Per Detection class accuracy on validation set about yolov5

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[glenn-jocher]

Hi Bree! 😊

It sounds like you're looking to refine the output of your detection results to focus on class comparison for high-confidence detections. You can achieve this by modifying the detect.py script to include additional logging for each detection that meets your criteria (e.g., highest IOU match).

Here’s a basic snippet you can add to your detect.py after detections are processed to filter and save the desired data:

import pandas as pd

# Assuming 'results' holds your model's output
detections = results.xyxy[0]  # detections for the first image in batch
high_conf_detections = detections[detections[:, 4] > confidence_threshold]  # confidence threshold, e.g., 0.25

# Prepare DataFrame
df = pd.DataFrame(high_conf_detections.numpy(), columns=['x1', 'y1', 'x2', 'y2', 'conf', 'class'])
df['image'] = 'name_of_image.jpg'  # Update with actual dynamic image name handling

# Save to CSV
df.to_csv('detections.csv', index=False)

This script snippet assumes you have a threshold for what you consider 'high confidence' and filters detections accordingly. You'll need to integrate it properly with the image handling in your existing detect.py script to dynamically assign image names.

For more detailed modifications, you might need to dive deeper into the post-processing steps of the detection script. If you need further guidance on this, feel free to check out the tutorials at https://docs.ultralytics.com/yolov5/tutorials/model_export/.

Hope this helps! 🚀

[breannashi]

This helps with editing the final prediction csv but I am still wondering how it will compare with the validation labels because when I use detection it seems to be agnostic of the fact that I have labels for those values.

[glenn-jocher]

@breannashi hi there!

To compare your detections with validation labels, you can modify the script to load the corresponding label files and perform a comparison. Here's a concise way to do it:

1. Load the label file for each image (assuming label files are in the same order as your images).
2. Compare the predicted classes and bounding boxes with the ground truth labels.
3. Calculate metrics like IOU to determine matches.

Here’s a basic example:

import numpy as np

# Load ground truth labels for an image
gt_labels = np.loadtxt('path_to_label_file.txt')

# Assuming 'detections' is your DataFrame from the previous example
for index, detection in detections.iterrows():
    # Compare detection['class'] with classes in gt_labels and calculate IOU, etc.
    # You can add conditions to filter matches based on IOU threshold

This approach requires you to align each detection with its corresponding label file and implement the logic for comparison. This might involve additional functions to calculate IOU or other metrics based on your specific needs.

Hope this points you in the right direction! 😊