LMS_train Computer Vision Model
How to use the LMS_train Detection API
Try This Model
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Or try a test image
Model type: Roboflow RF-DETR Object Detection (Nano)
Dataset: lms_train/4 (468 images)
Checkpoint: lms_train/3
Feb 20, 2026
Code Snippets
from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="lms_train/4")Give your agent everything it needs
Or, Use Free Dog, Cat and Bottle Detection API
Powered by general detection model
Code
pip install inference-sdk# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workspace
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="<YOUR_WORKSPACE>",
workflow_id="<YOUR_WORKFLOW_ID>",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
parameters={
"classes": "dog, cat, bottle, chair, box"
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Run on custom image
Drop an image here or click to upload
Detecting classes:
Or try a test image
About LMS_train Model
Training Image for my project of Cooperative so real
Roboflow Agent
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ lms_train_dataset,
title = { LMS_train Dataset },
type = { Open Source Dataset },
author = { Test },
howpublished = { \url{ https://universe.roboflow.com/test-8cptb/lms_train } },
url = { https://universe.roboflow.com/test-8cptb/lms_train },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2026 },
month = { feb },
note = { visited on 2026-07-29 },
}

