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license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- biology
---
# 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](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html)
## Support Platform
- AX650
- [M4N-Dock(η±θ―ζ΄ΎPro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
- [M.2 Accelerator card](https://docs.m5stack.com/zh_CN/ai_hardware/LLM-8850_Card)
- AX630C
- [η±θ―ζ΄Ύ2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html)
- [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM)
- [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit)
| 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
.
βββ README.md
βββ axmodel_infer.py
βββ class_name.txt
βββ model
βΒ Β βββ AX615
βΒ Β βΒ Β βββ bird_615_npu1.axmodel
βΒ Β βΒ Β βββ bird_615_npu2.axmodel
βΒ Β βββ AX620E
βΒ Β βΒ Β βββ bird_630_npu1.axmodel
βΒ Β βΒ Β βββ bird_630_npu2.axmodel
βΒ Β βββ AX650
βΒ Β βββ bird_650_npu3.axmodel
βββ onnx_infer.py
βββ prediction_result_top5.png
βββ quant
βΒ Β βββ Bird.json
βΒ Β βββ README.md
βΒ Β βββ bird.tar.gz
βββ test_images
βββ 03111_2c0dfa5a-c4a0-47f8-ac89-6a289208050f.jpg
βββ 03332_01b365c3-a741-4f45-bac2-4345bc901ec6.jpg
βββ 03412_0ffc115b-43b4-4474-a373-24233f391de3.jpg
βββ 03615_0dfbf6ae-434d-4648-b5d2-08412546ea64.jpg
βββ 04251_3a52191e-be71-4539-98ea-14a8f2347330.jpg
βββ 04405_0c5a6785-0bc2-49d9-9702-b9e94ba9b686.jpg
βββ 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(η±θ―ζ΄ΎPro)
```
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
```
outputοΌ
<img src="./prediction_result_top5.png" width="200%" alt="ι’ζ΅η»ζ" /> |