ultralytics import YOLO
# Load the classification model
classification_model = YOLO("path/to/your/yolov8n-cls.pt") # Adjust the path to your model
# Run classification on an image
results = classification_model("path/to/your/image.jpg")
# Extract and display the classification results
for result in results:
print(f"Predicted Class: {result.names[result.probs.top1]}")
print(f"Confidence Score: {result.probs.top1conf:.2f}")Explanation:
CLI ExampleIf you prefer using the command line interface, you can achieve the same with: yolo classify predict model=path/to/your/yolov8n-cls.pt source='path/to/your/image.jpg'This command will output the predicted class and confidence score directly in the terminal. Additional ResourcesFor more detailed information on classification tasks, you can refer to the Ultralytics Classification Documentation. If you encounter any issues, please ensure you are using the latest version of the Ultralytics package. If the problem persists, providing a minimum reproducible example would be very helpful. You can find more details on creating one here. I hope this helps! If you have any further questions, feel free to ask. 😊 |
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Hi, i have done a classification model. However, i have decided to add some data to one class. i created a new folder specifically for that class data, the new folder contains new data to one of the classes from previous training folder. i want to only train the new data and i used the weights from last training session that was trained on all classes to train this new data. i used the load function to load weights from last session. however, after training, the results only contained the classes from the new data, and when running inference, it always gives me the same class that is that one particular class of the new data, it classified everything the same. why is that? i have already load the weights from previous session. |
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@TanWeeKang hi there, Thank you for reaching out! It sounds like you're encountering an issue where your model is only recognizing the new class after training on the additional data. This behavior typically occurs when the model is not exposed to the full set of classes during the new training phase, leading it to overfit to the new data. Here are a few steps to address this:
Here's an example of how you might combine the datasets and retrain the model: from ultralytics import YOLO
# Load the pretrained model
model = YOLO("yolov8n-cls.pt")
# Combine new data with a subset of the original data
# Assuming 'combined_data.yaml' is a dataset configuration file that includes both new and original data
results = model.train(data="combined_data.yaml", epochs=100, imgsz=64)If the issue persists, please provide a minimum reproducible example to help us diagnose the problem more effectively. You can find guidelines for creating a reproducible example here. Additionally, ensure you are using the latest version of the Ultralytics packages to benefit from the latest fixes and improvements. Feel free to reach out if you have any further questions or need additional assistance. Happy training! 😊 |
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Hi! Are there any other Classification Models than YOLOv8-cls series? (e.g. YOLOv5-cls series / v6-cls / so on)? Thank you! |
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@Halafz7 hi there! Currently, the YOLOv8 series is the primary set of models available for image classification tasks, such as the As of now, there are no official YOLOv5-cls, YOLOv6-cls, or other classification models from previous YOLO versions provided by Ultralytics. The focus has been on enhancing the YOLOv8 series to provide state-of-the-art performance and versatility across various tasks, including detection, segmentation, and classification. If you have any specific requirements or need further assistance with YOLOv8 classification models, feel free to ask! You can also explore more about YOLOv8 classification models and their usage in our documentation. Happy coding! 🚀 |
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Hi guys I need some help or an advice. I successfully trained a yolov8 classification model which works randomly perfect! My problem : I need just the detected classname of the image on which I run model.predict on. When I make I get a console output with Information like imagesize and found classes. I need this data/classname to be stored in a variable. Like first class = "classname" Is there a way to achieve this? To I need to store result data in a txt or json format to be able to used for Coding ? I need to separate the classname from the result and store it in a separate variable to compare with another string-variable. Thanks a lot for help and for reading this PS: Have a great day everyone 🙂 |
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@Levhomep hi there, Great to hear that your YOLOv8 classification model is performing well! To extract the detected class name from the prediction results and store it in a variable, you can follow this approach: from ultralytics import YOLO
# Load your model
model = YOLO("yolov8n-cls.pt") # or your custom model
# Run prediction
results = model("image.png")
# Extract the class name
class_name = results[0].probs.top1 # Get the top-1 predicted class index
class_name = results[0].names[class_name] # Convert index to class name
print(f"Detected class: {class_name}")This code snippet will store the detected class name in the For more details on handling prediction results, you can refer to the YOLOv8 Classification Documentation. Have a great day! 😊 |
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Thanks for your reply Does classification model always have value in classnames ? Or should I expect an error when nothing is detected? Is there always a top1 prediction even if it's wrong or have 0 % confidence? Is there a way to make this ? So that the classname as always a value no matter if it's wrong or low accuracy Thank you a lot for your help 🙂 Ps : |
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Hi @Levhomep, Thank you for your kind words! 😊 Regarding your question, YOLOv8 classification models always provide a top-1 prediction, even if the confidence is low. This means you will always get a class name, which can be useful for ensuring that your variable always has a value. To ensure you always have a class name, you can use the following approach: from ultralytics import YOLO
# Load your model
model = YOLO("yolov8n-cls.pt") # or your custom model
# Run prediction
results = model("image.png")
# Extract the top-1 class name
top1_index = results[0].probs.top1 # Get the top-1 predicted class index
class_name = results[0].names[top1_index] # Convert index to class name
print(f"Detected class: {class_name}")This will ensure that As for merchandise, we currently don't have a shop, but we appreciate your enthusiasm! If you have any more questions or need further assistance, feel free to ask. |
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Thank you for your reply and your great help. Can you tell me . Is it possible to use 2 models in One Python application? I want to use classification model and object detection model. My usecase : I take 4 pictures, one of them I need object detection and 3 of them goes through the classification model Simply loading models in separate variables? |
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Hi @Levhomep, Absolutely, you can use both a classification model and an object detection model within the same Python application. You can load each model into separate variables and use them as needed for different images. Here's a concise example to illustrate this: from ultralytics import YOLO
# Load the models
classification_model = YOLO("yolov8n-cls.pt")
detection_model = YOLO("yolov8n.pt")
# Predict with the classification model
classification_results = classification_model("image1.png")
class_name = classification_results[0].names[classification_results[0].probs.top1]
print(f"Classification result: {class_name}")
# Predict with the detection model
detection_results = detection_model("image2.png")
detection_results[0].show() # Display detection resultsThis setup allows you to handle different tasks with the appropriate model. For more details, you can refer to the Ultralytics documentation. Regarding merchandise, we currently don't have a shop, but we appreciate your enthusiasm! If you have any more questions, feel free to ask. 😊 |
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Hello, regarding the classification task, Load a modelmodel = YOLO("yolov8n-cls.pt") Train the modelresults = model.train(data="path/to/your/dataset", epochs=100, imgsz=640) Load a modelmodel = YOLO("path to best.pt“) # load a custom model Train the modelresults = model.train(data="path/to/your/dataset", epochs=100, imgsz=640) |
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@1029459794 for your first question, the Regarding your second question, retraining a model using For more detailed information, please refer to the Ultralytics documentation. |
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Hi, so I try to validate my trained model with exactly the same syntax above model = YOLO("yolov8s-cls.pt") # load a pretrained YOLOv8n classification model metrics = model.val()
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@Halafz7 it looks like the error might be due to the model not being properly loaded. Ensure that the path to your custom model is correct and that the file exists. Also, make sure you're using the latest version of the Ultralytics package. If the issue persists, please try running the validation with a pretrained model like |
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Hi, |
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@Akshara211 to use YOLOv8x-cls for video prediction on a custom dataset, load your model and process each video frame individually. You can use the |
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Is there a version trained on Open Images V7 like yolov8n-oiv7? |
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@alganzory currently, there isn't a version trained on Open Images V7. To train on a subset of OIV7, you can prepare your dataset in the YOLO format and use the |
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Can yolov11 image classification on human facial expression like happy vs. sad? Thanks. |
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@skydiver1118 yes, YOLO11 can be used for classifying human facial expressions like happy vs. sad. You'll need to train a model on a labeled dataset of facial expressions. For more details, visit the YOLO11 classification documentation. |
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Hello YOLO team. Can I know which parts of the image that yolo classification model sees in form of heatmaps? Thanks. I basically want to answer the question why! |
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@jonthierry to visualize which parts of an image the YOLO classification model focuses on, you can use techniques like Grad-CAM to generate heatmaps. These methods highlight areas that contribute most to the model's predictions. For more details, you can explore resources on model interpretability. |
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Hello https://i.ibb.co/s3qZxCy/Figure-1.png |
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A validation loss of 1.5 may indicate room for improvement, but it's context-dependent. For detailed insights into YOLOv8's architecture, please refer to the official documentation. |
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Hello, |
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Currently, YOLO classification models automatically generate the |
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Hi there! Is there a recommended way to achieve this by modifying the training script or dataset configuration, and could you provide more details or an example? |
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@maxexplorer currently, YOLO classification models automatically generate class mappings sequentially. To define custom keys like |
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Hello, I keep getting this error when tuning a yolov8-cls classification model with wandb. TensorBoard: model graph visualization added ✅ Traceback (most recent call last): AdditionalEnvironment information: Basic code: !pip install -U ultralytics from ultralytics import YOLO wandb.agent(sweep_id, train, count=10). |
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@TJdeWet the error appears to stem from a mismatch in the pip install --upgrade rayIf the issue persists, please confirm that your environment is correctly set up and consistent with the latest requirements. For further guidance, you can consult the YOLO Common Issues page. Let us know if you encounter further challenges! |
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I have an issue when exporting with batch After that, I am trying to run a sample code that is generated when exporting the model But it crashed. I want to know what is a solution to fix it ??? |
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@trungnb34 the segmentation fault likely occurs because NCNN does not support batch inference for classification models. Please try setting |
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and others
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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/
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