zer0int/CLIP-Regression-BRUT-ViT-L-14

Regression CLIP - with strong typographic robustness!

• Fine-tuned using CLS-Patch Linear Regression teachers
• This model (BRUT): Even more robust to typographic attacks (but less generalization)
• Check the benchmarks below - or read the 📄 Latent Crossroads paper
• ➕
• New full-auto CLIP-fine-tune suite, (almost) config-free & super fast:
• Get the code: 👉 github.com/zer0int/CLIP-fine-tune
• Dataset heuristics (will infer dataset from local or HuggingFace automatically)
• Loads HuggingFace models, pickles, state dicts / local safetensors, ...
• Geometry analysis tools: get human-language answers to 'what went wrong', if it did

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📊 Standard Benchmark Evaluation

🌟 = This Model

Zero-Shot (Typographic Attack)

👉 CLICK to reproduce: Expand SCAM typographic attack benchmark code ⚡💻

from datasets import load_dataset
from transformers import CLIPModel, CLIPProcessor
import torch
from PIL import Image
from tqdm import tqdm
import pandas as pd

device = "cuda" if torch.cuda.is_available() else "cpu"

# BLISS / SCAM Typographic Attack Dataset
# https://huggingface.co/datasets/BLISS-e-V/SCAM
ds = load_dataset("BLISS-e-V/SCAM", split="train")

# Benchmark pre-trained model against my fine-tune
model_variants = [
    ("OpenAI ", "openai/clip-vit-large-patch14-336", "openai/clip-vit-large-patch14-336"),
    ("regr-norm", "zer0int/CLIP-Regression-ViT-L-14", "zer0int/CLIP-Regression-ViT-L-14"),
    ("regr-brut", "zer0int/CLIP-Regression-BRUT-ViT-L-14", "zer0int/CLIP-Regression-BRUT-ViT-L-14"),
]

models = {}
for name, model_path, processor_path in model_variants:
    model = CLIPModel.from_pretrained(model_path).to(device).float()
    processor = CLIPProcessor.from_pretrained(processor_path)
    models[name] = (model, processor)

for variant in ["NoSCAM", "SCAM", "SynthSCAM"]:
    print(f"\n=== Evaluating var.: {variant} ===")
    idxs = [i for i, v in enumerate(ds['id']) if v.startswith(variant)]
    if not idxs:
        print(f"  No samples for {variant}")
        continue
    subset = [ds[i] for i in idxs]

    for model_name, (model, processor) in models.items():
        results = []
        for entry in tqdm(subset, desc=f"{model_name}", ncols=30, bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} |"):
            img = entry['image']
            object_label = entry['object_label']
            attack_word = entry['attack_word']

            texts = [f"a photo of a {object_label}", f"a photo of a {attack_word}"]
            inputs = processor(
                text=texts,
                images=img,
                return_tensors="pt",
                padding=True
            )
            for k in inputs:
                if isinstance(inputs[k], torch.Tensor):
                    inputs[k] = inputs[k].to(device)

            with torch.no_grad():
                outputs = model(**inputs)
                image_features = outputs.image_embeds
                text_features = outputs.text_embeds

                logits = image_features @ text_features.T
                probs = logits.softmax(dim=-1).cpu().numpy().flatten()
                pred_idx = probs.argmax()
                pred_label = [object_label, attack_word][pred_idx]
                is_correct = (pred_label == object_label)

            results.append({
                "id": entry['id'],
                "object_label": object_label,
                "attack_word": attack_word,
                "pred_label": pred_label,
                "is_correct": is_correct,
                "type": entry['type'],
                "model": model_name
            })

        n_total = len(results)
        n_correct = sum(r['is_correct'] for r in results)
        acc = n_correct / n_total if n_total else float('nan')
        print(f"| > > > > Zero-shot accuracy for {variant}, {model_name}: {n_correct}/{n_total} = {acc:.4f}")

Zero-Shot (CLIP Benchmark)

Retrieval (CLIP Benchmark)

Retrieval (MSCOCO Captions, COCO 2014 val) — own scripts

Retrieval (SugarCrepe, COCO 2017 val) — own scripts

Linear Probe (ImageNet-1k) — own scripts

🔗 Note: 'own scripts' available at github.com/zer0int/CLIP-fine-tune

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🎯 Special Evaluation

Please see the paper for more information!

Zero-Shot Accuracy