Add MobileNetV2 0.5 Plant Village classifier

README.md

@@ -28,6 +28,7 @@ In addition, some models can be evaluated directly through [Akida Cloud](https:/
 | Vision        | Classification    | [MobileNetV2 1.0](vision/classification/mobilenetv2/imagenet)             | 224        | ImageNet      | 3.5M    | 8            | 70.35%   |          |       | 7              |
 | Vision        | Classification    | [MobileNetV2 0.75](vision/classification/mobilenetv2/imagenet)            | 160        | ImageNet      | 2.6M    | 8            | 62.85%   |          |       | 4 ☁️          |
 | Vision        | Classification    | [MobileNetV2 0.35](vision/classification/mobilenetv2/imagenet)            | 96         | ImageNet      | 1.2M    | 8            | 43.47%   |          |       | 2 ☁️          |
+| Vision        | Classification    | [MobileNetV2 0.5](vision/classification/mobilenetv2/plant_village)        | 224        | Plant Village | 0.7M    | 8            | 99.12%   |          |       | 4              |
 | Vision        | Classification    | [MobileNetV4 1.0](vision/classification/mobilenetv4/imagenet)             | 224        | ImageNet      | 3.77M   | 8            | 71.86%   |          |       | 8              |
 | Vision        | Classification    | [MobileNetV2_1.0](vision/classification/mobilenetv2/cifar10)              | 128        | CIFAR-10      | 2.25M   | 8            | 93.96%   |          |       | 5 ☁️          |
 | Vision        | Classification    | [MobileNetV2_1.0](vision/classification/mobilenetv2/oxford_flowers)       | 224        | Oxford_Flower | 2.4M    | 8            | 91.97%   |          |       | 7              |

vision/classification/mobilenetv2/plant_village/README.md

@@ -0,0 +1,69 @@
+# MobileNetV2 0.5 — Plant Village
+
+## Source
+- **Backbone**: [MobileNetV2](https://www.tensorflow.org/api_docs/python/tf/keras/applications/MobileNetV2) (tf.keras), ImageNet pretrained
+- **Alpha**: `MBV2_ALPHA = 0.5`
+- **Input resolution**: 224 × 224 × 3
+- **Classes**: 38 (Plant Village)
+- **Quantization**: 8-bit weights / 8-bit activations (QAT, `_i8_w8_a8.h5`)
+
+## Performance
+
+| Variant               | Top-1 accuracy |
+|-----------------------|----------------|
+| Float                 | 99.47%         |
+| Quantized 8w8a8 (QAT) | 99.12%         |
+
+Quantized-model sparsity — ReLU: 89%, overall: 53%.
+
+## Environment
+
+Install the exact versions used to export and convert these checkpoints — older
+or newer versions may fail to round-trip the `quantizeml` custom layers or produce
+a different Akida layer graph:
+
+```bash
+pip install \
+    akida==2.17.0 \
+    cnn2snn==2.17.0 \
+    quantizeml==1.0.1
+```
+
+## Files
+
+- `mobilenet_v2_0.5_plant_village.h5` — float Keras model
+- `mobilenet_v2_0.5_plant_village_i8_w8_a8.h5` — QAT 8w8a8 Keras model
+
+## Load the quantized model and convert to Akida
+
+```python
+import quantizeml
+from cnn2snn import convert, set_akida_version, AkidaVersion
+
+keras_model = quantizeml.load_model("mobilenet_v2_0.5_plant_village_i8_w8_a8.h5")
+
+with set_akida_version(AkidaVersion.v2):
+    akida_model = convert(keras_model)
+
+akida_model.summary()
+# Input shape:  [224, 224, 3]
+# Output shape: [1, 1, 38]
+```
+
+## Map on device (optional)
+
+```python
+import akida
+
+device = akida.SixNodesIPv2()       # or akida.devices()[0] for an Akida 2.0 FPGA
+akida_model.map(device, hw_only=True)
+akida_model.summary()
+```
+
+## References
+- MobileNetV2 — [Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
+- Dataset — [PlantVillage](https://www.tensorflow.org/datasets/catalog/plant_village)
+- TensorFlow [MobileNetV2 docs](https://www.tensorflow.org/api_docs/python/tf/keras/applications/MobileNetV2)
+
+## License
+Apache 2.0 License (models only)