BioCLIP 2.5 ViT-H — ReefNet LoRA (species, 92-class global)

Artifacts

• open_clip_model.safetensors — the merged full model. Base BioCLIP 2.5 with the LoRA adapters folded into the weights and restored to the original nn.MultiheadAttention layout. Load as a plain CLIP, no PEFT needed:
  python

  1import open_clip 2model, _, preprocess = open_clip.create_model_and_transforms( 3 "hf-hub:BobDerBaum/bioclip-2.5-vith14-reefnet-lora" 4) 5tokenizer = open_clip.get_tokenizer("hf-hub:BobDerBaum/bioclip-2.5-vith14-reefnet-lora")
• adapter.safetensors + adapter_config.json — the raw PEFT LoRA adapters (vision encoder only, 3.6% trainable) plus the trained logit_scale. Load onto the base model with PEFT (see adapter_config.json for target_modules).

Training

• Backbone: imageomics/bioclip-2.5-vith14 (frozen), LoRA on Q/K/V/O of attention and c_fc/c_proj of MLP of the vision encoder only (the text encoder was not adapted — text-encoder PEFT was not yet implemented at this run).
• Rank 32, alpha 64, dropout 0.1; AdamW lr 1e-4, weight decay 0.01, focal loss (gamma 2.0) with class-frequency weights; 5% linear warmup + cosine annealing.
• 10 epochs (early stopped at epoch 5, best val_acc=0.8865).

Metrics (run 62yuzh9j)

Splits / Data