Leaf count Computer Vision Model
How to use the Leaf count Detection API
Try This Model
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Model type: Roboflow 3.0 Object Detection (Fast)
Dataset: leaf-count-gonnx/1 (199 images)
Checkpoint: COCO
Jan 30, 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="leaf-count-gonnx/1")Give your agent everything it needs
Or, Use Free Leaf 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": "leaf"
},
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 Leaf count Model
An oriented bounding box dataset that detects leafs in Neptune grass
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{ leaf-count-gonnx_dataset,
title = { Leaf count Dataset },
type = { Open Source Dataset },
author = { TFG },
howpublished = { \url{ https://universe.roboflow.com/tfg-csujy/leaf-count-gonnx } },
url = { https://universe.roboflow.com/tfg-csujy/leaf-count-gonnx },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { jun },
note = { visited on 2026-07-29 },
}