Yolov5s Blood Cell
YOLOv5s-based blood cell object detection model, optimized for blood cell recognition tasks
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Release Time : 1/1/2023
Model Overview
This model is a lightweight object detection model based on the YOLOv5 architecture, specifically designed for blood cell detection tasks. It performs exceptionally well on blood cell datasets, achieving an mAP@0.5 of 0.902.
Model Features
High-Precision Detection
Achieves an mAP@0.5 accuracy of 0.902 on blood cell detection tasks
Lightweight Model
Based on the YOLOv5s architecture, the model is compact and suitable for deployment
Easy to Use
Provides simple Python API and training scripts
Model Capabilities
Blood Cell Detection
Medical Image Analysis
Object Localization
Use Cases
Medical Diagnosis
Blood Cell Counting
Automatically detects and counts various types of blood cells in blood samples
High-precision recognition of red blood cells, white blood cells, etc.
Abnormal Cell Detection
Identifies abnormal cell morphology in blood samples
Medical Research
Blood Sample Analysis
Used for automated analysis in blood-related research
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