Use only Classifiaction Part of a yoloV5 Detection model about ultralytics

[glenn-jocher]

@7Vyshak7 hello! 👋

To use only the classification part of your second YOLOv5 model (model2) on the crops detected by model1, you can indeed extract the classification layer from model2 and apply it to the regions of interest (ROIs) detected by model1. Here's a simplified approach:

1. Detect objects with model1:

   • Continue using model1 as you have in your script to detect objects and obtain bounding boxes.

2. Crop and classify using model2:

   • For each bounding box from model1, crop the image.
   • Use model2 to classify these cropped images. Ensure that model2 is set up or modified to function purely as a classifier (you might need to adjust the model architecture slightly for this).

Here's a basic snippet to guide you:

# Assuming model2 is modified to act as a classifier
for box in results.xyxy[0]:
    x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
    crop_img = img[y1:y2, x1:x2]
    # Classify using model2
    class_results = model2(crop_img)
    # Process class_results as needed

Note: You'll need to ensure that model2 is appropriately modified to handle the input size and format of the crops, and that it outputs class predictions.

If you need further customization on the model architecture or additional help, feel free to ask!

[7Vyshak7]

Thanks @glenn-jocher for the reply, What you said,'Ensure that model2 is set up or modified to function purely as a classifier',
is the only thing I want, the modification of model2's architecture..! Can you help on achieving that customization on the model architecture?

[glenn-jocher]

Hello @7Vyshak7,

Absolutely, I'd be glad to help guide you through customizing model2 to function purely as a classifier. Here’s a general approach:

1. Load the Model: Continue loading model2 as you currently do.
2. Modify the Architecture: You'll need to adjust the model to accept the cropped image dimensions and focus on the classification layers. This typically involves removing or adapting the final layers responsible for bounding box predictions.

Here's a conceptual example using PyTorch, assuming model2 is a YOLOv5 model:

import torch

# Load your model
model = torch.hub.load("ultralytics/yolov5", "custom", path="path_to_model2.pt")

# Assuming you want to use the output from one of the middle layers
class_only_model = torch.nn.Sequential(
    *list(model.model.children())[:-1],  # Remove the last layer
    torch.nn.Flatten(),  # Flatten the output
    torch.nn.Linear(flatten_size, num_classes),  # Add a new classification layer
    torch.nn.Softmax(dim=1)  # Softmax activation
)

Note: You'll need to adjust flatten_size and num_classes based on your specific model and number of classes.

3. Adjust Input Size: Ensure that the input size of the crops matches the input size expected by model2.

4. Test the Classifier: Before deploying, make sure to test the modified model on a few samples to verify that it classifies correctly.

If you need more specific guidance based on your model's details or further assistance, feel free to share more about your model's current architecture or any particular challenges you're facing!