Universe

LY-batch1 Computer Vision Dataset

byHFTask:
Semantic Segmentation
License:CC BY 4.0169 views5 downloads
See all 609 imagesLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing MetacarpusLY-batch1 Metacarpus sample showing Metacarpus

About LY-batch1 Dataset

Here are a few use cases for this project:

  1. Medical Education: Using the LY-batch1 model to create interactive learning tools for medical students, helping them identify and differentiate various Metacarpus classes and expand their knowledge of anatomical structures.

  2. Orthopedic Diagnostics: Assisting healthcare professionals in analyzing X-ray or MRI images to identify Metacarpus abnormalities or fractures, allowing for quicker and more accurate diagnosis and treatment planning.

  3. Physical Therapy and Rehabilitation: Incorporating LY-batch1 into a monitoring system to assess patients' hand movements and Metacarpus conditions in real-time, guiding them in performing exercises accurately and ensuring their progress.

  4. Virtual and Augmented Reality: Enhancing virtual and augmented reality applications with anatomically accurate Metacarpus models identified by LY-batch1, enabling users to explore and learn about the human hand in an immersive and engaging way.

  5. Hand Prosthetics and Robotics: Refining the design of prosthetic hands and robotic manipulators by recognizing the precise Metacarpus classes, aiding in the development of more accurate, functional, and user-friendly devices.

Use Free Metacarpus 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": "Metacarpus"
  },
  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 

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ ly-batch1_dataset,
  title = { LY-batch1 Dataset },
  type = { Open Source Dataset },
  author = { HF },
  howpublished = { \url{ https://universe.roboflow.com/hf-w1rwi/ly-batch1 } },
  url = { https://universe.roboflow.com/hf-w1rwi/ly-batch1 },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { aug },
  note = { visited on 2026-07-29 },
}

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