---
library_name: transformers
tags: []
base_model: microsoft/conditional-detr-resnet-50
---

# Model Overview

This model is a DETR-based object detection model trained for medical image analysis,
4 classes : 0:Pneumonia, 1:Normal, 2:Pneumonia_bacteria, and 3:Pneumonia_virus

## Model Description
Model Architecture: DEtection TRansformers (DETR)
Training Data: Trained on a custom dataset of annotated medical images
Intended Use: Designed for analyzing chest X-ray images to detect the presence and type of pneumonia, or classify as normal..
## Uses
Example Code test model!

```python
import os 
from transformers import AutoImageProcessor, AutoModelForObjectDetection
import torch
from PIL import Image
import pandas as pd

folder_path = ""
processor = AutoImageProcessor.from_pretrained("0llheaven/CON-DETR-V5")
model = AutoModelForObjectDetection.from_pretrained("0llheaven/CON-DETR-V5")

results_list = []

for image_name in os.listdir(folder_path):
    if image_name.endswith((".jpg", ".png", ".jpeg")): 
        image_path = os.path.join(folder_path, image_name)
        image = Image.open(image_path)

        # RGB To grayscale
        if image.mode != "RGB":
            image = image.convert("RGB")

        # prediction
        inputs = processor(images=image, return_tensors="pt")
        outputs = model(**inputs)

        target_sizes = torch.tensor([image.size[::-1]])
        results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)
        print(f"Processing image: {image_name}")

        # detect_box
        detected_any = False
        for result in results:
            scores = result["scores"]
            labels = result["labels"]
            boxes = result["boxes"]

            filtered_data = [(score, label, box) for score, label, box in zip(scores, labels, boxes) if score > 0.5][:2] # จำกัดให้เก็บได้ไม่เกิน 2 กล่อง
            for score, label, box in zip(scores, labels, boxes):
                if score > 0.5:
                    if len(filtered_data) > 0:
                        detected_any = True
                            
                    for score, label, box in filtered_data:
                        if label.item() == 0:
                            label_name = "Pneumonia"
                        elif label.item() == 1:
                            label_name = "Normal"
                        elif label.item() == 2:
                            label_name = "Pneumonia_bacteria"
                        else:
                            label_name = "Pneumonia_virus"
                        
                        xmin, ymin, xmax, ymax = [round(i, 2) for i in box.tolist()]
                        print(f" - Detected {label_name} with score {round(score.item(), 3)} at {xmin, ymin, xmax, ymax}")
                        results_list.append({
                            "image_name": image_name,
                            "label": label_name,
                            "xmin": xmin,
                            "ymin": ymin,
                            "xmax": xmax,
                            "ymax": ymax,
                            "score": round(score.item(), 3),
                        })

        if not detected_any:
            print(" - No Detect")
            results_list.append({
                "image_name": image_name,
                "label": "Other",
                "xmin": 0,
                "ymin": 0,
                "xmax": 0,
                "ymax": 0,
                "score": 0,
            })

results_df = pd.DataFrame(results_list)
print("\nFinal results:")
results_df.to_csv("testmodel.csv", index=False)
```