ImportError: cannot import name 'YOLO' from 'ultralytics' (unknown location)

[FONG1234]

Search before asking

• [X] I have searched the YOLOv8 issues and discussions and found no similar questions.

Question

Anybody know how to solve this problem?

Currently, I want to use the local environment (PyCharm) for conducting my project. I already follow all the step for install the repo of YOLOv8. However, this error still occur. I do try to execute the code of 'from ultralytics import YOLO' in Google Colab, it work well, but with local environment this problem occurred. Thanks for the people who willing to help me.

Additional

No response

[FONG1234]

Solve it, although we already git clone or pip the ultralytics in the PyCharm. We still need to go to 'Python Interpreter' to install the ultralytics package. Then, it should be can run the 'from ultralytics import YOLO'.

[glenn-jocher]

@FONG1234 thank you for bringing this issue to our attention. It's great to hear that you were able to solve the issue by installing the ultralytics package in the Python Interpreter. It's important to note that although you may have installed the package globally, your local environment (in this case, PyCharm) may require further setup.

In general, we recommend double-checking all of the installation steps in our installation guide and ensuring that all dependencies are met. Additionally, PyCharm may have its own installation requirements, so it's worth checking their documentation as well.

If you have any further issues or questions, please don't hesitate to ask. We're happy to assist you in any way we can. Thank you for using YOLOv8!

[Dhamu785]

Even I have the error while importing YOLO from ultralytics in Google-colab

ImportError: cannot import name 'YOLO' from 'ultralytics' (unknown location)

This error occurred after succesfull installation of ultralytics

[glenn-jocher]

@Dhamu785 i'm sorry to hear you're encountering import issues with the YOLOv8 model from Ultralytics in your Google Colab environment. Firstly, ensure that the Ultralytics repository is correctly cloned and installed in the Colab notebook you're working on. Import errors often stem from incorrect installation or potential conflicts with Python package versions.

Since you confirmed successful installation of the Ultralytics package, your problem could also be due to issues navigating the file system in Colab. When you clone the Ultralytics repo in Colab, you need to adjust your directory paths to correctly import the YOLO model. These adjustments depend on where the Ultralytics folder resides in your drive.

Lastly, you might want to confirm you're using the correct version of the Ultralytics YOLOv8 package - sometimes, import errors can occur due to incompatibility between different versions of packages in Python.

I hope these suggestions help. If you're still encountering problems after considering these points, please provide further details on how you're installing and importing Ultralytics YOLOv8. Your detailed feedback would be immensely helpful in narrowing down the problem.

[Dhamu785]

Thank you for the response. The issue was resolved after updating the ultralytics. Use this for update the package - pip install -U ultralytics

[glenn-jocher]

@Dhamu785 i'm glad to hear that updating the Ultralytics package resolved your issue! Keeping packages up-to-date is indeed vital as it ensures you have the latest features and enhancements -- and most importantly, bug fixes that can resolve issues like the one you encountered.

Remember, the -U flag in the pip install -U ultralytics command is used to upgrade all packages to their latest available version. This can often help resolve compatibility and dependency issues within your environment.

Thank you for sharing the solution that worked for you. It will undoubtedly help others who might encounter the same issue. If you have any more questions or face any other issues, feel free to ask. We're here to help.

Happy coding with YOLOv8!

[Dhamu785]

@glenn-jocher Thanks for the dedicated response

[GRUTHWIKREDDY]

ImportError: cannot import name 'YOLOv8' from 'yolov8'

[glenn-jocher]

@GRUTHWIKREDDY i'm glad you reached out for help! It looks like there might be a slight mix-up in the import statement. If you're using the Ultralytics YOLOv8 package, the correct way to import the YOLO model would be:

from ultralytics import YOLO

Make sure you've installed the latest ultralytics package with:

pip install -U ultralytics

If you're still facing issues, please ensure the package is correctly installed in your environment. Let us know how it goes! 😊

[Manjutiwari]

I am working on Microsoft planetary computers. Yolov8 was working. from ultralytics import YOLO all of a sudden I started getting error here

ModuleNotFoundError: No module named 'ultralytics'

I tried using !pip install torch torchvision torchaudio --upgrade !pip install -U ultralytics !pip show ultralytics

gives response Name: ultralytics Version: 8.1.30 Summary: Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification. Home-page: Author: Glenn Jocher, Ayush Chaurasia, Jing Qiu Author-email: License: AGPL-3.0 Location: /srv/conda/lib/python3.10/site-packages Requires: matplotlib, opencv-python, pandas, pillow, psutil, py-cpuinfo, pyyaml, requests, scipy, seaborn, thop, torch, torchvision, tqdm Required-by:

Please help in to debug

[glenn-jocher]

@Manjutiwari hi there! It looks like your environment might have encountered a hiccup. Since you've already tried reinstalling the ultralytics package, could we attempt a few steps to pinpoint the issue? ✨

Firstly, ensure that your Python environment is activated, and you're in the correct workspace. Sometimes, Python packages might get installed in a different environment, causing such import errors.

Try restarting your Jupyter kernel or your application to ensure all paths are refreshed. Here’s a small code snippet that can sometimes help reset the environment within a Jupyter Notebook:

import os
os._exit(00)

If the issue persists, considering the ModuleNotFoundError points to a potential path issue, you might want to verify if your Python environment is pointing to the correct path and includes /srv/conda/lib/python3.10/site-packages.

Also, ensure the import statement is correctly executed in your notebook cells without accidental misspellings. In case the problem continues, feel free to share any additional error messages or behaviors you observe. We're here to help! 😊

[Manjutiwari]

I am trying to detect grass in rice using yolov8m.pt. My training dataset contains 17000 bounding boxes in 42 images (image size 8000*6000 mp) in three classes: rice, grass, and background. I am trying to tune it but am not able to get an accurate result. Can you help or suggest how to improve? so far, I am using the training parameters, like model.train(data = "data_custom.yaml", batch=2, imgsz=1600, rect=True, epochs=100,lr0=0.001, optimizer='Adam', workers=1)

[glenn-jocher]

@Manjutiwari hi there! 😊 It sounds like you're undertaking an interesting project with YOLOv8 for distinguishing between rice and grass in high-resolution images. Given the large size of your images and the high density of your bounding boxes, your task is indeed challenging but quite feasible with some adjustments.

With large, high-resolution images and a significant number of bounding boxes, training can be tricky due to the vast amount of detail and information to process. Here are a couple of suggestions that might help improve your results:

1. Image Size and Batch Size: Your current image size (imgsz=1600) and batch size (batch=2) seem reasonable for starting. Consider experimenting with these parameters slightly to find a better balance for your specific dataset. However, be mindful of your hardware limitations.

2. Data Augmentation: Enhance your dataset’s variability with more augmented data. This can help the model generalize better across similar but slightly different scenes. YOLOv8 automatically applies some level of augmentation, but adjusting the extent could be beneficial.

3. Learning Rate Scheduler: The initial learning rate and optimizer are good choices. Still, incorporating a learning rate scheduler (if not already done by default in YOLOv8) could help by adapting the learning rate throughout the training process for better convergence.

4. Pretrained Weights: Starting with pretrained weights (yolov8m.pt) is great. Ensure your dataset diversity adequately represents the target scenarios, so fine-tuning is effective.

5. Regularization Techniques: To prevent overfitting, especially given the relatively small number of very high-resolution images, explore adding regularization techniques. They can be quite beneficial in improving model generalization.

6. Focused Data Annotation: Lastly, consider revisiting your dataset annotations. Given the large size of each image and the detail within, ensuring high-quality, accurate annotations is crucial for performance.

Attempting slight modifications within these areas and observing the impact on your model's performance can guide you toward the optimal setup. Keep experimenting and fine-tuning your parameters! 💪

# Example tweak: Increasing image size and using a learning rate scheduler
model.train(data="data_custom.yaml", batch=2, imgsz=2048, rect=True, epochs=100, lr0=0.001, optimizer='Adam', workers=1, scheduler='cosine')

Let us know how it goes, and feel free to share any updates or further questions you might have!

[devangm206]

I am using yolov8 and ultralytics for object detection but recently it is giving me error
ModuleNotFoundError: No module named 'ultralytics.yolo' I was using ultralytics version 8.0.196 now i updated it to 8.1.34. Still the same error

[glenn-jocher]

@devangm206 hey there! 😊 It seems like there might be a slight mix-up in the import statement due to the update. To use YOLOv8 models, you should import YOLO from ultralytics, not ultralytics.yolo. Here's how you can do it correctly:

from ultralytics import YOLO

Make sure your installation is up to date with:

pip install -U ultralytics

If the error persists, try giving your environment a quick refresh by restarting it. Let us know how it goes! 🚀

[devangm206]

Actually I am running this command - loaded_dict = torch.load(model_path) to load my saved model weights and perform object detection. But after running this command it gives me this error - loaded_dict = torch.load(model_path) ^^^^^^^^^^^^^^^^^^^^^^ File "site-packages/torch/serialization.py", line 1026, in load return _load(opened_zipfile, ^^^^^^^^^^^^^^^^^^^^^ File "site-packages/torch/serialization.py", line 1438, in _load result = unpickler.load() ^^^^^^^^^^^^^^^^ File "site-packages/torch/serialization.py", line 1431, in find_class return super().find_class(mod_name, name) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ModuleNotFoundError: No module named 'ultralytics.yolo'

[glenn-jocher]

@devangm206 hi there! It looks like you're encountering an issue with loading your model. Instead of directly using torch.load for YOLOv8 models, you'll want to utilize the YOLO class from the ultralytics package for loading your model. Here's a quick example on how you can do this:

from ultralytics import YOLO

model_path = 'path_to_your_model/model.pt'
model = YOLO(model_path)

This should help you load your model and bypass the ModuleNotFoundError. Let us know how it goes! 😊

[devangm206]

@glenn-jocher Thank you very much. Your solution solved my error. Its working !!

I had one more error. Do you have any idea about raspberrypi's 'Picamera2' python library ?

[Manjutiwari]

Thanks for the response, May I ask you to help me:

1. Please let me know how to configure or use the scheduler mentioned above.
2. I have my own annotated dataset. Can you let me know how I can use a slicer to cut the labelled images (yaml output from label studio)in 4 parts to get images of smaller sizes?

On Sat, Mar 23, 2024 at 5:28 AM Glenn Jocher @.***> wrote:

@Manjutiwari https://github.com/Manjutiwari hi there! 😊 It sounds like you're undertaking an interesting project with YOLOv8 for distinguishing between rice and grass in high-resolution images. Given the large size of your images and the high density of your bounding boxes, your task is indeed challenging but quite feasible with some adjustments.

With large, high-resolution images and a significant number of bounding boxes, training can be tricky due to the vast amount of detail and information to process. Here are a couple of suggestions that might help improve your results:

1.

Image Size and Batch Size: Your current image size (imgsz=1600) and batch size (batch=2) seem reasonable for starting. Consider experimenting with these parameters slightly to find a better balance for your specific dataset. However, be mindful of your hardware limitations. 2.

Data Augmentation: Enhance your dataset’s variability with more augmented data. This can help the model generalize better across similar but slightly different scenes. YOLOv8 automatically applies some level of augmentation, but adjusting the extent could be beneficial. 3.

Learning Rate Scheduler: The initial learning rate and optimizer are good choices. Still, incorporating a learning rate scheduler (if not already done by default in YOLOv8) could help by adapting the learning rate throughout the training process for better convergence. 4.

Pretrained Weights: Starting with pretrained weights (yolov8m.pt) is great. Ensure your dataset diversity adequately represents the target scenarios, so fine-tuning is effective. 5.

Regularization Techniques: To prevent overfitting, especially given the relatively small number of very high-resolution images, explore adding regularization techniques. They can be quite beneficial in improving model generalization. 6.

Focused Data Annotation: Lastly, consider revisiting your dataset annotations. Given the large size of each image and the detail within, ensuring high-quality, accurate annotations is crucial for performance.

Attempting slight modifications within these areas and observing the impact on your model's performance can guide you toward the optimal setup. Keep experimenting and fine-tuning your parameters! 💪

Example tweak: Increasing image size and using a learning rate schedulermodel.train(data="data_custom.yaml", batch=2, imgsz=2048, rect=True, epochs=100, lr0=0.001, optimizer='Adam', workers=1, scheduler='cosine')

Let us know how it goes, and feel free to share any updates or further questions you might have!

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/ultralytics/issues/2709#issuecomment-2016187491, or unsubscribe https://github.com/notifications/unsubscribe-auth/A5ICHNOYXSICKRETFBQHMMTYZTATDAVCNFSM6AAAAAAYHX2YKOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDAMJWGE4DONBZGE . You are receiving this because you were mentioned.Message ID: @.***>

[glenn-jocher]

I'm glad to hear the solution worked for you! 😄 Regarding your inquiry about Raspberry Pi's Picamera2 library, while it's not something I specialize in, I suggest checking out the official Raspberry Pi documentation and the GitHub repository for Picamera2. They are great starting points for understanding how to integrate and utilize Picamera2 in your Raspberry Pi projects.

As for configuring a scheduler in YOLOv8 and slicing your large images into smaller ones:

1. Scheduler: When using the model.train() method, you can specify a learning rate scheduler directly in the arguments. For example, to utilize a cosine scheduler, add scheduler='cosine' to your model.train() call, like so:

model.train(data="data_custom.yaml", batch=2, imgsz=2048, epochs=100, lr0=0.001, scheduler='cosine')

2. Slicing Images: There isn't a built-in slicer in the Ultralytics repo, but you can easily preprocess your images with OpenCV or PIL by dividing each into 4 parts:

from PIL import Image

def slice_image(image_path, output_size=(2000,1500)):
    im = Image.open(image_path)
    width, height = im.size

    # Slice dimensions
    left = 0
    top = 0
    right = output_size[0]
    bottom = output_size[1]

    # Crop the image and save the pieces
    for i in range(0, height, output_size[1]):
        for j in range(0, width, output_size[0]):
            # Define the bounding box
            bbox = (left+j, top+i, right+j, bottom+i)
            slice = im.crop(bbox)
            slice.save(f'slice_{i}_{j}.png')

Just adjust output_size according to how you want to slice the images. For more complex logic or different slicing patterns, you might need to modify the function accordingly.

Hope this helps, and feel free to ask if you have any more questions!

[Manjutiwari]

Thanks , Please let me know How to split labels from 'L' to 'L1' and 'L2'.(Please see the file attached) Is there any existing Py libs to do this?

On Mon, Apr 15, 2024 at 9:59 PM Glenn Jocher @.***> wrote:

I'm glad to hear the solution worked for you! 😄 Regarding your inquiry about Raspberry Pi's Picamera2 library, while it's not something I specialize in, I suggest checking out the official Raspberry Pi documentation https://www.raspberrypi.com/documentation/ and the GitHub repository for Picamera2. They are great starting points for understanding how to integrate and utilize Picamera2 in your Raspberry Pi projects.

As for configuring a scheduler in YOLOv8 and slicing your large images into smaller ones:

1. Scheduler: When using the model.train() method, you can specify a learning rate scheduler directly in the arguments. For example, to utilize a cosine scheduler, add scheduler='cosine' to your model.train() call, like so:

model.train(data="data_custom.yaml", batch=2, imgsz=2048, epochs=100, lr0=0.001, scheduler='cosine')

2. Slicing Images: There isn't a built-in slicer in the Ultralytics repo, but you can easily preprocess your images with OpenCV or PIL by dividing each into 4 parts:

from PIL import Image def slice_image(image_path, output_size=(2000,1500)): im = Image.open(image_path) width, height = im.size

# Slice dimensions
left = 0
top = 0
right = output_size[0]
bottom = output_size[1]

# Crop the image and save the pieces
for i in range(0, height, output_size[1]):
    for j in range(0, width, output_size[0]):
        # Define the bounding box
        bbox = (left+j, top+i, right+j, bottom+i)
        slice = im.crop(bbox)
        slice.save(f'slice_{i}_{j}.png')

Just adjust output_size according to how you want to slice the images. For more complex logic or different slicing patterns, you might need to modify the function accordingly.

Hope this helps, and feel free to ask if you have any more questions!

— Reply to this email directly, view it on GitHub https://github.com/ultralytics/ultralytics/issues/2709#issuecomment-2057277372, or unsubscribe https://github.com/notifications/unsubscribe-auth/A5ICHNL5X2PM7MX3SYF6JP3Y5P6ANAVCNFSM6AAAAAAYHX2YKOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANJXGI3TOMZXGI . You are receiving this because you were mentioned.Message ID: @.***>

[glenn-jocher]

Hey there! 👋 To split labels from 'L' to 'L1' and 'L2', you can use simple Python scripting. While there's no specific library just for this task, Python's built-in file handling and string manipulation capabilities are more than enough for this job.

Here's a quick example to get you started. This snippet reads an existing label file, splits each 'L' label into 'L1' and 'L2', and then writes these modified labels back to new files:

with open('your_label_file.txt', 'r') as file:
    lines = file.readlines()

with open('label_L1.txt', 'w') as file1, open('label_L2.txt', 'w') as file2:
    for line in lines:
        if 'L' in line:
            file1.write(line.replace('L', 'L1'))
            file2.write(line.replace('L', 'L2'))

Remember, the above is a basic template. Depending on how your labels are formatted and what the exact requirement is, you might need to adjust the logic a bit. Feel free to tweak it as needed! 😊

Hope this helps! Let me know if there's anything else you're curious about.