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license: mit
language:
- en
pipeline_tag: image-to-image
tags:
- dehazing
- ImageEnhancement
---
# ImageDehazing
This is a collection of image dehazing algorithms, models have been converted to run on the Axera NPU using **w8a8** quantization.
This model has been optimized with the following LoRA:
Compatible with Pulsar2 version: 6.0 115775d3
## 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/en/ai_hardware/LLM-8850_Card)
| 模型 | 输入分辨率 | AX650板端耗时 |
|------|-----------|--------------|
| AOD-Net | 640×480 | 2.4 ms |
| Light-Dehazenet | 480×640 | 9.2 ms |
| DehazeFormer_t | 512×512 | 161ms |
| MixDehazeNet | 256×256 | 38 ms |
| GCANet | 512×512 | 80 ms |
| GridDehazeNet | 640×480 | 113 ms |
| DEA-Net | 512×512 | 127 ms |
| FFA-Net | 512×512 | 873 ms |
## How to use
Download all files from this repository to the device
```
所有模型文件组织方式均如下：
.
|-- model_convert
| |-- axmodel
| | `-- dehazeformer-t-512-constant.axmodel
| `-- dehazeformer.json
|-- pic
| `-- 00000_0_0.1800.png
|-- python
| |-- axmodel_infer.py
| `-- onnx_infer.py
`-- res
`-- output.png
```
### Inference
#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)
模型推理，执行命令:
```
(base) root@ax650:~/GCANet# python axmodel_infer.py
[INFO] Available providers: ['AxEngineExecutionProvider', 'AXCLRTExecutionProvider']
[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: 7.0 22923e4e
Saved: axmodel_output/0051_0.8_0.2_input_dehaze_compare.png
Saved: axmodel_output/0099_0.9_0.16_input_dehaze_compare.png
```
推理结果样例：
 |