Update README.md

README.md

@@ -94,35 +94,28 @@ pip install transformers
 
 ```python
 
-from transformers import YolosImageProcessor, YolosForObjectDetection
-from PIL import Image
-import torch
-import requests
+from ultralyticsplus import YOLO, render_result
 
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
+# load model
+model = YOLO('foduucom/thermal-image-object-detection')
 
-model = YolosForObjectDetection.from_pretrained('foduucom/thermal-image-object-detection')
-image_processor = YolosImageProcessor.from_pretrained("foduucom/thermal-image-object-detection")
+# set model parameters
+model.overrides['conf'] = 0.25  # NMS confidence threshold
+model.overrides['iou'] = 0.45  # NMS IoU threshold
+model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+model.overrides['max_det'] = 1000  # maximum number of detections per image
 
-inputs = image_processor(images=image, return_tensors="pt")
-outputs = model(**inputs)
+# set image
+image = '/path/to/your/document/images'
 
-# model predicts bounding boxes and corresponding COCO classes
-logits = outputs.logits
-bboxes = outputs.pred_boxes
-
-
-# print results
-target_sizes = torch.tensor([image.size[::-1]])
-results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
-for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-    box = [round(i, 2) for i in box.tolist()]
-    print(
-        f"Detected {model.config.id2label[label.item()]} with confidence "
-        f"{round(score.item(), 3)} at location {box}"
-    )
+# perform inference
+results = model.predict(image)
 
+# observe results
+print(results[0].boxes)
+render = render_result(model=model, image=image, result=results[0])
+render.show()
+```
 
 ### Compute Infrastructure