---
license: cc-by-nc-sa-4.0
language:
- en
- zh
metrics:
- accuracy
pipeline_tag: image-segmentation
tags:
- Keras
- TF-Keras
- Safetensors
- TensorFlow
- biology
- agriculture
- weeds
- vegetation
- camouflage
- deep learning
- imagery
- segmentation
- medical
- forest fire
- wildfire
- fuel
---


![image/gif](https://cdn-uploads.huggingface.co/production/uploads/66c0d74ca15b4eed7fa90867/a_ry2xQlywVEgkshiO-fJ.gif)

### 11/20/25 Note - Added safetensors and onnx formats. Report all issues. Thanks.
### 11/20/25 Note - Colorado beetlekill wildfire fuel model is inbound.

### Model Information - fire火-fuel薪-vegetation植被-image-segmentation-1.0
- Red Green Blue (RGB) binary segmentation of particular vegetation in leaf. Shrub or tree applications. 
</br>
植被叶片的红、绿、蓝二元分割。
</br>
- Likely medical applications as the model was originally designed for.
</br>
该模型最初很可能是为医疗应用而设计的。
</br>
- Originally trained to specific species. Semantically segmented for accuracy.
</br>
最初针对特定物种进行训练。为了提高准确性，进行了语义分割。
</br>
- Keras / Tensorflow .h5 supervised model.
</br>
- Opportunities are to transfer learn or further fine-tune with LoRA, etc. 
</br>
使用 LoRA 进行迁移学习或进一步微调的机会
</br>
- Data sources are proprietary via hand drawn masked samples.
</br>
数据源是通过手绘的掩蔽样本专有的。
</br>
- Some extrapolation of source data to synthetic data.
</br>
将一些源数据推断为合成数据。
</br>
- Novel applications – require specific vegetation imagery.
</br>
新颖的应用——需要特定的植被图像。
</br>
- Other applications - Vegetation 2D area calculations. Wildfire / fire fuel. Land Cover change. Medical. Line clearing. Noxious weeds. Environmental assessments. Camouflage object detection.
</br>
其他应用 - 植被 2D 面积计算。野火/火灾燃料。土地覆盖变化。医疗。线路清理。有害杂草。环境评估。伪装物体检测。
</br>

![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c0d74ca15b4eed7fa90867/Z3_EQy6lzfDUsXcN5a8SB.png)

**Forest Fire Fuel**
</br>
LoRA adapter of base segmentation vegetation model. 
</br>
**Example - Model Not currently available.**
</br>
Moderate to extreme beetle kill.
</br>
Colorado coniferous forest fuel source.
![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c0d74ca15b4eed7fa90867/Jx1-lGJ2BJzqqygD7VWt-.png)
</br>
USFS fire severity for area.
![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c0d74ca15b4eed7fa90867/vrdK83koWPZi7WsjyT8hM.png)
LoRA adapted fuel layer overlaying USFS fire severity.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c0d74ca15b4eed7fa90867/cL9zYCvEJfSUFVy2FV_pS.png)




**Model developer**: Mark Rodrigo

**Associated code**: https://github.com/mprodrigo - coming soon

**Model Architecture**: Modified U-Net

### Model Input / Output Overview:
- Input: 256, 256, 3
- Output: 256, 256, 1

### Training Source Tile Example


![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c0d74ca15b4eed7fa90867/jwKSpfmE9V4exKlaiMuCP.png)

### Training Source Tile Histogram


![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c0d74ca15b4eed7fa90867/rIwLF5VHBNAXPyPP6GFLv.png)

### Further Reference
TODO

### Example Code
**Keras**
</br>
import keras
model = keras.models.load_model('../model/image-segmentation-vegetation-1.0.keras')
</br>
model.summary()
</br>
or
</br>
import keras
loaded_model = keras.models.load_model('/home/phantom/Projects/agverde/data/product/Agverde/z1/model/image-segmentation-vegetation-1.0.h5')
</br>
loaded_model.summary()

**TensorFlow**
</br>
https://www.tensorflow.org/tutorials/keras/save_and_load

### Evaluation / Accuracy of Target Vegetation
Rand Index: .92 - .96 (geographic latitude and regional vegetation color variations)

### Training and Validation data

- 3840 256x256 RGB images and corresponding 256x256 binary mask images
- ~ 1/3 allocated to validation
- Separate test sets by latitude and region. Target species has regional color variations.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-04
- train_batch_size: 8
- eval_batch_size: 3
- distributed_type: multi-GPU
- num_devices: 2
- batch steps: 60
- eval steps: 9
- optimizer: Adam 
- num_epochs: 8

### Training results

    | Training Loss | Epoch  | Training Accuracy |
    |:-------------:|:------:|:-----------------:|
    | 0.4718        |   1    | 0.8227            |
    | 0.3869        |   2    | 0.8328            |
    | 0.3770        |   3    | 0.8403            |
    | 0.2557        |   4    | 0.8562            |
    | 0.2432        |   5    | 0.8587            |
    | 0.0856        |   6    | 0.9557            |
    | 0.0338        |   7    | 0.9870            |
    | 0.0303        |   8    | 0.9891            |


### Framework versions

Keras 3.6.0
</br>
Tensorflow 2.16.2