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--- |
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library_name: transformers |
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tags: [] |
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base_model: microsoft/conditional-detr-resnet-50 |
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--- |
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# Model Overview |
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This model is a DETR-based object detection model trained for medical image analysis, |
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4 classes : 0:Pneumonia, 1:Normal, 2:Pneumonia_bacteria, and 3:Pneumonia_virus |
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## Model Description |
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Model Architecture: DEtection TRansformers (DETR) |
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Training Data: Trained on a custom dataset of annotated medical images |
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Intended Use: Designed for analyzing chest X-ray images to detect the presence and type of pneumonia, or classify as normal.. |
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## Uses |
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Example Code test model! |
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```python |
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import os |
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from transformers import AutoImageProcessor, AutoModelForObjectDetection |
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import torch |
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from PIL import Image |
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import pandas as pd |
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folder_path = "" |
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processor = AutoImageProcessor.from_pretrained("0llheaven/CON-DETR-V5") |
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model = AutoModelForObjectDetection.from_pretrained("0llheaven/CON-DETR-V5") |
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results_list = [] |
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for image_name in os.listdir(folder_path): |
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if image_name.endswith((".jpg", ".png", ".jpeg")): |
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image_path = os.path.join(folder_path, image_name) |
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image = Image.open(image_path) |
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# RGB To grayscale |
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if image.mode != "RGB": |
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image = image.convert("RGB") |
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# prediction |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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target_sizes = torch.tensor([image.size[::-1]]) |
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results = processor.post_process_object_detection(outputs, target_sizes=target_sizes) |
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print(f"Processing image: {image_name}") |
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# detect_box |
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detected_any = False |
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for result in results: |
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scores = result["scores"] |
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labels = result["labels"] |
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boxes = result["boxes"] |
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filtered_data = [(score, label, box) for score, label, box in zip(scores, labels, boxes) if score > 0.5][:2] # จำกัดให้เก็บได้ไม่เกิน 2 กล่อง |
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for score, label, box in zip(scores, labels, boxes): |
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if score > 0.5: |
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if len(filtered_data) > 0: |
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detected_any = True |
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for score, label, box in filtered_data: |
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if label.item() == 0: |
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label_name = "Pneumonia" |
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elif label.item() == 1: |
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label_name = "Normal" |
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elif label.item() == 2: |
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label_name = "Pneumonia_bacteria" |
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else: |
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label_name = "Pneumonia_virus" |
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xmin, ymin, xmax, ymax = [round(i, 2) for i in box.tolist()] |
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print(f" - Detected {label_name} with score {round(score.item(), 3)} at {xmin, ymin, xmax, ymax}") |
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results_list.append({ |
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"image_name": image_name, |
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"label": label_name, |
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"xmin": xmin, |
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"ymin": ymin, |
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"xmax": xmax, |
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"ymax": ymax, |
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"score": round(score.item(), 3), |
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}) |
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if not detected_any: |
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print(" - No Detect") |
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results_list.append({ |
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"image_name": image_name, |
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"label": "Other", |
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"xmin": 0, |
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"ymin": 0, |
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"xmax": 0, |
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"ymax": 0, |
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"score": 0, |
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}) |
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results_df = pd.DataFrame(results_list) |
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print("\nFinal results:") |
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results_df.to_csv("testmodel.csv", index=False) |
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``` |
