Computer Vision
Computer Vision models get more efficient
Pruna improves the efficiency of computer vision models to handle high resolution images without losing performance.
Computer Vision models get more efficient
Pruna improves the efficiency of computer vision models to handle high resolution images without losing performance.
Computer Vision models get more efficient
Pruna improves the efficiency of computer vision models to handle high resolution images without losing performance.
DDRNet23
Up to 30x the speed.
SegFormer-MiTB
Up to 60x the speed.
ViT-tiny-pitch16-224
Up to x85 the speed.
ResNet-50
Up to x56 the speed.
DDRNet23
Up to 30x the speed.
SegFormer-MiTB
Up to 60x the speed.
ViT-tiny-pitch16-224
Up to x85 the speed.
ResNet-50
Up x56 the speed.
DDRNet23
Up to 30x the speed.
SegFormer-MiTB
Up to 60x the speed.
ViT-tiny-pitch16-224
Up to x85 the speed.
ResNet-50
Up x56 the speed.
Tackling the Resource Challenges
Tackling the Resource Challenges
Overcoming the Computational Demands
Optimizing computer vision models is crucial for edge devices which have limited memory and need to operate in real-time.
This is where Pruna comes into play.
Efficient models are key to balancing performance and hardware constraints, ensuring smooth operation without overloading resources.
The Preferred Smashing Methods
Compilation and Quantization
The Preferred Smashing Methods
Compilation and Quantization
For computer vision models, compilation and quantization are the preferred methods to overcome hardware constraints.
Compilation
Compilation
Compilation
Optimizes model execution for hardware, ideal for tasks like real-time object detection where fast, efficient processing is key. Compilation improves execution speed on GPUs and specialized accelerators.
Optimizes model execution for hardware, ideal for tasks like real-time object detection where fast, efficient processing is key. Compilation improves execution speed on GPUs and specialized accelerators.
Quantization
Quantization
Quantization
Perfect for scenarios like autonomous vehicles where low-latency inference and power-efficient models are essential. Quantization obtains faster processing with lower memory use, ensuring smooth operation even in demanding environments.
Perfect for scenarios like autonomous vehicles where low-latency inference and power-efficient models are essential. Quantization obtains faster processing with lower memory use, ensuring smooth operation even in demanding environments.
Optimizing Computer Vision Models
Optimizing Computer Vision Models
Pruna AI Optimizing Image &
Video generation models
By using Pruna, you gain access to the most advanced optimization engine, capable of smashing any AI model with the latest compression methods for unmatched performance.
ResetNet50
ViT Base
ViT Huge
ResetNet50
ViT Base
ViT Huge
ResetNet50
ViT Base
ViT Huge
Why Do You Need Efficient AI Models?
Why Do You Need Efficient AI Models?
AI models are getting bigger, demanding more GPUs, slowing performance, and driving up costs and emissions. ML practitioners are left burdened with solving these inefficiencies.
Direct
Cost
Critical
Use cases
Key
Example
💰
Money
Budget
constraints
One H100 costs
=
-$30K per year
️⏱️
Time
User experience
Real-time reaction
ViT processes 20-100
images/sec on a A100
vs
A human processed
30-60 images/sec
📟
Memory
Edge portability
Data privacy
ViT >> 1G
vs
Arduino UNO = 32KB
⚡️
Energy / CO2
Edge portability
ESG consideration
ViT processes 1 image
on a A100
~= 1 light bulb for an hour
Direct
Cost
Critical
Use cases
Key
Example
💰
Money
Budget
constraints
One H100 costs
=
-$30K per year
️⏱️
Time
User experience
Real-time reaction
ViT processes 20-100
images/sec on a A100
vs
A human processed
30-60 images/sec
📟
Memory
Edge portability
Data privacy
ViT >> 1G
vs
Arduino UNO = 32KB
⚡️
Energy / CO2
Edge portability
ESG consideration
ViT processes 1 image
on a A100
~= 1 light bulb for an hour
Direct
Cost
Critical
Use cases
Key
Example
💰
Money
Budget
constraints
One H100 costs
=
-$30K per year
️⏱️
Time
User experience
Real-time reaction
ViT processes 20-100
images/sec on a A100
vs
A human processed
30-60 images/sec
📟
Memory
Edge portability
Data privacy
ViT >> 1G
vs
Arduino UNO = 32KB
⚡️
Energy / CO2
Edge portability
ESG consideration
ViT processes 1 image
on a A100
~= 1 light bulb for an hour
Speed Up Your Models With Pruna
Inefficient models drive up costs, slow down your productivity and increase carbon emissions. Make your AI more accessible and sustainable with Pruna.
pip install pruna[gpu]==0.1.2 --extra-index-url https://prunaai.pythonanywhere.com/
Copied
Speed Up Your Models With Pruna
Inefficient models drive up costs, slow down your productivity and increase carbon emissions. Make your AI more accessible and sustainable with Pruna.
pip install pruna[gpu]==0.1.2 --extra-index-url https://prunaai.pythonanywhere.com/
Copied
Speed Up Your Models With Pruna
Inefficient models drive up costs, slow down your productivity and increase carbon emissions. Make your AI more accessible and sustainable with Pruna.
pip install pruna[gpu]==0.1.2 --extra-index-url https://prunaai.pythonanywhere.com/
Copied
© 2024 Pruna AI - Built with Pretzels & Croissants 🥨 🥐
© 2024 Pruna AI - Built with Pretzels & Croissants
© 2024 Pruna AI - Built with Pretzels & Croissants 🥨 🥐