anotado Computer Vision Dataset
About anotado Dataset
https://www.youtube.com/watch?v=4MA_6oZQz7s&ab_channel=tektronix475
Spotted caps, are the normal OK class (fully closed). Clean caps, are the bad or anomally target class (partially closed). One double prediction at 3:59. 100x100 classification accuracy, out of 200 samples. Inference over unseen test dataset. 150 epochs training. 700 samples training dataset, no data augmentation.
PREPROCESSING Auto-Orient: Applied Resize: Stretch to 416x416 Grayscale: Applied AUGMENTATIONS No augmentations were applied.
Anomaly detection with: Roboflow, tensorflow, google colab, Ultralytics, yolo v5, cvat,
Use Free Bad and Ok Detection API
Powered by general detection model
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": "bad, ok"
},
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
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{ anotado_dataset,
title = { anotado Dataset },
type = { Open Source Dataset },
author = { new-workspace-lzcyx },
howpublished = { \url{ https://universe.roboflow.com/new-workspace-lzcyx/anotado } },
url = { https://universe.roboflow.com/new-workspace-lzcyx/anotado },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2021 },
month = { oct },
note = { visited on 2026-07-29 },
}