Tell the qwen 9b model to help you design something lighter! It's a decent coding model, it can sure assist you with that. For example:

CLIP is an "image-text" model that is much smaller and faster than a 9B VLM, but smart enough to understand colors. We use YOLO to find the cars first, then let CLIP "look" at the crops to confirm which ones are red.

The "Find & Confirm" Script

This script uses YOLO to find the objects and CLIP to verify the color.

Python

import cv2
from ultralytics import YOLO
import torch
from PIL import Image
from transformers import CLIPProcessor, CLIPModel

# 1. Load the "Specialists"
# YOLO for finding cars (The "Eyes")
detector = YOLO("yolo11n.pt") 
# CLIP for identifying "red" (The "Brain")
classifier = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

def find_red_cars(image_path):
    img = cv2.imread(image_path)
    results = detector(img, verbose=False)

    found_red_car = False

    for r in results:
        for box in r.boxes:
            # Check if object is a car (class 2 in COCO)
            if int(box.cls) == 2:
                # Crop the car from the image
                x1, y1, x2, y2 = map(int, box.xyxy[0])
                car_crop = img[y1:y2, x1:x2]
                car_pil = Image.fromarray(cv2.cvtColor(car_crop, cv2.COLOR_BGR2RGB))

                # Ask CLIP: "Is this car red or another color?"
                inputs = processor(
                    text=["a red car", "a car that is not red"],
                    images=car_pil,
                    return_tensors="pt",
                    padding=True
                )

                outputs = classifier(**inputs)
                probs = outputs.logits_per_image.softmax(dim=1)

                # If "red car" has the highest probability
                if probs[0][0] > 0.8: # 80% confidence threshold
                    print(f"🎯 Red car spotted in {image_path}!")
                    found_red_car = True

    return found_red_car

# Run it
find_red_cars("parking_lot.jpg")

Why this architecture is superior:

• Modular: If you suddenly decide you want to find blue trucks, you just change the YOLO class to 7 (truck) and the CLIP text to "a blue truck".
• Performance: You aren't running the "Brain" (CLIP) on the whole 4K image. You only run it on the tiny little square where the "Eyes" (YOLO) already found a car.
• Hardware: This will run comfortably on a laptop with 8GB of RAM, whereas a 9B model would likely crash it or crawl at one frame per minute.

By chaining these two models, you've created a specialized pipeline that is faster, cheaper, and more accurate than the "giant model" approach.