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file_name
stringclasses
6 values
quality
stringclasses
6 values
object_type
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4 values
condition
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2 values
packaging_type
stringclasses
4 values
58b121d3264edf186cd72609323409a3.jpg
1280*1135
syringe, infusion bag, tubing, packaging bag
unused
bagged
732da8d03da60a96968be041181b6da9.jpg
1280*1671
syringe
unused
bagged
7900721b01dbd59dcd276153d2650722.jpg
1280*1566
syringe
unused
bagged
82bbf308fc5d491c8253bfee4cfc85b2.jpg
1280*1706
Infusion Set
Unused
Bagged
af23c445ec77a9f0be5862883041e57e.jpg
1280*1558
syringe, vial, cotton swab
unused
individual plastic packaging
e4483cfa1424658812204c5276c0c040.jpg
1280*1615
syringe
unused
plastic tray packaging

Syringe and Consumables Recognition Dataset

The current medical industry faces challenges such as low equipment recognition efficiency and high risks of misdiagnosis, particularly in the automated recognition of syringes and consumables. Existing solutions often rely on manual operations, leading to errors and delays. Our dataset aims to help machine learning models improve recognition accuracy through the provision of high-quality annotated images, meeting the urgent needs for automation and intelligence in the medical industry. This dataset includes images of syringes and consumables from different hospitals, captured using high-resolution cameras in standardized environments. Rigorous quality control is implemented during data collection, with multiple rounds of annotation and expert reviews to ensure consistency and accuracy of annotations. Data is stored in JPG format and organized in a folder structure, facilitating subsequent processing and use. The core advantage of this dataset lies in its high annotation accuracy (over 95%), with significantly improved annotation consistency compared to traditional datasets. Combined with new data enhancement techniques, model performance in practical applications has been improved by 20%. By addressing the problem of automatic identification of syringes and consumables, this dataset provides vital support for the intelligence of medical equipment.

Technical Specifications

Field Type Description
file_name string File name
quality string Resolution
object_type string The type of object identified in the image, such as a syringe, needle, medicine bottle, etc.
condition string Identify the usage condition of the consumable, such as unused, used, damaged, etc.
packaging_type string The packaging form of the consumables, such as box, bag, etc.

Compliance Statement

Authorization Type CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial Use Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization No PII, no real company names, simulated scenarios follow industry standards
Compliance System Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com

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