# Resnet 50 v1

# SSD Resnet 50

## Optimizations

Optimization name | Optimization type | Precision | Dataset | Accuracy | Performance | Size | Action
Optimization1 | Quantization | int8 | dummy | 78.90% | 427.93 FPS | 25 MB |
Optimization2 | Graph optimization | fp32 | dummy | - | - | 48 MB |
Optimization3 | Quantization | int8 | MyImageRecord | - | - | - |

## Results

78.90% Accuracy [%] | 427.93 FPS Performance [sample/sec] | {{ model['size_optimized_model'] ? model['size_optimized_model'] : '-' }} Size [MB]

## Tuning parameters

Strategy: | basic
Accuracy goal: | 0.01
Objective: | performance
Timeout: | 0
Max trials: | 100
Random seed: | 9527

## Accuracy - tuning history

## Duration - tuning history

## Benchmarks

Model | Precision | Dataset | Batch size | Cores per instance | Number of instances | Accuracy | Throughput | Action | Compare
Input model | fp32 | MyImageRecord | 1 | 4 | 7 | 78.75% | 210.12 FPS |  |
Optimization1 | int8 | dummy | 4 | 28 | 1 | - | 437.12 FPS |  |
Optimization1 | int8 | dummy | 8 | 14 | 2 | - | 452.12 FPS |  |
Optimization1 | int8 | MyImageRecord | 1 | 4 | 7 | 78,90% | 425.12 FPS |  |
Optimization2 | fp32 | dummy | 32 | 7 | 4 | - | 241.12 FPS |  |
Optimization2 | fp32 | MyImageRecord | 1 | 4 | 7 | - | - |  |

## Benchmark details

Optimization: | some details
Dataset: | dummy
Iterations: | 100
Configuration: | Show config
Output: | Show log
Execution command |

## Parameters comparison

## Profiling

Model | Precision | Action
Input model | fp32 |
Optimization1 | int8 |
Optimization2 | fp32 |

## Profiling data

{{ header | modelList }} {{ header.includes('time') ? '[μs]' : null}} | Chart
run | defined
{{ row[header].run }} | {{ row[header].defined }} | {{ row[header] }} |

## Graph visualization

Model | Precision
Input model | fp32
Optimization1 | int8
Optimization2 | fp32

## Datasets

Dataset name | Dataset type | Details
dummy | Dummy | [300, 300, 3]
MyImageRecord | Real data | /dataset/ImageRecord_mini

## Dataset details

Dataset type: | ImageRecord
Dataset location: | /path/to/dataset
Calibration sampling size: | 100
Calibration batch size: | 100

## Transforms

ResizeCropImagenet
height: | 224
width: | 224
random crop: |
resize side: |
mean value: | 123.68,116.78,103.94

## Metric

topk: | 1

## Project details

Project name: | Resnet 50 v1 | SSD Resnet 50
Created at: | 25/11/2021 8:07
Modified at: | 25/11/2021 15:03
Domain: | Image recognition
Size: | 50 MB
Input model path: | {{ model['model_path'] ? getFileName(model['model_path']) : null }}

## Notes