Pharmacy Drug Image Recognition Dataset
#Image Classification #Object Recognition #Image Processing #Intelligent Pharmacy #Medical Image Recognition #Drug Management System
- 500 records
- 1.3G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-14
AI Analysis & Value Prop
The dataset is characterized by 99% annotation accuracy and 95% annotation consistency. It uses innovative multimodal image enhancement technology and quality assessment methods specific to medical images, ensuring excellent performance and a 92% recognition accuracy in various usage environments. In comparison with other medical image datasets, this dataset stands out with its refined category annotations and diverse collection conditions, providing more comprehensive training data for deep learning models. Additionally, its inclusion of over 30,000 drug images has proven to have good scalability and versatility across application scenarios. These features of rarity and quantity leadership offer it a significant competitive advantage in the market.
Dataset Insights
Sample Examples
c31d78b0**.jpg|1440*2560|832.70 KB
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81a19237**.jpg|1920*2560|849.11 KB
67e24433**.jpg|1080*1920|443.67 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| medication_name | string | The name of the medication, such as Aspirin. |
| medication_code | string | The unique identifier code for the medication. |
| dosage | string | The dosage information of the medication, such as 500mg. |
| manufacturer | string | The name of the manufacturer of the medication. |
| expiry_date | date | The expiration date of the medication. |
| package_type | string | The type of packaging of the medication, such as bottle or box. |
| active_ingredients | string | The main active ingredients in the medication. |
| medication_form | string | The physical form of the medication, such as tablet or capsule. |
| barcode_number | string | The barcode number of the medication. |
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 |
Frequently Asked Questions
- What types of images are included in the Pharmacy Medicine Image Recognition Dataset?
- This dataset primarily includes images of various pharmaceutical packaging used to train image recognition models for identifying medicines.
- What application scenarios is the Pharmacy Medicine Image Recognition Dataset suitable for?
- This dataset is suitable for developing intelligent pharmacy systems to enhance the accuracy of medicine recognition and for automating medicine management in healthcare applications.
- How is the high quality of the Pharmacy Medicine Image Recognition Dataset ensured?
- The dataset ensures high quality through rigorous collection and annotation procedures, ensuring image clarity, annotation accuracy, and diversity of pharmaceutical packaging.
- How was the Pharmacy Medicine Image Recognition Dataset collected?
- The dataset was compiled by collecting real pharmaceutical packaging images from various pharmacies and then professionally annotating and validating them.
- What are the key technical advantages of using the Pharmacy Medicine Image Recognition Dataset?
- Models trained with this dataset can significantly improve medicine recognition accuracy, reduce human errors, and enhance the overall efficiency of intelligent pharmacy systems.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Pharmacy Drug Image Recognition Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/6b52c0256f8e7fe8be32c133b86cd23a},
urldate={2026-02-04},
keywords={Pharmacy Recognition Dataset, Medical Image Dataset, Drug Image Classification},
version={1.0}
}Using this in research? Please cite us.
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