Universe

JC0005_inner Computer Vision Dataset

byNguyn TrnTask:
Semantic Segmentation
License:Public Domain57 views4 downloads
See all 1.2k imagesJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, trayJC0005_inner tray sample showing section, tray

About JC0005_inner Dataset

Here are a few use cases for this project:

  1. Geological Studies: This model can be used in geological studies and research projects where identifying the type and properties of rocks based on tray and section is essential. Researchers could quickly identify and sort different rock samples in various tray classes.

  2. Mining and Mineral Processing: JC0005_inner can be useful in the mining and mineral processing industry to properly classify and organize various mineral samples that come in different tray classes, streamlining the sample study process.

  3. Educational Tool: In academic settings, the model could serve as an interactive tool for teaching students about different rock types and formations, helping them recognize various trays used in geology labs.

  4. Museum Cataloging: Museums housing geological collections could use this model to automate the process of cataloging and sorting various rock specimens according to their respective tray classes.

  5. Quality Inspection in Manufacturing: Nonetheless, the model could be applied outside geology, for example in manufacturing. Manufacturers of trays could employ it to identify defects or inconsistencies in their product lines, ensuring each piece adheres to its respective class.

Use Free Tray and Section 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": "tray, section"
  },
  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

LicensePublic Domain

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

@misc{ jc0005_inner_dataset,
  title = { JC0005_inner Dataset },
  type = { Open Source Dataset },
  author = { Nguyn Trn },
  howpublished = { \url{ https://universe.roboflow.com/nguyn-trn/jc0005_inner } },
  url = { https://universe.roboflow.com/nguyn-trn/jc0005_inner },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { feb },
  note = { visited on 2026-07-29 },
}

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