m4te Computer Vision Model
How to use the m4te Segmentation API
Try This Model
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Model type: Roboflow 3.0 Instance Segmentation (Fast)
Dataset: m4te/5 (2852 images)
Checkpoint: COCOn-seg
Jan 22, 2024
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="m4te/5")Give your agent everything it needs
Or, Use Free Green, Ripe and Half_ripe Detection API
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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": "green, ripe, half_ripe"
},
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 m4te Model
quai vat ti hon
Roboflow Agent
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Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ m4te_dataset,
title = { m4te Dataset },
type = { Open Source Dataset },
author = { Tomatoosegg },
howpublished = { \url{ https://universe.roboflow.com/tomatoosegg/m4te } },
url = { https://universe.roboflow.com/tomatoosegg/m4te },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { feb },
note = { visited on 2026-07-29 },
}