Moondream2
Dedicated Deployment या self-hosted Inference पर open-vocabulary detection के लिए Moondream2 का उपयोग करें
कोड नमूना
3
मॉडल चलाएँ
import os
import cv2
import numpy as np
import requests
import supervision as sv
from inference_sdk import InferenceHTTPClient
content = requests.get("https://media.roboflow.com/notebooks/examples/dog.jpeg").content
image = cv2.imdecode(np.frombuffer(content, np.uint8), cv2.IMREAD_COLOR)
client = InferenceHTTPClient(
api_url="https://your-deployment.roboflow.cloud",
api_key=os.environ["ROBOFLOW_API_KEY"],
)
result = client.infer_lmm(
image,
model_id="moondream2",
prompt="dog",
)
preds = result["predictions"]
xyxys = [
[p["x"] - p["width"] / 2, p["y"] - p["height"] / 2,
p["x"] + p["width"] / 2, p["y"] + p["height"] / 2]
for p in preds
]
detections = sv.Detections(
xyxy=np.array(xyxys, dtype=float),
class_id=np.array([p.get("class_id", 0) for p in preds]),
confidence=np.array([p.get("confidence", 1.0) for p in preds], dtype=float),
data={"class_name": np.array([p["class"] for p in preds])},
)
labels = [f"{p['class']} {p.get('confidence', 1.0):.2f}" for p in preds]
annotated = sv.BoxAnnotator().annotate(image.copy(), detections)
annotated = sv.LabelAnnotator().annotate(annotated, detections, labels=labels)
cv2.imwrite("dog_annotated.png", annotated)
Inference speed
उपनाम
विलंबता (ms)
अंतिम अपडेट
क्या यह उपयोगी था?