# Sharpiless/Yolov5-Deepsort

最新版本yolov5+deepsort目标检测和追踪，能够显示目标类别，支持5.0版本可训练自己数据集

Repository: https://github.com/Sharpiless/Yolov5-Deepsort
Canonical: https://ross.abutalabs.com/products/yolov5-deepsort
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: pytorch, computer-vision, object-detection, object-tracking, yolov5, deepsort
Last push: 2022-10-06T08:33:23+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1925, "days_push": 1427, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1154, forks 171 (observed 2026-08-28T04:03:47.745972+00:00)

## What it is
A Python project combining YOLOv5 object detection with DeepSort multi-object tracking, packaged as a Detector class for vehicle and pedestrian tracking and counting. Supports training custom YOLOv5 models.

## Use cases
- track vehicles and pedestrians in video
- count objects crossing a frame
- run yolov5 detection with deepsort tracking
- train custom yolov5 model for detection and tracking
- integrate object tracking into my python project

## When to choose
- you need YOLOv5 detection plus DeepSort tracking in one pipeline
- you want a simple Detector class to embed in your project

## When to avoid
- you need actively maintained code or recent YOLOv5 versions
- you need tracking beyond person/car/truck classes without modification

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing
- domain: computer-vision
- platform: python
- tags: yolov5, deepsort, object-tracking

## Member repositories
- Sharpiless/Yolov5-Deepsort (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:47.745972+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:32:15.836561+00:00, confidence not recorded.
  - readme: https://github.com/Sharpiless/Yolov5-Deepsort (fetched 2026-08-28T04:03:47.745972+00:00, sha 57df8f69e2ee)
- Data as of 2026-08-30T08:39:29.467469+00:00.