---
license: mit
base_model:
- google/vit-large-patch16-384
pipeline_tag: image-classification
library_name: timm
tags:
- biology
---
# rexologue/vit_large_384_for_trees
This repository hosts a fine-tuned `vit_large_patch16_384` classifier 

## Labels

- abies_sibirica
- acer_campestre
- acer_ginnala
- acer_negundo
- acer_platanoides
- acer_pseudoplatanus
- acer_tataricum
- aesculus_hippocastanum
- alnus_alnobetula_fruticosa
- alnus_glutinosa
- alnus_incana
- arctostaphylos_uva-ursi
- berberis_vulgaris
- betula_nana
- betula_pendula
- betula_pubescens
- calluna_vulgaris
- cornus_alba
- cornus_mas
- cornus_sanguinea
- cornus_suecica
- cotoneaster_lucidus
- cotoneaster_melanocarpus
- daphne_mezereum
- elaeagnus_angustifolia
- euonymus_europaeus
- euonymus_verrucosus
- fraxinus_excelsior
- fraxinus_pennsylvanica
- genista_tinctoria
- hippophae_rhamnoides
- hypericum_maculatum
- hypericum_perforatum
- juglans_mandshurica
- juniperus_communis
- larix_sibirica
- ligustrum_vulgare
- lonicera_caerulea
- lonicera_nigra
- lonicera_tatarica
- lonicera_xylosteum
- physocarpus_opulifolius
- picea_abies
- picea_obovata
- pinus_sibirica
- pinus_sylvestris
- populus
- populus_alba
- populus_nigra
- populus_tremula
- potentilla_argentea
- potentilla_erecta
- potentilla_intermedia
- potentilla_norvegica
- potentilla_paradoxa
- potentilla_reptans
- potentilla_supina
- quercus_robur
- ribes_nigrum
- ribes_rubrum
- ribes_uva-crispa
- rosa_acicularis
- rosa_majalis
- rosa_rugosa
- rubus_arcticus
- rubus_caesius
- rubus_chamaemorus
- rubus_idaeus
- rubus_nessensis
- rubus_saxatilis
- salix_alba
- salix_caprea
- salix_cinerea
- salix_gmelinii
- salix_myrsinifolia
- salix_pentandra
- salix_triandra
- salix_viminalis
- sorbaria_sorbifolia
- sorbus_aucuparia
- spiraea_salicifolia
- symphoricarpos_albus
- tilia_cordata
- ulmus_glabra
- ulmus_laevis
- ulmus_pumila
- vaccinium_myrtillus
- vaccinium_oxycoccos
- vaccinium_uliginosum
- vaccinium_vitis-idaea
- viburnum_lantana
- viburnum_opulus

## Usage

```python
import json, torch, timm
from huggingface_hub import hf_hub_download
from timm.data.transforms_factory import create_transform
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from PIL import Image

REPO = "rexologue/vit_large_384_for_trees"
MODEL_NAME = "vit_large_patch16_384"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# 1) labels
labels_path = hf_hub_download(REPO, filename="labels.json")
with open(labels_path, "r", encoding="utf-8") as f:
    raw = json.load(f)
labels = [raw[str(i)] for i in range(len(raw))] if isinstance(raw, dict) else list(raw)

# 2) weights
ckpt_path = hf_hub_download(REPO, filename="pytorch_model.bin")
state = torch.load(ckpt_path, map_location="cpu")
if any(k.startswith("module.") for k in state):  # DDP fix
    state = {k.replace("module.", "", 1): v for k, v in state.items()}

# 3) model
model = timm.create_model(MODEL_NAME, num_classes=len(labels), pretrained=False)
model.load_state_dict(state, strict=True)
model.to(DEVICE).eval()

# 4) preprocessing (ViT-L/16 @ 384 w/ ImageNet mean/std + bicubic)
transform = create_transform(
    input_size=(3, 384, 384),
    interpolation="bicubic",
    mean=IMAGENET_DEFAULT_MEAN,
    std=IMAGENET_DEFAULT_STD,
)

# 5) run
img = Image.open("your_image.jpg").convert("RGB")
x = transform(img).unsqueeze(0).to(DEVICE)
with torch.no_grad():
    logits = model(x)
probs = torch.softmax(logits, dim=1)[0].cpu()
topk = probs.topk(k=min(5, len(labels)))
print([(labels[i], float(probs[i])) for i in topk.indices])
```