README.md

---

language: en
license: apache-2.0
library_name: open_clip
tags:
- open-clip
- bioclip
- vision-language
- zero-shot-classification
- marine-species
- multimodal
- oceanclip
- oceangpt-x
---

# OceanCLIP-0.15B: Marine Vision-Language Model

A vision-language model fine-tuned on marine imagery and biological terminology using the OpenCLIP framework. Built upon [BioCLIP](https://github.com/Imageomics/bioclip), it is optimized for marine species identification, zero-shot classification, and cross-validation in underwater/sonar environments.

## 📂 Repository Contents
| Directory | File | Description |
|:---|:---|:---|
| `oceanclip-bio/` | `epoch_50.pt` | **Fine-tuned checkpoint**. Marine-adapted weights after 50 training epochs. Contains the updated vision & text encoder projections. |
| `oceanclip-bio/` | `terms.txt` | **Marine terminology list**. Line-by-line species names (e.g., `A abramis`). Used for zero-shot classification to dynamically build class-specific text prompts. |
| `bioclip/` | `open_clip_config.json` | **Architecture & preprocessing config**. Defines ViT-B/16 vision encoder, Transformer text encoder (77 context, 512 width), and image normalization (`mean`/`std`). |
| `bioclip/` | `open_clip_pytorch_model.bin` | **Base BioCLIP weights**. Original OpenCLIP-format pre-trained weights. Serves as the initialization backbone before marine-specific fine-tuning. |

## 🚀 Usage
Requires `open_clip_torch` and `torch`.
```python

import open_clip

import torch

from PIL import Image



# 1. Load architecture & base weights

model, _, preprocess = open_clip.create_model_and_transforms(

    model_name="ViT-B-16",

    pretrained="bioclip/open_clip_pytorch_model.bin"

)

tokenizer = open_clip.get_tokenizer("ViT-B-16")



# 2. Load fine-tuned marine weights

state_dict = torch.load("oceanclip-bio/epoch_50.pt", map_location="cpu")

model.load_state_dict(state_dict, strict=False)

model.eval()



# 3. Inference (Zero-Shot with terms.txt)

image = preprocess(Image.open("marine_input.jpg")).unsqueeze(0)

terms = [line.strip() for line in open("oceanclip-bio/terms.txt", "r") if line.strip()]

text_tokens = tokenizer(terms)



with torch.no_grad():

    image_feat = model.encode_image(image)

    text_feat = model.encode_text(text_tokens)

    logits = (image_feat @ text_feat.T).softmax(dim=-1)

    

    top_idx = logits.argmax().item()

    print(f"Predicted species: {terms[top_idx]} (Confidence: {logits[0, top_idx]:.4f})")