--- 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(爱芯派Pro)](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 - [爱芯派2](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 . ├── 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： 预测结果