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USE CASE

Pigs Detection Image Annotation

100,000 photos and descriptions of people in these photos. Descriptions in the dataset are unique and do not repeat
Computer Vision Ability to recognize and analyze images and videos
Agribusiness Using machine learning in the agro-industry
Object Detection Defining the object position by Bounding Box marking
Data Labeling Highlighting objects in a photo to train the system to recognise and interpret them
1000
images
7
weeks

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CASE DESCRIPTION

Pig detection in farm photographs using Bounding Box in CVAT

Additional classification of pigs by health and breed type

The data is used for monitoring animal health, controlling population numbers, and automating record-keeping on farms

APPLICATION AREAS OF THE DATASET

Farming and livestock:

Detection for automatic identification and counting of pigs during transportation

Improving security on the farm:

Classification to determine abnormal animal behavior and the presence of foreign objects on the farm

Breeding process optimization:

Computer vision and classification to optimize the ratio of pigs of various ages and genders on the farm

Livestock monitoring system:

Computer vision for automatic tracking of the number of pigs on the farm and planning of feeding and veterinary care

DIDN'T FIND THE NECESSARY INFORMATION?

Leave a request for a free consultation and a test dataset!

Why
Training Data

  • Quality Assurance:
  • Enhanced Data Accuracy
  • Consistency in Labels
  • Reliable Ground Truth
  • Mitigation of Annotation Biases
  • Cost and Time Efficiency
  • Data Security and Confidentiality:
  • GDPR Compliance
  • Non-disclosure agreement
  • Data Encryption
  • Multiple data storage options
  • Access Controls and Authentication
  • Expert Team:
  • 6 years in industry
  • 35 top project managers
  • 40+ languages
  • 100+ countries
  • 250k+ assessors
  • Flexible and Scalable Solutions:
  • 24/7 availability of customer service
  • 100% post payment
  • $550 minimum check
  • Variable Workload
  • Customized Solutions

Team leads project

Sergey Razumny
TeamLead Crowd Solutions
Arthur Kazukevich
Python-developer
Wadim Starosotnikow
Senior quality control manager
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