vit_large_384_for_trees

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

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])