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Project details
I am a deep learning and machine learning expert having experience of 2 years in computer vision. and have a good command on binary/multiclass and multi-label image classification models using CNN, also good practices on SVM, Transfer learning and on pre-trained models with python language.
So here are the services included in this project:
Binary image classification
Multiclass image classification
Multi-label image classification
Convolutional neural network
Spot detection
Plant leave disease classification
So here are the services included in this project:
Binary image classification
Multiclass image classification
Multi-label image classification
Convolutional neural network
Spot detection
Plant leave disease classification
What's included
| Service Tiers |
Starter
$100
|
Standard
$200
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 4 days | 8 days | 25 days |
Number of Revisions | 2 | 2 | 5 |
Number of Model Variations | 1 | 2 | 2 |
Number of Scenarios | 1 | 2 | 4 |
Number of Graphs/Charts | 3 | 4 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - | - |
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It was a great experience working with this Humaira! Clear communication, professional attitude. I would be happy to work together again in the future. Highly recommended!
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About Humaira
Medical Image Segmentation Expert | PhD AI Researcher | PyTorch & ViTs
50%
Job Success
Jampur, Pakistan - 4:06 pm local time
Service:
• Medical image segmentation (polyp / MRI / CT)
• Paper implementation (research → code)
• Vision Transformer fine-tuning
• Dataset preprocessing + evaluation
“I help researchers implement deep learning models for medical image segmentation (PyTorch).”
Steps for completing your project
After purchasing the project, send requirements so Humaira can start the project.
Delivery time starts when Humaira receives requirements from you.
Humaira works on your project following the steps below.
Revisions may occur after the delivery date.
analysis data
first, check the data, and after training a your dataset adding your requirement
