markrodrigo/image-segmentation-1.0

Model Information

Red Green Blue (RGB) binary segmentation of particular vegetation in leaf. Shrub or tree applications.
Likely medical applications as the model was originally designed for.
Originally trained to specific species. Semantically segmented for accuracy.
Keras / Tensorflow .h5 supervised model.
Opportunities are to transfer learn or further fine-tune.
Data sources are proprietary via hand drawn masked samples.
Some extrapolation of source data to synthetic data.
Novel applications - A particular weed and imagery set required.
Generic applications - Vegetation 2D area calculations. Land Cover change. Medical. Line clearing. Noxious weeds. Environmental assessments. Camouflage object detection.

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

Further Reference

TODO

Example Code

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

TensorFlow
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
Tensorflow 2.16.2