---
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).