Closed breannashi closed 1 month ago
@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:
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.
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.
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.
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!
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:
For classification with yolov5s-cls
, the steps are slightly different:
To ensure consistency between the two, you can follow these steps:
Cropping Consistency:
yolov5s
) to crop the images.Image Resizing:
yolov5s-cls
), typically 224x224 or 640x640 depending on the model configuration.Normalization:
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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Question
I am creating a semi-automatic pipeline that chains object detections from yolov5 back in to the yolov5-cls (just the classifier) to enhance rare class detection, but the results are showing some drop in performance on the yolov5-cls but overall improvement. should the yolov5s-cls and the yolov5s produce identical results (for classification)? or are their some performance differences?
Additional
I ran yolov5s and yolov5-cls on the same data (cropped bounding boxes for the classifier) and received diffrent results.