COCO 128 Computer Vision Model
How to use the COCO 128 Detection API
Try This Model
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Model type: Roboflow 3.0 Object Detection (Fast)
Dataset: coco-128-nxvyz/1 (372 images)
Checkpoint: COCO
Jul 19, 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="coco-128-nxvyz/1")Give your agent everything it needs
Or, Use Free Car, Truck and Bus 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": "car, truck, bus, dog, cat"
},
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 COCO 128 Model
COCO 128 is a subset of 128 images of the larger COCO dataset. It reuses the training set for both validation and testing, with the purpose of proving that your training pipeline is working properly and can overfit this small dataset.
COCO 128 is a great dataset to use the first time you are testing out a new model.
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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{ coco-128-nxvyz_dataset,
title = { COCO 128 Dataset },
type = { Open Source Dataset },
author = { bandrai },
howpublished = { \url{ https://universe.roboflow.com/bandrai/coco-128-nxvyz } },
url = { https://universe.roboflow.com/bandrai/coco-128-nxvyz },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { jul },
note = { visited on 2026-07-29 },
}










