Yolov5s versus Yolov5s-cls about yolov5

[glenn-jocher]

@breannashi hello,

Thank you for reaching out and for your detailed question!

To address your query, the yolov5s and yolov5s-cls models are designed for different purposes. The yolov5s model is primarily for object detection, while yolov5s-cls is optimized for classification tasks. Although they share a common architecture, their training processes and optimizations differ, which can lead to variations in performance and results.

Here are a few points to consider:

1. Model Training and Optimization: The yolov5s model is trained to detect objects and their bounding boxes, while the yolov5s-cls model is trained specifically for classification. This difference in training objectives can lead to variations in performance when using them for classification tasks.

2. Data Preprocessing: Ensure that the data preprocessing steps for both models are consistent. Differences in how the data is prepared and fed into the models can impact the results.

3. Evaluation Metrics: Make sure you are using the same evaluation metrics for both models to compare their performance accurately.

If you are experiencing a drop in performance with yolov5s-cls, it might be helpful to fine-tune the model on your specific dataset. Additionally, verifying that you are using the latest versions of torch and the YOLOv5 repository can help ensure you benefit from the latest improvements and bug fixes.

If you could provide a minimum reproducible code example, it would help us investigate the issue more effectively. You can refer to our minimum reproducible example guide for more details on how to create one.

Feel free to share any additional details or code snippets that might help us understand the issue better.

[breannashi]

Could you point me to the data processing steps used in the object detection mode for classification. For example, I was to ensure my cropping is consistent across the two versions of the sets.
Thank you!

[glenn-jocher]

Hello @breannashi,

Thank you for your question! Ensuring consistent data processing between object detection and classification tasks is crucial for achieving reliable results.

For object detection with yolov5s, the data processing steps typically involve:

1. Image Augmentation: This includes resizing, flipping, and other transformations to increase the diversity of the training data.
2. Bounding Box Normalization: Bounding boxes are normalized to the image dimensions.
3. Label Encoding: Labels are encoded in a format suitable for detection tasks.

For classification with yolov5s-cls, the steps are slightly different:

1. Image Cropping: Cropping the detected objects from the original images based on the bounding boxes provided by the detection model.
2. Image Resizing: Resizing the cropped images to a fixed size that the classification model expects.
3. Normalization: Normalizing the pixel values to a range suitable for the classification model.

To ensure consistency between the two, you can follow these steps:

1. Cropping Consistency:

   • Use the bounding boxes from the detection model (yolov5s) to crop the images.
   • Ensure that the cropping is done accurately without any padding or scaling.

2. Image Resizing:

   • Resize the cropped images to the input size expected by the classification model (yolov5s-cls), typically 224x224 or 640x640 depending on the model configuration.

3. Normalization:

   • Apply the same normalization techniques (e.g., mean subtraction, scaling) used during the training of the classification model.

Here is a sample code snippet to illustrate the cropping and resizing process:

from PIL import Image
import numpy as np

def crop_and_resize(image, bbox, target_size=(224, 224)):
    # Crop the image using the bounding box
    cropped_image = image.crop((bbox[0], bbox[1], bbox[2], bbox[3]))
    
    # Resize the cropped image to the target size
    resized_image = cropped_image.resize(target_size, Image.ANTIALIAS)
    
    # Normalize the image (example normalization)
    normalized_image = np.array(resized_image) / 255.0
    
    return normalized_image

# Example usage
image = Image.open('path_to_image.jpg')
bbox = [xmin, ymin, xmax, ymax]  # Bounding box coordinates
processed_image = crop_and_resize(image, bbox)

If you haven't already, please ensure you are using the latest versions of torch and the YOLOv5 repository to benefit from the latest updates and improvements.

For more detailed guidance on data processing and training, you can refer to our Tips for Best Training Results.

If you encounter any issues or have further questions, feel free to share a minimum reproducible code example. This will help us investigate and provide more accurate assistance. You can find more details on how to create one here.

Best of luck with your project! 🚀

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