thenukegun10x/PLantDetect-WA

PlantDetect: Western Australia Plant Vision (999 Species)

Built with DINOv3

Fine-grained botanical vision models specializing in the flora of Western Australia.

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1. Multi-View Botanical Inference (Single vs. Multi-Photo)

In the field, plant species can be difficult to distinguish from a single photo. By providing multiple complementary angles of the same plant (e.g. Flower, Leaf, Habit, Fruit), the model achieves near-perfect classification accuracy:

Photos Provided by User	Mobile Dense Student (plant_dense_4view) — PlantDetect-Dense-4View-BF16	Server MoE++ BEST (PlantDetect-BF16)	Real-World Botanical Impact
1 Photo (Single-Shot)	82.08% Top-1 (93.03% Top-5)	89.31% Top-1 (96.14% Top-5)	Fast single photo identification
2 Photos (Dual-Angle)	94.27% Top-1 (99.28% Top-5)	97.11% Top-1 (99.66% Top-5)	Flower + Leaf resolves 94%+ of species
3 Photos (Tri-Angle)	97.45% Top-1 (99.84% Top-5)	98.77% Top-1 (99.95% Top-5)	Flower + Leaf + Growth habit
4 Photos (Quad-Angle)	98.31% Top-1 (99.90% Top-5)	99.42% Top-1 (100.00% Top-5)	99.9% Top-5 certainty in the field

Re-benched 2026-08-30 sequential BF16 27,673 val, K-view mean logits via bench_multiview_finals.py (data/bench_multiview_finals.json) — PlantDetect-Dense-4View-BF16 / PlantDetect-BF16.

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2. Complete Model Artifact Catalog (Direct Download Links)

A. Mobile Edge & Embedded Models (Dense ViT-Base)

Optimized for Samsung Galaxy S24 (Exynos 2400 / Snapdragon 8 Gen 3), iOS, Windows DirectML, and Raspberry Pi.

Model File	Model Type	Runtime Target	Format / Precision	File Size	Top-1 (1-View)	Top-1 (4-View)	Target Hardware
PlantDetect-Dense-INT8.safetensors	Single-Shot	PyTorch / Python	Standard W8A16 INT8	93.3 MB	83.02%	97.34%	Ultra-compact Python edge
PlantDetect-Dense-FP8-AdaRound.safetensors	Single-Shot	PyTorch / Python	AdaRound FP8 (E4M3)	92.7 MB	83.02%	97.34%	GPU / Python lightweight
PlantDetect-Dense-BF16.safetensors	Single-Shot	PyTorch / Python	Full bfloat16	181.5 MB	83.02%	97.34%	Dense PyTorch reference
PlantDetect-Dense-4View-INT8.safetensors	4-View Distilled	PyTorch / Python	Standard W8A16 INT8	93.3 MB	82.08%	98.31%	Dedicated multi-photo mobile edge
PlantDetect-Dense-4View-FP8-AdaRound.safetensors	4-View Distilled	PyTorch / Python	AdaRound FP8 (E4M3)	92.7 MB	82.08%	98.31%	Dedicated multi-photo GPU edge
PlantDetect-Dense-4View-BF16.safetensors	4-View Distilled	PyTorch / Python	Full bfloat16	181.5 MB	82.08%	98.31%	Lossless 4-view dense reference
onnx/PlantDetect-Dense-INT8.onnx	Single-Shot	ONNX Runtime	Mixed-Precision INT8	106.3 MB	83.02%	97.34%	Android, iOS CoreML, Windows
onnx/PlantDetect-Dense-FP32.onnx	Single-Shot	ONNX Runtime	Full FP32	363.3 MB	83.02%	97.34%	Standard reference ONNX
onnx/PlantDetect-Dense-4View-INT8.onnx	4-View Distilled	ONNX Runtime	Mixed-Precision INT8	106.3 MB	82.08%	98.31%	Multi-photo ONNX Runtime
onnx/PlantDetect-Dense-4View-FP32.onnx	4-View Distilled	ONNX Runtime	Full FP32	363.3 MB	82.08%	98.31%	Multi-photo reference ONNX
litert/PlantDetect-Dense-INT8.tflite	Single-Shot	Google LiteRT (TFLite)	Full Integer INT8	387.0 MB	83.02%	97.34%	Samsung S24 NPU acceleration
litert/PlantDetect-Dense-FP16.tflite	Single-Shot	Google LiteRT (TFLite)	Float16	196.2 MB	83.02%	97.34%	Mobile GPU / XNNPACK CPU
litert/PlantDetect-Dense-4View-INT8.tflite	4-View Distilled	Google LiteRT (TFLite)	Full Integer INT8	385.8 MB	82.08%	98.31%	Samsung S24 Multi-View NPU
litert/PlantDetect-Dense-4View-FP16.tflite	4-View Distilled	Google LiteRT (TFLite)	Float16	195.8 MB	82.08%	98.31%	Multi-View Mobile GPU

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B. Server / Desktop GPU Models (Mixture-of-Experts)

Model File	Model Type	Architecture	Format	Size	Top-1 (1-View)	Top-1 (4-View)	Primary Use Case
PlantDetect-FP8-AdaRound.safetensors	Single-Shot	MoE++ (16 FFNs)	AdaRound FP8 (E4M3)	104.3 MB	89.31%	99.42%	Fastest, ultra-compact GPU server inference
PlantDetect-BF16.safetensors	Single-Shot	MoE++ (16 FFNs)	Full bfloat16	205.2 MB	89.31%	99.42%	Lossless baseline reference
PlantDetect-4View-FP8-AdaRound.safetensors	4-View Distilled	MoE++ 4-View	AdaRound FP8 (E4M3)	104.3 MB	85.15%	99.07%	Dedicated multi-photo teacher (FP8)
PlantDetect-4View-BF16.safetensors	4-View Distilled	MoE++ 4-View	Full bfloat16	205.2 MB	85.15%	99.07%	Lossless multi-photo baseline

Quantization Architecture Details:

• FP8 Models (*-FP8-AdaRound.safetensors): Quantized using AdaRound FP8 with quadratic activation reconstruction loss $\min |Wx - \tilde{W}(V)x|_2^2$ over non-uniform E4M3 discrete grids (100% Top-1 FP32 match).
• INT8 Models (*-INT8.safetensors / .onnx / .tflite): Quantized using Symmetric Per-Channel W8A16 Dynamic Range Quantization, delivering 43.81 dB SQNR and 0.999985 cosine similarity.

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3. 🖥️ Official Plant CLI Guide

1. Installation

git clone https://huggingface.co/thenukegun10x/wa-plant-identifier
cd wa-plant-identifier
pip install -r requirements.txt

2. Identify a Plant from a Single Photo

python plant_cli.py identify wild_flower.jpg --topk 5

3. Multi-View Botanical Identification (Flower + Leaf + Habit)

Achieves 97%–99% certainty by averaging logits across complementary plant angles:

python plant_cli.py identify flower.jpg leaf.jpg habit.jpg fruit.jpg

4. Batch Directory Identification

Recursively process an entire field folder and export results to CSV:

python plant_cli.py batch --dir field_photos/ --pattern "*.jpg" --recursive --out results.csv

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4. Python Quickstart Examples

Multi-View Inference with PyTorch / Safetensors

import torch
import numpy as np
from PIL import Image
from safetensors.torch import load_file
from src.models.plant_vit import PlantViT

# 1. Load model
model = PlantViT(stem_name="vit_base_patch16_dinov3", n_classes=999, use_moe=None)
sd = load_file("PlantDetect-Dense-BF16.safetensors")
model.load_state_dict(sd, strict=False)
model.eval()

# 2. Preprocess multiple photos of the same plant (e.g. Flower + Leaf)
def preprocess(path):
    img = Image.open(path).convert("RGB").resize((383, 383))
    crop = img.crop((23, 23, 359, 359))
    arr = (np.array(crop, dtype=np.float32) / 255.0 - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
    return torch.from_numpy(arr.transpose(2, 0, 1)).unsqueeze(0)

photos = [preprocess("flower.jpg"), preprocess("leaf.jpg")]

# 3. Compute mean logits across all views
with torch.no_grad():
    logits_list = [model(p)[0] for p in photos]
    mean_logits = torch.stack(logits_list).mean(dim=0)
    top_species_idx = mean_logits.argmax(dim=-1).item()

print(f"Identified Species Index: {top_species_idx}")

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Attribution & Licensing

• Built with DINOv3: Uses the DINOv3 vision backbone (vit_base_patch16_dinov3.lvd1689m) developed by Meta AI.
• Dataset Attribution: Observation data sourced from the Global Biodiversity Information Facility (GBIF) and Western Australian Herbarium (FloraBase / DBCA).
• License: Released under Creative Commons Non-Commercial (CC BY-NC 4.0) for scientific and non-commercial research.