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Train YOLOv5 object detection model with custom dataset
In this tutorial we will download object detection data in YOLOv5 format from Roboflow. In the tutorial, we train YOLOv5 to detect cells in the blood stream with a public blood cell detection dataset. You can follow along with the public blood cell dataset or upload your own dataset.
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The YOLO family of object detection models grows ever stronger with the introduction of YOLOv5. In this post, we will walk through how you can train YOLOv5 to recognize your custom objects for your use case.
In this tutorial we will download object detection data in YOLOv5 format from Roboflow. In the tutorial, we train YOLOv5 to detect cells in the blood stream with a public blood cell detection dataset. You can follow along with the public blood cell dataset or upload your own dataset.
We use a public blood cell detection dataset, which you can export yourself. You can also use this tutorial on your own custom data.
01.Requirements
!git clone https://github.com/ultralytics/yolov5 # clone
%cd yolov5
%pip install -qr requirements.txt # install
02. Usage
import torch
import utils
display = utils.notebook_init() # checks
!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images
# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)
Roboflow enables you to easily organize, label, and prepare a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the roboflow pip package.
- Custom Training Example:
- Custom Training Notebook:
Train YOLOv5 Model On a Custom Dataset
- Category : LLM
- Time Read:10 Min
- Source: Roboflow
- Author: Partener Link
- Date: June 23, 2023, 3:18 p.m.
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