Bird-Species-Classification

This project only to show a demo of Bird Species Classification model within 1400+ species of birds.

The model is trained with 224x224 resolution.

This model has been converted to run on the Axera NPU using w8a16 quantization.

This model has been optimized with the following LoRA:

Compatible with Pulsar2 version: 5.1

Convert tools links:

For those who are interested in model conversion, you can try to export axmodel through

• Pulsar2 Link, How to Convert ONNX to axmodel

Support Platform

• AX650
  • M4N-Dock(爱芯派Pro)
  • M.2 Accelerator card
• AX630C
  • 爱芯派2
  • Module-LLM
  • LLM630 Compute Kit

Models	Platforms	latency	Top5 Accuracy	CMM size(MB)
	AX650	0.19ms
bird-s	AX630C	0.54ms	66%	1.07
	AX615	0.87ms
	AX650	0.58ms
bird-m	AX630C	2.52ms	79%	12.2
	AX615	4.97ms

How to use

Download all files from this repository to the device

root@ax650:~/Bird-Species-Classification# tree
.
├── README.md
├── axmodel_infer.py
├── class_name.txt
├── model
│   ├── AX615
│   │   ├── bird_615_npu1.axmodel
│   │   └── bird_615_npu2.axmodel
│   ├── AX620E
│   │   ├── bird_630_npu1.axmodel
│   │   └── bird_630_npu2.axmodel
│   └── AX650
│       └── bird_650_npu3.axmodel
├── onnx_infer.py
├── prediction_result_top5.png
├── quant
│   ├── Bird.json
│   ├── README.md
│   └── bird.tar.gz
└── test_images
    ├── 03111_2c0dfa5a-c4a0-47f8-ac89-6a289208050f.jpg
    ├── 03332_01b365c3-a741-4f45-bac2-4345bc901ec6.jpg
    ├── 03412_0ffc115b-43b4-4474-a373-24233f391de3.jpg
    ├── 03615_0dfbf6ae-434d-4648-b5d2-08412546ea64.jpg
    ├── 04251_3a52191e-be71-4539-98ea-14a8f2347330.jpg
    ├── 04405_0c5a6785-0bc2-49d9-9702-b9e94ba9b686.jpg
    └── 04593_3d74d5a7-15b1-4bb9-af6f-1bcd78485787.jpg

6 directories, 20 files

python env requirement

pyaxengine

https://github.com/AXERA-TECH/pyaxengine

wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3rc0/axengine-0.1.3-py3-none-any.whl
pip install axengine-0.1.3-py3-none-any.whl

Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)

root@ax650:~/Bird-Species-Classification# python3 axmodel_infer.py --image test_images/04251_3a52191e-be71-4539-98ea-14a8f2347330.jpg
[INFO] Available providers:  ['AxEngineExecutionProvider']
Loading ONNX model with providers: ['AxEngineExecutionProvider']
[INFO] Using provider: AxEngineExecutionProvider
[INFO] Chip type: ChipType.MC50
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Engine version: 2.12.0s
[INFO] Model type: 2 (triple core)
[INFO] Compiler version: 5.1-patch1 74996179

Image: test_images/04251_3a52191e-be71-4539-98ea-14a8f2347330.jpg
Top-5 Predictions:
#1: 04251_Animalia_Chordata_Aves_Passeriformes_Tityridae_Tityra_semifasciata (0.9823)
#2: 04333_Animalia_Chordata_Aves_Passeriformes_Tyrannidae_Tyrannus_savana (0.0103)
#3: 04208_Animalia_Chordata_Aves_Passeriformes_Sylviidae_Sylvia_melanocephala (0.0056)
#4: 03561_Animalia_Chordata_Aves_Coraciiformes_Alcedinidae_Todiramphus_chloris (0.0009)
#5: 04455_Animalia_Chordata_Aves_Piciformes_Picidae_Melanerpes_lewis (0.0005)
Result saved to: prediction_result_top5.png

output：