PS4gamepadchacker Computer Vision Model
How to use the PS4gamepadchacker Detection API
Try This Model
Drop an image here or click to upload
Or try a test image
Model type: Roboflow 3.0 Object Detection (Fast)
Dataset: ps4gamepadchacker/2 (1863 images)
Checkpoint: ps4gamepadchacker/1
Apr 23, 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="ps4gamepadchacker/2")Give your agent everything it needs
Or, Use Free Objects, Push_circle and Push_cross 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": "objects, push_circle, push_cross, push_down, push_L1"
},
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 PS4gamepadchacker Model
A description for this project has not been published yet.
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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{ ps4gamepadchacker_dataset,
title = { PS4gamepadchacker Dataset },
type = { Open Source Dataset },
author = { test },
howpublished = { \url{ https://universe.roboflow.com/test-zd7qg/ps4gamepadchacker } },
url = { https://universe.roboflow.com/test-zd7qg/ps4gamepadchacker },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { apr },
note = { visited on 2026-07-29 },
}