Datasets:
file_name stringclasses 6 values | quality stringclasses 6 values | object_type stringclasses 4 values | condition stringclasses 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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