---
license: apache-2.0
datasets:
- juppy44/gbif-plants-raw
language:
- en
base_model:
- google/vit-base-patch16-224
pipeline_tag: image-classification
tags:
- biology
- ecology
- environmental
- GBIF
- iNaturalist
- Plant
- species
---

## Highlights

* **Base model:** `google/vit-base-patch16-224`
* **Parameters:** ~97.2M
* **Training samples:** 2,000,000 curated plant occurrences
* **Species coverage:** ~14,000 unique species
* **Source data:** GBIF (research-grade iNaturalist images)
* **Training method:** End-to-end supervised fine-tuning
* **Use-case:** Fast species-level classification from a single photo

---

## Example Usage

```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
import torch

model_id = "juppy44/plant-identification-2m-vit-b"

processor = AutoImageProcessor.from_pretrained(model_id)
model = AutoModelForImageClassification.from_pretrained(model_id)

url = "https://example.com/plant.jpg"
image = Image.open(requests.get(url, stream=True).raw)

inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

pred = logits.softmax(dim=-1)[0]
topk = torch.topk(pred, k=5)

for prob, idx in zip(topk.values, topk.indices):
    label = model.config.id2label[idx.item()]
    print(f"{label}: {prob.item():.4f}")
```

---

## Intended Applications

* Ecological surveys
* Nursery and horticulture tools
* Restoration and revegetation workflows
* Field research and biodiversity monitoring
* Citizen science and educational platforms
* Image-based species tagging pipelines

---

## Data & Training Details

### Dataset Construction

* Sourced from **GBIF** occurrences with valid `species` and image metadata.
* Cleaned and deduplicated.
* Species filtered to those with **≥ 20 images**.
* Maximum cap of **1,000 images per species** to reduce class imbalance.
* Final training dataset: **2,000,000 images** across **~14k species**.

### Training

* ViT-Base fine-tuned for 1 epoch over 2M samples.
* AdamW optimizer, standard ViT augmentations.
* Mixed-precision training on GPU.

### Limitations

* Some species are visually indistinguishable without context (location, reproduction structures, etc.).
* Performance varies for rare, morphologically similar, or poorly photographed species.
* No location metadata incorporated yet — purely image-based.

---

## Labels

Species names follow the canonical GBIF taxonomy (`species_name`).
Each class corresponds directly to a species.

You can inspect all labels via:

```python
from transformers import AutoConfig
cfg = AutoConfig.from_pretrained("juppy44/plant-identification-2m-vit-b")
labels = cfg.id2label
```

---

## Performance

No fixed metric is published yet due to absence of a clean evaluation split in GBIF/iNat.
However, the model performs strongly in practical testing and is suitable for downstream adaptation or LoRA-based fine-tuning.

Formal benchmarks will be added when a standardized evaluation subset is released.

---

## Fine-Tuning & Adapters

You can further specialize the model using LoRA adapters for:

* Regional subsets
* Functional groups
* Threatened species
* Agricultural crops
* Disease classification

The base model is trained broadly enough to support domain-specific adapter tuning with minimal compute.

---

## License

Model weights follow the same license as the underlying ViT-Base model.
Users are responsible for ensuring compliance with GBIF/iNaturalist usage terms for any downstream dataset creation.

---