mint and weed Computer Vision Model
How to use the mint and weed Segmentation API
Try This Model
Drop an image here or click to upload
Or try a test image
Model type: Roboflow 3.0 Instance Segmentation (Accurate)
Dataset: mint-and-weed-fd3mp/14 (700 images)
Checkpoint: mint-and-weed-fd3mp/7
Nov 20, 2025
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="mint-and-weed-fd3mp/14")Give your agent everything it needs
Or, Use Free Weed, Crop and 0 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": "weed, crop, 0, cweed, weeed"
},
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 mint and weed Model
A description for this project has not been published yet.
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Cite This Project
LicenseMITIf you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ mint-and-weed-fd3mp_dataset,
title = { mint and weed Dataset },
type = { Open Source Dataset },
author = { mint and weed },
howpublished = { \url{ https://universe.roboflow.com/mint-and-weed/mint-and-weed-fd3mp } },
url = { https://universe.roboflow.com/mint-and-weed/mint-and-weed-fd3mp },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { nov },
note = { visited on 2026-07-29 },
}