creative-graphic-design
/

LongCLIP-B

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 ---
+language: en
+license: mit
+tags:
+  - clip
+  - vision-language
+  - image-text
+  - zero-shot
+  - retrieval
+pipeline_tag: zero-shot-image-classification
 ---
+# LongCLIP: Unlocking the Long-Text Capability of CLIP
+[![Paper](https://img.shields.io/badge/arXiv-2403.15378-b31b1b)](https://arxiv.org/abs/2403.15378)
+[![Conference](https://img.shields.io/badge/ECCV-2024-blue)](https://eccv2024.ecva.net/)
+[![GitHub](https://img.shields.io/badge/GitHub-creative-graphic-design/longclip--transformers-black)](https://github.com/creative-graphic-design/longclip-transformers)
+## Model Description
+LongCLIP is an enhanced version of OpenAI's CLIP that extends the maximum input text length from **77 to 248 tokens**, enabling better understanding of detailed, long-form text descriptions. This model maintains CLIP's zero-shot capabilities while significantly improving performance on long-caption retrieval tasks.
+### Key Features
+- 🔥 **Extended Context Length**: 248 tokens (3.2× longer than original CLIP)
+- 🔥 **Strong Performance**: +20% R@5 on long-caption retrieval, +6% on standard retrieval
+- 🔥 **Plug-and-Play**: Drop-in replacement for CLIP in existing workflows
+- 🔥 **Two Model Sizes**: Base (LongCLIP-B) and Large (LongCLIP-L)
+### Model Variants
+| Model          | Text Encoder    | Vision Encoder   | Params | Projection Dim |
+| -------------- | --------------- | ---------------- | ------ | -------------- |
+| **LongCLIP-B** | 12 layers, 512d | 12 layers, 768d  | ~150M  | 512            |
+| **LongCLIP-L** | 12 layers, 768d | 24 layers, 1024d | ~430M  | 768            |
 ## Uses
 ### Direct Use
+LongCLIP can be used for:
+- **Zero-shot image classification** with detailed text descriptions
+- **Image-text retrieval** with long, descriptive captions
+- **Text-to-image generation** (e.g., Stable Diffusion XL integration)
+- **Visual question answering** with complex queries
+### Downstream Use
+LongCLIP serves as a backbone for:
+- Vision-language models requiring long text understanding
+- Multimodal retrieval systems
+- Content-based image search engines
+- Automated image captioning evaluation
+## How to Use
+### Installation
+```bash
+pip install "transformers[torch,torch-vision]"
+```
+### Quick Start
+```python
+from transformers import AutoModel, AutoProcessor
+from PIL import Image
+import torch
+# Load model and processor
+model = AutoModel.from_pretrained(
+    "creative-graphic-design/LongCLIP-B",
+    trust_remote_code=True
+)
+processor = AutoProcessor.from_pretrained(
+    "creative-graphic-design/LongCLIP-B",
+    trust_remote_code=True
+)
+# Prepare inputs
+image = Image.open("your_image.jpg")
+texts = [
+    "A man is crossing the street with a red car parked nearby.",
+    "A man is driving a car in an urban scene."
+]
+inputs = processor(
+    text=texts,
+    images=image,
+    return_tensors="pt",
+    max_length=248,
+    padding="max_length"
+)
+# Get predictions
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits_per_image = outputs.logits_per_image
+    probs = logits_per_image.softmax(dim=-1)
+print("Probabilities:", probs)
+```
+### Advanced Usage: Feature Extraction
+```python
+# Extract features separately (unnormalized)
+text_inputs = processor(text=texts, return_tensors="pt", max_length=248, padding="max_length")
+image_inputs = processor(images=image, return_tensors="pt")
+with torch.no_grad():
+    text_features = model.get_text_features(**text_inputs)
+    image_features = model.get_image_features(**image_inputs)
+    # Compute similarity (like original CLIP)
+    logits = image_features @ text_features.T
+    probs = logits.softmax(dim=-1)
+```
+### Comparison with Original CLIP
+```python
+# Original CLIP: max 77 tokens
+clip_text = "A cat"
+# LongCLIP: up to 248 tokens
+longclip_text = "A fluffy orange tabby cat with green eyes is sitting on a wooden table near a window, with sunlight streaming through the curtains in the background, creating a warm and cozy atmosphere in a modern living room."
+# LongCLIP can handle both short and long texts effectively!
+```
+## Citation
+If you use LongCLIP in your research, please cite:
+```bibtex
+@inproceedings{zhang2024longclip,
+  title={Long-CLIP: Unlocking the Long-Text Capability of CLIP},
+  author={Zhang, Beichen and Zhang, Pan and Dong, Xiaoyi and Zang, Yuhang and Wang, Jiaqi},
+  booktitle={European Conference on Computer Vision (ECCV)},
+  year={2024}
+}
+```
+## License
+This model is released under the MIT License, consistent with the original CLIP model.
+## Acknowledgments
+- **OpenAI CLIP**: Foundation model and architecture
+- **Original Authors**: Beichen Zhang, Pan Zhang, Xiaoyi Dong, Yuhang Zang, Jiaqi Wang
 ## Model Card Contact
+For questions and feedback, please open an issue on the [GitHub repository](https://github.com/creative-graphic-design/longclip-transformers).