| --- |
| 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 |
| --- |
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|
| ### 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> |
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| **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. |
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| </br> |
| USFS fire severity for area. |
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| LoRA adapted fuel layer overlaying USFS fire severity. |
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| **Model developer**: Mark Rodrigo |
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| **Associated code**: https://github.com/mprodrigo - coming soon |
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| **Model Architecture**: Modified U-Net |
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| ### Model Input / Output Overview: |
| - Input: 256, 256, 3 |
| - Output: 256, 256, 1 |
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| ### Training Source Tile Example |
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| ### Training Source Tile Histogram |
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| ### Further Reference |
| TODO |
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| ### 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() |
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| **TensorFlow** |
| </br> |
| https://www.tensorflow.org/tutorials/keras/save_and_load |
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| ### Evaluation / Accuracy of Target Vegetation |
| Rand Index: .92 - .96 (geographic latitude and regional vegetation color variations) |
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| ### Training and Validation data |
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| - 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. |
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| ### Training hyperparameters |
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| 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 |
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| ### Training results |
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| | 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 | |
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| ### Framework versions |
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| Keras 3.6.0 |
| </br> |
| Tensorflow 2.16.2 |
