---
license: apache-2.0
datasets:
- imagenet-1k
library_name: torchvision
pipeline_tag: image-classification
tags:
- onnx
- ENOT-AutoDL
---

# ENOT-AutoDL pruning benchmark on ImageNet-1k

This repository contains models accelerated with [ENOT-AutoDL](https://pypi.org/project/enot-autodl/) framework.
Models from [Torchvision](https://pytorch.org/vision/stable/models.html) are used as a baseline.
Evaluation code is also based on Torchvision references.

## ResNet-50

| Model                     | Latency (MMACs) | Accuracy (%)  |
|---------------------------|:---------------:|:-------------:|
| **ResNet-50 Torchvision** |     4144.85     |     76.14     |
| **ResNet-50 ENOT (x2)**   | 2057.61 (x2.01) | 75.48 (-0.66) |
| **ResNet-50 ENOT (x4)**   | 867.94 (x4.77)  | 73.58 (-2.57) |

## ViT-B/32

| Model                    | Latency (MMACs) | Accuracy (%)  |
|--------------------------|:---------------:|:-------------:|
| **ViT-B/32 Torchvision** |     4413.99     |     75.91     |
| **ViT-B/32 ENOT (x4.8)** | 911.80 (x4.84)  | 75.68 (-0.23) |
| **ViT-B/32 ENOT (x9)**   | 490.78 (x8.99)  | 73.72 (-2.19) |

## MobileNetV2

| Model                       | Latency (MMACs) | Accuracy (%)  |
|-----------------------------|:---------------:|:-------------:|
| **MobileNetV2 Torchvision** |     334.23      |     71.88     |
| **MobileNetV2 ENOT**        | 156.80 (x2.13)  | 69.90 (-1.98) |

# Validation

To validate results, follow this steps:

1. Install all required packages:
   ```bash
   pip install -r requrements.txt
   ```
1. Calculate model latency:
   ```bash
   python measure_mac.py --model-ckpt path/to/model.pth
   ```
1. Measure accuracy of ONNX model:
   ```bash
   python test.py --data-path path/to/imagenet --model-onnx path/to/model.onnx --batch-size 1
   ```
1. Measure accuracy of PyTorch (.pth) model:
   ```bash
   python test.py --data-path path/to/imagenet --model-ckpt path/to/model.pth
   ```

If you want to book a demo, please contact us: enot@enot.ai .