chunked/nltk_chunking/tasks_object_detection/chunk_74.txt

Instantiate a pipeline
for object detection with your model, and pass an image to it:

from transformers import pipeline
import requests
url = "https://i.imgur.com/2lnWoly.jpg"
image = Image.open(requests.get(url, stream=True).raw)
obj_detector = pipeline("object-detection", model="devonho/detr-resnet-50_finetuned_cppe5")
obj_detector(image)

You can also manually replicate the results of the pipeline if you'd like:

image_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
with torch.no_grad():
     inputs = image_processor(images=image, return_tensors="pt")
     outputs = model(**inputs)
     target_sizes = torch.tensor([image.size[::-1]])
     results = image_processor.post_process_object_detection(outputs, threshold=0.5, 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}"
     )
Detected Coverall with confidence 0.566 at location [1215.32, 147.38, 4401.81, 3227.08]
Detected Mask with confidence 0.584 at location [2449.06, 823.19, 3256.43, 1413.9]

Let's plot the result:

draw = ImageDraw.Draw(image)
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
     box = [round(i, 2) for i in box.tolist()]
     x, y, x2, y2 = tuple(box)
     draw.rectangle((x, y, x2, y2), outline="red", width=1)
     draw.text((x, y), model.config.id2label[label.item()], fill="white")
image