YOLOv5

Use the YOLOv5 model family through our Serverless Hosted API

We support YOLOv5 inferencing via our Serverless Hosted API. YOLOv5 is available in two task variants:

• Object detection

• Instance segmentation

Training YOLOv5 on Roboflow is deprecated for new projects. Uploading your own YOLOv5 weights to a Roboflow Project and running inference against the Serverless Hosted API remains supported.

For self-hosted deployment, see Roboflow Inference.

YOLOv5 input size is set when you train your model (typical values: 640x640 or 1280x1280).

Default COCO aliases

YOLOv5 has no pretrained COCO aliases on the Serverless Hosted API. To run YOLOv5 inferencing, train a model elsewhere, upload your weights to a Roboflow Project, and call its model_id and version.

Code samples

Install the SDK and supervision for visualization:

pip install inference-sdk supervision opencv-python

Pass your Roboflow API Key via the API_KEY environment variable, and replace model_id with your workspace, project, and version.

Object detection

import os
import urllib.request

import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/cars-highway.png"
image_path = "cars-highway.png"
urllib.request.urlretrieve(image_url, image_path)

image = cv2.imread(image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")

detections = sv.Detections.from_inference(result)

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

annotated = box_annotator.annotate(scene=image.copy(), detections=detections)
annotated = label_annotator.annotate(scene=annotated, detections=detections)

cv2.imwrite("output.png", annotated)

Instance segmentation

import os
import urllib.request

import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/bicycle.png"
image_path = "bicycle.png"
urllib.request.urlretrieve(image_url, image_path)

image = cv2.imread(image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")

detections = sv.Detections.from_inference(result)

mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator()

annotated = mask_annotator.annotate(scene=image.copy(), detections=detections)
annotated = label_annotator.annotate(scene=annotated, detections=detections)

cv2.imwrite("output.png", annotated)

Set api_url to match your deployment target:

• https://serverless.roboflow.com for the Serverless Hosted API.

• http://localhost:9001 for a local Inference server.

• Your Dedicated Deployment URL for a private endpoint.