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README.md

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+---
+license: apache-2.0
+library_name: litert
+pipeline_tag: image-classification
+tags:
+  - litert
+  - tflite
+  - android
+  - on-device
+  - gpu
+  - fine-grained
+  - plant-identification
+  - plantnet
+  - resnet18
+---
+
+# PlantNet-300K ResNet18 — LiteRT (plant species ID, GPU)
+
+On-device **fine-grained plant species identification** — 1081 species — running
+**fully on the LiteRT `CompiledModel` GPU** delegate (no CPU fallback). A
+[PlantNet-300K](https://github.com/plantnet/PlantNet-300K) (NeurIPS 2021) ResNet18.
+~16 ms/frame on a Pixel 8a.
+
+- **Architecture:** torchvision ResNet18 (pure CNN).
+- **Weights:** [cpoisson/plantnet300k-resnet18](https://huggingface.co/cpoisson/plantnet300k-resnet18) · Apache-2.0.
+- **Classes:** 1081 plant species (Latin names).
+- **Size:** 47 MB.
+
+![PlantNet-300K plant identification](hero.png)
+
+## I/O
+
+- **Input:** `[1, 3, 224, 224]` NCHW, RGB, ImageNet-normalized
+  (mean `[0.485,0.456,0.406]`, std `[0.229,0.224,0.225]`; center-crop then resize 224).
+- **Output:** `[1, 1081]` species logits — softmax + top-k for the predicted species.
+
+**Labels:** class index `i` maps to the `i`-th species when the PlantNet-300K
+species-id strings are sorted (torchvision `ImageFolder` order); names from
+`plantnet300K_species_id_2_name.json`.
+
+## GPU conversion
+
+Plain torchvision ResNet18 — a pure CNN. It converts to a fully GPU-compatible graph
+(**37/37 nodes on the delegate, 1 partition**; device corr 0.99999, top-1 match) with
+**one patch**: the ResNet stem `MaxPool2d(padding=1)` lowers to a PADV2 with `-inf`
+padding (`PADV2: src has wrong size` on the Mali delegate), replaced by an explicit
+0-pad + unpadded maxpool — exact, since the maxpool input is post-ReLU (≥ 0).
+CPU-exact vs PyTorch (corr 0.99999999999).
+
+## Minimal usage
+
+### Kotlin (Android, LiteRT CompiledModel GPU)
+
+```kotlin
+val options = CompiledModel.Options(Accelerator.GPU)
+val model = CompiledModel.create(context.assets, "plantnet.tflite", options, null)
+val inBufs = model.createInputBuffers()
+val outBufs = model.createOutputBuffers()
+
+inBufs[0].writeFloat(inputNCHW)          // [1,3,224,224], RGB, ImageNet-norm
+model.run(inBufs, outBufs)
+val logits = outBufs[0].readFloat()      // [1081]
+val top = logits.indices.sortedByDescending { logits[it] }.take(5)  // species indices
+```
+
+### Python (LiteRT / ai-edge-litert)
+
+```python
+from ai_edge_litert.interpreter import Interpreter
+import numpy as np
+
+it = Interpreter(model_path="plantnet.tflite"); it.allocate_tensors()
+inp, out = it.get_input_details(), it.get_output_details()
+it.set_tensor(inp[0]["index"], x)        # [1,3,224,224] float32, ImageNet-norm
+it.invoke()
+logits = it.get_tensor(out[0]["index"])[0]   # [1081]
+top5 = logits.argsort()[::-1][:5]
+```
+
+## Conversion
+
+Converted with **litert-torch** (`build_plantnet.py`): loads the Apache-2.0 ResNet18
+weights, applies the ZeroPadMaxPool patch, and exports.
+
+## License
+
+Apache-2.0 (weights: cpoisson/plantnet300k-resnet18). PlantNet-300K code: BSD-2-Clause
+(plantnet/PlantNet-300K, NeurIPS 2021).