api_url= "http://localhost:9001" , api_key= "***" ) with client. use_model (model_id= "soccer-players- 5fuqs/1" ): predictions = clien infer ( "https://media. roboflow.com/inference/soccer.mp4" ) SERVER STARTED STARTING
api_url= "http://localhost:9001" , api_key= "***" ) with client. use_model (model_id= "soccer-players- 5fuqs/1" ): predictions = client. infer ( "https://media. roboflow.com/inference/soccer.mp4" ) SERVER STARTED STARTING
from roboflow import Roboflow rf = Roboflow(api_key="YOUR API KEY HERE") project = rf.workspace().project("YOUR PROJECT") dataset = project.version("YOUR VERSION").download("yolov5") 该代码将以与YOLOv5 兼容的格式下载数据集,以便快速开始训练模型。更多详情,请参阅 "导出数据"部分。
获取Roboflow API密钥 现在,我们需要获取一个Roboflow API密钥,以便让Roboflow推理服务器正常工作。 步骤1: 注册一个新Roboflow帐户,输入您的凭据 步骤2: Sign in to the account, navigate to Projects > Workspaces > <your_workspace_name> > Roboflow API, and click Copy next to "Private API Key" ...
...要上传模型权重,请运行以下代码: from roboflow import Roboflow rf = Roboflow(api_key='YOUR_API_KEY') project = rf.workspace...· 查找您的工作空间和型号ID · 查找您的API密钥 一旦你运行了上面的代码,你的权重将在你的Roboflow帐户中可用,用于你的本地推理部署。
https://api.roboflow.com/:workspace/:project/images/:image_id/tags Here is an example request to the API (can "add", "remove", or "set" a tag): curl-XPOST"https://api.roboflow.com/my-workspace/my-project-name/images/image-id/tags?api_key=$ROBOFLOW_API_KEY"\-H'Content-Type: app...
/usr/local/lib/python3.9/dist-packages/roboflow/init.pyin check_key(api_key, model, notebook, num_retries) 17 def check_key(api_key, model, notebook, num_retries=0): 18 if type(api_key) is not str: —> 19 raise RuntimeError( 20 “API Key is of Incorrect Type \n Expected Type...
curl --location --request GET 'https://api.roboflow.com/${WORKSPACE}/${PROJECT}/batches/${BATCH_ID}?api_key=${ROBOFLOW_API_KEY}' This request will return data in the following format: Copy { "name": "Uploaded on 11/22/22 at 1:39 pm", "uploaded": { "_seconds": 1669146024, "...
OpenAI CLIP YOLOv5 DocTR YOLOv7 Python cURL Javascript Swift .Net from inference_sdk import InferenceHTTPClient CLIENT = InferenceHTTPClient( api_url="https://detect.roboflow.com", api_key="***" ) result = CLIENT.infer(your_image.jpg, model_id="license-plate-recognition-rxg4e/4") ARM...
fromroboflowimportRoboflow rf=Roboflow(api_key="API_KEY")project=rf.workspace().project("MODEL_ID")model=project.version(2).model job_id,signed_url=model.predict_video("football-video.mp4",fps=5,prediction_type="batch-video",)results=model.poll_until_video_results(job_id) ...