https://github.com/praeclarumjj3/backbone-profile
Inference Time Performance stats for various backbone networks.
https://github.com/praeclarumjj3/backbone-profile
inference-performance pytorch
Last synced: about 1 month ago
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Inference Time Performance stats for various backbone networks.
- Host: GitHub
- URL: https://github.com/praeclarumjj3/backbone-profile
- Owner: praeclarumjj3
- License: mit
- Created: 2020-12-21T00:10:13.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-12-28T15:24:50.000Z (over 5 years ago)
- Last Synced: 2025-10-29T02:40:59.005Z (8 months ago)
- Topics: inference-performance, pytorch
- Language: Python
- Homepage:
- Size: 178 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ResNet Profile
[](https://opensource.org/licenses/MIT)
[](https://pytorch.org/)
## Contents
1. [Overview](#1-overview)
2. [Setup Instructions](#2-setup-instructions)
3. [Repository Overview](#3-repository-overview)
4. [Reproduction](#5-reproduction)
5. [Results](#5-results)
## 1. Overview
This repo contains the folowing **Performance Stats** for a few popularly used **backbone networks** in the field of Computer Vision:
```
- Inference Time on a GTX 2080Ti
- Inference Time on a TitanXP
- Infernce Time on a CPU
- Memory Report during Inference
- Model Structures
```
I have performed experiments on **two** types of inputs:
`Size Format: (B,C,H,W)`
- **Cityscapes:** Input of size = (1,3,1024,2048)
- **PASCAL-VOC-2012:** Input of size = (1,3,500,334)
## 2. Setup Instructions
You can setup the repo by running the following commands:
```
$ git clone https://github.com/praeclarumjj3/BackBone-Profile.git
```
```
$ pip install -r requirements.txt
```
## 3. Repository Overview
The repository contains the following architecture:
- [MobileNetV2 Profiler](https://github.com/praeclarumjj3/BackBone-Profile/tree/master/MobilnetV2%20Profiler) - Scripts and stats for inference performance of **MobileNet-V2**.
- [ResNet Profiler](https://github.com/praeclarumjj3/BackBone-Profile/tree/master/ResNet%20Profiler) - Scripts and stats for inference performance of various variants of **ResNet**.
- [Xception Profiler](https://github.com/praeclarumjj3/BackBone-Profile/tree/master/Xception%20Profiler) - Scripts and stats for inference performance of **Xception**.
## 4. Reproduction
- Refer to the **README.md** of the corresponding architectures.
## 5. Results
All the experiments are performed with a `batch size=1` and 300 iterations.
#### Performance on Cityscapes
| Model | Inference Time (ms) [2080Ti] | Inference Time (ms) [TitanXP]| FPS [2080Ti] | FPS [TitanXP] | Allocated Memory (MB) | # Params (M) |
| ----------------- | ---------------------------- | ---------------------------- | -------------- | -------------- | ----------------------- | --------------|
| **ResNet-18** | **18.719** | **23.622** | **53.42** | **42.33** | **68.69** | **11.689** |
| **ResNet-34** | **31.779** | **38.588** | **31.46** | **25.91** | **108.16** | **21.797** |
| **ResNet-50** | **61.397** | **82.334** | **16.28** | **12.14** | **121.73** | **25.557** |
| **ResNet-101** | **100.426** | **122.491** | **9.95** | **8.16** | **194.65** | **44.549** |
| **MobileNet-V2** | **33.627** | **54.314** | **29.73** | **18.41** | **37.58** | **3.504** |
| **Xception** | **77.079** | **144.919** | **12.97** | **6.90** | **111.45** | **22.855** |
#### Performance on PASCAL-VOC-2012
| Model | Inference Time (ms) [2080Ti] | Inference Time (ms) [TitanXP]| FPS [2080Ti] | FPS [TitanXP] | Allocated Memory (MB) | # Params (M) |
| ----------------- | ---------------------------- | ---------------------------- | -------------- | -------------- | ----------------------- | --------------|
| **ResNet-18** | **2.547** | **2.940** | **392.61** | **340.13** | **46.60** | **11.689** |
| **ResNet-34** | **5.197** | **4.959** | **192.41** | **201.65** | **85.20** | **21.797** |
| **ResNet-50** | **7.628** | **8.927** | **131.09** | **112.01** | **100.23** | **25.557** |
| **ResNet-101** | **12.579** | **14.509** | **79.49** | **68.92** | **172.65** | **44.549** |
| **MobileNet-V2** | **5.570** | **5.795** | **179.53** | **172.56** | **14.49** | **3.504** |
| **Xception** | **7.919** | **12.042** | **126.27** | **83.04** | **89.36** | **22.855** |
### Inference Time (CPU)
#### Performance on Cityscapes
| Model | Inference Time (ms) | FPS |
| ----------------- | --------------------| --------- |
| **ResNet-18** | **566.75** | **1.76** |
| **ResNet-34** | **807.57** | **1.23** |
| **ResNet-50** | **1626.05** | **0.61** |
| **ResNet-101** | **2344.98** | **0.42** |
| **MobileNet-V2** | **560.022** | **1.78** |
| **Xception** | **2782.874** | **0.35** |
#### Performance on PASCAL-VOC-2012
| Model | Inference Time (ms) | FPS |
| ----------------- | -------------------- | --------- |
| **ResNet-18** | **55.79** | **17.92** |
| **ResNet-34** | **78.77** | **12.69** |
| **ResNet-50** | **133.71** | **7.47** |
| **ResNet-101** | **223.59** | **4.47** |
| **MobileNet-V2** | **70.180** | **14.24** |
| **Xception** | **229.000** | **4.36** |