Understanding YOLOv8 core pyTorch segmentation model output

[aknirala]

YOLO v8 segmentation performs both pre and post-processing steps during segmentation. In post-processing, it uses techniques like non-max suppression to generate bounding boxes and patches. I am trying to understand the output of the core PyTorch segmentation model, i.e, before post-processing.

Please find my Colab notebook here, where I am segmenting a simple image.
YbzkAf8hAaQ1YI?usp=sharing).

Essentially, my code is:

from ultralytics import YOLO
model = YOLO("yolov8n-seg.pt").to("cuda")   #Load the pre-trained model
resized_inp <= Resized this image https://ultralytics.com/images/bus.jpg, and made it in form of a batch
#the shape of resized_inp is: torch.Size([1, 3, 1080, 810])

#Now, we invoke the core model
images = resized_inp.clone().detach().to(torch.device('cuda'))
results = model.model(images)
#How to interpret the format of results?

Here is the structure of the output I get in the results:

"""
results[0] ->      tensor: torch.Size([1, 116, 17325])

results[1][0][0]-> tensor: torch.Size([1, 144, 132, 100])
results[1][0][1]-> tensor: torch.Size([1, 144, 66, 50])
results[1][0][2]-> tensor: torch.Size([1, 144, 33, 25])

results[1][1] -> tensor: torch.Size([1, 32, 17325])
results[1][2] -> tensor: torch.Size([1, 32, 264, 200])
"""

My question is: What does each tensor represent?

[glenn-jocher]

@aknirala hello!

Thank you for your detailed question and for sharing your Colab notebook. Understanding the output of the core PyTorch segmentation model in YOLOv8 before post-processing can indeed be a bit intricate. Let's break down the structure of the results you are seeing:

1. results[0]: This tensor represents the raw output of the detection head. The shape [1, 116, 17325] indicates:

   • 1: Batch size.
   • 116: Number of channels, which includes bounding box coordinates, objectness score, class scores, and mask coefficients.
   • 17325: Number of anchor points (grid cells) across all feature map levels.

2. results[1][0]: This is a list of tensors representing the feature maps at different scales. The shapes [1, 144, 132, 100], [1, 144, 66, 50], and [1, 144, 33, 25] correspond to:

   • 1: Batch size.
   • 144: Number of channels in the feature map.
   • The last two dimensions represent the height and width of the feature maps at different scales.

3. results[1][1]: This tensor [1, 32, 17325] represents the mask coefficients for each anchor point. The 32 channels correspond to the coefficients used to generate the masks.

4. results[1][2]: This tensor [1, 32, 264, 200] represents the mask feature maps. These are used in conjunction with the mask coefficients to generate the final segmentation masks.

To interpret these results, you can follow these steps:

• Bounding Boxes and Scores: Extract the bounding box coordinates, objectness scores, and class scores from results[0].
• Mask Coefficients: Use results[1][1] to get the mask coefficients for each detected object.
• Feature Maps: Use results[1][0] and results[1][2] to generate the segmentation masks.

Here's a simplified example to help you visualize the process:

import torch
from ultralytics import YOLO

# Load the model
model = YOLO("yolov8n-seg.pt").to("cuda")

# Prepare the input image
resized_inp = torch.randn(1, 3, 1080, 810).to("cuda")  # Example input

# Get the raw model output
images = resized_inp.clone().detach().to(torch.device('cuda'))
results = model.model(images)

# Extract bounding boxes and scores
bbox_scores = results[0]  # Shape: [1, 116, 17325]

# Extract mask coefficients
mask_coeffs = results[1][1]  # Shape: [1, 32, 17325]

# Extract feature maps
feature_maps = results[1][0]  # List of feature maps at different scales
mask_feature_maps = results[1][2]  # Shape: [1, 32, 264, 200]

# Further processing would involve applying non-max suppression and using the mask coefficients with feature maps to generate final masks.

For a more detailed explanation and additional resources, you can refer to the Isolating Segmentation Objects guide. This guide provides a comprehensive walkthrough on how to handle and interpret segmentation results.

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

[aknirala]

Hi @glenn-jocher,

Thank you for your detailed explanation; it makes so much sense now. I understand that the 116 in results[0] represents: 4 for the bounding box, 80 for class confidence, and 32 for mask coefficients.

Kindly let me know:

• results[1][1] is contained in results[0], correct?
• I see that the mask size is fixed, which seems to be a design choice in YOLO models. I believe features are used to derive the mask, but could you explain how this process works? Could you point me to any resources (like a paper) that explain the idea behind this? I believe the actual implementation for YOLO is in the ultralytics.utils.ops.py > process_mask(...) function.

Thanks again for your help!

[glenn-jocher]

Hi @aknirala,

Thank you for your kind words! I'm glad the explanation helped clarify things for you. Let's address your questions:

1. results[1][1] and results[0]:

   • results[1][1] contains the mask coefficients, which are indeed derived from the same output tensor as results[0]. Essentially, results[0] provides the bounding box coordinates, objectness scores, and class scores, while results[1][1] provides the mask coefficients for each detected object.

2. Fixed Mask Size and Feature Derivation:

   • You're correct that the mask size is fixed, which is a design choice in YOLO models. The process of deriving the mask involves using the mask coefficients in conjunction with the feature maps to generate the final segmentation masks.
   • The mask coefficients are applied to the feature maps to produce the masks. This process typically involves a linear combination of the feature maps weighted by the mask coefficients, followed by a sigmoid activation to obtain the final mask probabilities.

For a deeper understanding of the underlying principles, you might find the following resources helpful:

• Mask R-CNN Paper: Although not specific to YOLO, the Mask R-CNN paper provides a good foundation for understanding how instance segmentation works. You can find it here.
• YOLOv4 Paper: This paper includes some insights into the segmentation capabilities of YOLO models. You can find it here.

Regarding the implementation in YOLO, you are correct that the process_mask(...) function in ultralytics.utils.ops.py plays a crucial role. This function processes the mask coefficients and feature maps to generate the final masks.

Here's a brief overview of how the process_mask(...) function works:

def process_mask(protos, masks_in, bboxes, shape, upsample=True):
    # protos: [N, 32, H, W] - mask prototypes
    # masks_in: [N, 32, num_dets] - mask coefficients
    # bboxes: [N, num_dets, 4] - bounding boxes
    # shape: (height, width) - original image shape

    # Apply mask coefficients to prototypes
    masks = torch.einsum('bqc,bqhw->bchw', masks_in, protos)  # [N, num_dets, H, W]

    # Crop masks using bounding boxes
    masks = crop_masks(masks, bboxes)

    # Resize masks to original image shape
    if upsample:
        masks = F.interpolate(masks, size=shape, mode='bilinear', align_corners=False)

    return masks

This function takes the mask prototypes (protos), mask coefficients (masks_in), and bounding boxes (bboxes), and processes them to generate the final masks. The masks are then optionally upsampled to match the original image shape.

I hope this helps! If you have any more questions or need further clarification, feel free to ask. 😊