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> [!TIP] > LLaVA-NeXT and transformers now supports MLCD-ViT-bigG-14-448px. > >
Modellquelle
Quellenbeschreibung
[!TIP] LLaVA-NeXT and transformers now supports MLCD-ViT-bigG-14-448px.
MLCD-ViT-bigG is a state-of-the-art vision transformer model enhanced with 2D Rotary Position Embedding (RoPE2D), achieving superior performance on document understanding and visual question answering tasks. Developed by DeepGlint AI, this model demonstrates exceptional capabilities in processing complex visual-language interactions.
Quellen
1 QuelleVerifiziert 9. Sept.
Modellartefakte
2 Artefaktemodel-00001-of-00002.safetensors
safetensors · 4,62 GB · SHA-256 572fbbeb8b95…256f · Hugging Face
Herunterladenmodel-00002-of-00002.safetensors
safetensors · 2,25 GB · SHA-256 55945c7441a4…1f01 · Hugging Face
HerunterladenQuellenauszüge
2 AuszügeWe adopted the official LLaVA-NeXT and the official training dataset LLaVA-NeXT-Data for evaluating the foundational visual models.
The language model is Qwen2.5-7B.
| Vision Tower | RoPE2D | ChartQA | DocVQA | InfoVQA | OCRBench | MMMU |
|---|---|---|---|---|---|---|
| CLIP (ViT-L-14-336px) | × | 66.52 | 75.21 | 38.88 | 525.00 | 44.20 |
| SigLIP (ViT-SO400M-384px) | × | 69.28 | 76.71 | 41.38 | 554.00 | 46.78 |
| DFN5B (ViT-H-14-378px) | × | 64.36 | 70.87 | 38.59 | 473.00 | 48.00 |
| MLCD (ViT-L-14-336px) | × |
pip install torch transformers
git clone https://github.com/deepglint/unicom
cd unicom/mlcd
from vit_rope2d_hf import MLCDVisionModel
from transformers import CLIPImageProcessor
from PIL import Image
import requests
import torch
# Load model and processor
model = MLCDVisionModel.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
processor = CLIPImageProcessor.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
# Process single image
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt")
# Get visual features
with torch.no_grad():
outputs = model(**inputs)
features = outputs.last_hidden_state
print(f"Extracted features shape: {features.shape}")
@inproceedings{anxiang_2024_mlcd,
title={Multi-label Cluster Discrimination for Visual Representation Learning},
author={An, Xiang and Yang, Kaicheng and Dai, Xiangzi and Feng, Ziyong and Deng, Jiankang},
booktitle={ECCV},
year={2024}
}
--- license: mit pipeline_tag: image-feature-extraction --- ## MLCD-ViT-bigG Model Card > [!TIP] > **[LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT) and [transformers](https://github.com/huggingface/transformers/releases/tag/v4.51.3-MLCD-preview) now supports MLCD-ViT-bigG-14-448px.** > > MLCD-ViT-bigG is a state-of-the-art vision transformer model enhanced with 2D Rotary Position Embedding (RoPE2D), achieving superior performance on document understanding and visual question answering tasks. Developed by DeepGlint AI, this model demonstrates exceptional capabilities in processing complex visual-language interactions. We adopted the official [LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT) and the official training dataset [LLaVA-NeXT-Data](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Data) for evaluating the foundational visual models. The language model is Qwen2.5-7B. | Vision Tower | RoPE2D | ChartQA | DocVQA | InfoVQA | OCRBench | MMMU | | :-------------------------------------------------------------------------------------------- | :----: | :-------- | :-------- | :-------- | :--------- | :-------- | | CLIP (ViT-L-14-336px) | × | 66.52 | 75.21 | 38.88 | 525.00 | 44.20 | | SigLIP (ViT-SO400M-384px) | × | 69.28 | 76.71 | 41.38 | 554.00 | 46.78 | | DFN5B (ViT-H-14-378px) | × | 64.36 | 70.87 | 38.59 | 473.00 | **48.00** | | **[MLCD (ViT-L-14-336px)](https://huggingface.co/DeepGlint-AI/mlcd-vit-large-patch14-336)** | × | 67.84 | 76.46 | 43.48 | 531.00 | 44.30 | | **[MLCD (ViT-bigG-14-336px)](https://huggingface.co/DeepGlint-AI/mlcd-vit-bigG-patch14-336)** | √ | 71.07 | 79.63 | 44.38 | 572.00 | 46.78 | | **[MLCD (ViT-bigG-14-448px)](https://huggingface.co/DeepGlint-AI/mlcd-vit-bigG-patch14-448)** | √ | **73.80** | **83.34** | **46.59** | **582.00** | 46.00 | ## Installation ```shell pip install torch transformers git clone https://github.com/deepglint/unicom cd unicom/mlcd ``` ## Usage ```python from vit_rope2d_hf import MLCDVisionModel from transformers import CLIPImageProcessor from PIL import Image import requests import torch # Load model and processor model = MLCDVisionModel.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448") processor = CLIPImageProcessor.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448") # Process single image url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(re...
Source context: 673 downloads · 4 likes · Pipeline image-feature-extraction · Repo DeepGlint-AI/mlcd-vit-bigG-patch14-448
| 67.84 |
| 76.46 |
| 43.48 |
| 531.00 |
| 44.30 |
| MLCD (ViT-bigG-14-336px) | √ | 71.07 | 79.63 | 44.38 | 572.00 | 46.78 |
| MLCD (ViT-bigG-14-448px) | √ | 73.80 | 83.34 | 46.59 | 582.00 | 46.00 |