Visualizing YOLOv5 Segmentation Data about yolov5 HOT 10 CLOSED

Hello,

Thank you for reaching out with your questions about YOLOv5 segmentation and training on Google Colab.

YOLOv5 Segmentation Issue

From your description, it seems like your model setup and inference code are generally correct. However, the issue might be related to how the masks are being processed or displayed. Here are a few things you might want to check:

1. Model Output: Ensure that the model is indeed outputting mask data. You can add a print statement to check the shape and content of the masks tensor right after inference.
2. Mask Processing: Verify that the mask resizing and thresholding are being done correctly. Sometimes, discrepancies in dimensions or incorrect threshold values can lead to invisible or incorrect masks.
3. Visualization: Check if the contours are being drawn on the frame correctly. You might want to try a simpler form of visualization, like overlaying the mask directly on the image with some transparency, to see if the masks are being generated at all.

Google Colab Disconnection

Regarding the issue with Google Colab disconnecting during long training sessions, here are a couple of suggestions:

• Code Activity: Try to include some form of output or logging in your training loop that updates more frequently. This might help in keeping the session alive.
• Colab Extensions: There are browser extensions available that can help keep your Colab session active by simulating interaction. These can be a temporary workaround if frequent manual interaction isn't helping.

If the problem persists, reviewing the exact output or errors, if any, could provide more insights into what might be going wrong.

Feel free to share any updates or additional information. We're here to help!

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@glenn-jocher, Thanks for the quick response!

I have been unsuccessful in getting the YOLOv5-seg model to work as expected. I notice that i get this error when loading the model:

WARNING  YOLOv5 SegmentationModel is not yet AutoShape compatible. You will not be able to run inference with this model.

I think this has something to do with it. Is there a way that you know of to load the model for inference in Python? If so, it would help if you could provide some example code to help me get started.

Also, about the Colab issue, I got a browser extension that seemingly fixed the issue!

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@DylDevs hello,

Thank you for the update and I'm glad to hear that the browser extension resolved your Colab issue!

Regarding the YOLOv5-seg model warning, it indicates that the model isn't compatible with the AutoShape feature, which simplifies the inference process. You can still run inference manually by handling the input and output tensors directly. Here’s a basic example to help you get started with manual inference:

import torch
from PIL import Image
from torchvision.transforms import functional as F

# Load your model (ensure it's in the correct directory)
model = torch.load('path_to_yolov5s-seg.pt')
model.eval()  # Set the model to evaluation mode

# Load an image
image = Image.open('path_to_your_image.jpg')
image = F.to_tensor(image).unsqueeze(0)  # Transform image to tensor and add batch dimension

# Perform inference
with torch.no_grad():
    results = model(image)

# Process results here (e.g., extracting masks)
# Note: You'll need to adapt this part based on how your model outputs data

This code snippet manually handles the image transformation and model inference. Make sure to adapt the result processing part according to the specific output format of your segmentation model.

If you encounter any more issues or have further questions, feel free to ask. Happy coding!

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@glenn-jocher
Thanks for the rsponse again. I tried your suggestion, and I got this error from it:

Traceback (most recent call last):
  File "c:\Users\Dylan\Documents\Coding\Python\ETS2 Vehicle Detection\yolov5Seg\run - Copy.py", line 17, in <module>
    model = torch.load(MODEL_PATH)
            ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Dylan\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1026, in load
    return _load(opened_zipfile,
           ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Dylan\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1438, in _load
    result = unpickler.load()
             ^^^^^^^^^^^^^^^^
  File "C:\Users\Dylan\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\serialization.py", line 1431, in find_class
    return super().find_class(mod_name, name)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ModuleNotFoundError: No module named 'models.yolo'

Any ideas as to how I can fix it?

from yolov5.

Hello @DylDevs,

Thank you for your patience and for providing the error details. The error you're encountering, ModuleNotFoundError: No module named 'models.yolo', typically occurs when trying to load a model that was saved with custom classes or modules that aren't available in your current environment.

To resolve this, you should load the model using the torch.hub.load method, which ensures all dependencies are correctly handled. Here’s how you can do it:

1. Ensure you have the correct YOLOv5 repository and dependencies installed:

   pip install -r https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt

2. Load the model using torch.hub.load:

   import torch
   
   # Load the YOLOv5-seg model from PyTorch Hub
   model = torch.hub.load('ultralytics/yolov5', 'custom', path='path_to_yolov5s-seg.pt')
   model.eval()  # Set the model to evaluation mode
   
   # Example inference
   from PIL import Image
   from torchvision.transforms import functional as F
   
   # Load an image
   image = Image.open('path_to_your_image.jpg')
   image = F.to_tensor(image).unsqueeze(0)  # Transform image to tensor and add batch dimension
   
   # Perform inference
   with torch.no_grad():
       results = model(image)
   
   # Process results here (e.g., extracting masks)
   # Note: You'll need to adapt this part based on how your model outputs data

This approach should help you avoid the ModuleNotFoundError by leveraging the torch.hub.load method, which manages dependencies and model loading more robustly.

If you have any further questions or run into other issues, feel free to ask. We're here to help! 😊

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@glenn-jocher This code does the same thing as I started with.

I have figured out that results is a list of tensors. How should I decode this to get the actual output from the model. I would like to get this sorted out as quickly as possible so i can get this implemented in my project.

Thanks!

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Hello @DylDevs,

Thank you for your patience and for the additional details. Let's dive into decoding the results from the YOLOv5-seg model to get the actual output.

When you perform inference with the YOLOv5-seg model, the results are typically a list of tensors. Each tensor contains information about the detected objects, including bounding boxes, confidence scores, class labels, and segmentation masks.

Here's a step-by-step guide to decode and visualize the results:

1. Perform Inference:

   import torch
   from PIL import Image
   from torchvision.transforms import functional as F
   import cv2
   import numpy as np
   
   # Load the YOLOv5-seg model from PyTorch Hub
   model = torch.hub.load('ultralytics/yolov5', 'custom', path='path_to_yolov5s-seg.pt')
   model.eval()  # Set the model to evaluation mode
   
   # Load an image
   image = Image.open('path_to_your_image.jpg')
   image = F.to_tensor(image).unsqueeze(0)  # Transform image to tensor and add batch dimension
   
   # Perform inference
   with torch.no_grad():
       results = model(image)

2. Decode the Results:

   # Assuming results[0] contains the output for the first image in the batch
   output = results[0]
   
   # Extract bounding boxes, confidence scores, class labels, and masks
   boxes = output[:, :4].cpu().numpy()  # Bounding boxes
   scores = output[:, 4].cpu().numpy()  # Confidence scores
   class_ids = output[:, 5].cpu().numpy()  # Class labels
   masks = output[:, 6:].cpu().numpy()  # Segmentation masks
   
   # Convert the masks to the same size as the original image
   masks = masks.squeeze()  # Remove unnecessary dimensions
   masks = np.array([cv2.resize(mask, (image.width, image.height)) for mask in masks])

3. Visualize the Results:

   # Convert the image back to a format suitable for OpenCV
   image_cv = cv2.cvtColor(np.array(image.squeeze().permute(1, 2, 0) * 255, dtype=np.uint8), cv2.COLOR_RGB2BGR)
   
   for i, mask in enumerate(masks):
       # Threshold the mask to create a binary mask
       mask_binary = (mask > 0.5).astype(np.uint8) * 255
   
       # Find contours and draw them on the image
       contours, _ = cv2.findContours(mask_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
       cv2.drawContours(image_cv, contours, -1, (0, 255, 0), 2)
   
       # Draw bounding boxes and labels
       x1, y1, x2, y2 = boxes[i]
       cv2.rectangle(image_cv, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2)
       label = f"Class: {int(class_ids[i])}, Score: {scores[i]:.2f}"
       cv2.putText(image_cv, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
   
   # Display the image with detections
   cv2.imshow('YOLOv5 Segmentation', image_cv)
   cv2.waitKey(0)
   cv2.destroyAllWindows()

This should help you decode the results and visualize the segmentation masks along with bounding boxes and class labels. If you have any further questions or need additional assistance, feel free to ask. We're here to help! 😊

from yolov5.

With a little bit of modification, that code worked, thanks!

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Hello @DylDevs,

I'm glad to hear that the code modifications helped! 🎉 If you encounter any further issues or have additional questions, feel free to reach out.

To ensure we can assist you most effectively, please make sure to provide a minimum reproducible code example if you run into any new bugs or issues. This helps us reproduce the problem on our end and investigate a solution more efficiently. You can find more details on how to create a minimum reproducible example here: Minimum Reproducible Example.

Additionally, always ensure you're using the latest versions of torch and the YOLOv5 repository from Ultralytics GitHub. This ensures you have the latest features and bug fixes.

If you have any more questions or need further assistance, don't hesitate to ask. The YOLO community and the Ultralytics team are here to help!

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👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

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