ViTamin: Designing Scalable Vision Models in the Vision-language Era
🔥 Officially supported by timm and OpenCLIP. Thanks @rwightman!
One line of code to call ViTamin:
model = timm.create_model('vitamin_xlarge_384')ViTamin-XL, with only 436M parameters and trained on the public DataComp-1B dataset, achieves an impressive 82.9% zero-shot ImageNet accuracy.
ViTamin-L sets a new SOTA across seven benchmarks for open-vocabulary segmentation, and also push forward the capabilities of large multi-modal models (e.g., LLaVA) significantly.
🤗 The HuggingFace collection of ViTamin model cards has been released! Check out the model cards!
Get Started
It currently includes code and models for the following tasks:
ViTamin Pre-training: See ./ViTamin/README.md for a quick start, which includes CLIP pre-training / fine-tuning pipelines and zero-shot evaluation pipelines.
Open-vocabulary Detection and Segmentation: See ViTamin for Open-vocab Detection and ViTamin for Open-vocab Segmentation.
Large Multi-Modal Models: See ViTamin for Large Multi-Modal Models.
We also support ViTamin with Hugging Face model jienengchen/ViTamin-XL-384px.
import torch
import open_clip
from PIL import Image
from transformers import AutoModel, CLIPImageProcessor
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModel.from_pretrained(
'jienengchen/ViTamin-XL-384px',
trust_remote_code=True).to(device).eval()
image = Image.open('./image.png').convert('RGB')
image_processor = CLIPImageProcessor.from_pretrained('jienengchen/ViTamin-XL-384px')
pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
pixel_values = pixel_values.to(torch.bfloat16).cuda()
tokenizer = open_clip.get_tokenizer('hf-hub:laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K')
text = tokenizer(["a photo of vitamin", "a dog", "a cat"]).to(device)
with torch.no_grad(), torch.cuda.amp.autocast():
image_features, text_features, logit_scale = model(pixel_values, text)
text_probs = (100.0 * image_features @ text_features.to(torch.float).T).softmax(dim=-1)
print("Label probs:", text_probs)
Main Results with CLIP Pre-training on DataComp-1B
We will provide 61 trained VLMs (48 benchmarked + 13 best performing) in Hugging Face for community use. Stay tuned!
| image encoder | 🤗 HuggingFace | image size | num patches | text encoder depth/width | seen samples (B) | trainable params Image+Text (M) | MACs Image+Text (G) | ImageNet Acc. | avg. 38 datasets | ImageNet dist. shift. | VTAB | retrieval |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ViTamin-L | Link | 224 | 196 | 12/768 | 12.8 | 333.3+123.7 | 72.6+6.6 | 80.8 | 66.7 | 69.8 | 65.3 | 60.3 |
| ViTamin-L | Link | 256 | 256 | 12/768 | 12.8+0.2 | 333.4+123.7 | 94.8+6.6 | 81.2 | 67.0 | 71.1 | 65.3 | 61.2 |
| ViTamin-L | Link | 336 | 441 | 12/768 | 12.8+0.2 | 333.6+123.7 | 163.4+6.6 | 81.6 | 67.0 | 72.1 | 64.4 | 61.6 |
| ViTamin-L | Link | 384 | 576 | 12/768 | 12.8+0.2 | 333.7+123.7 | 213.4+6.6 | 81.8 | 67.2 | 72.4 | 64.7 | 61.8 |
| ViTamin-L2 | Link | 224 | 196 | 24/1024 | 12.8 | 333.6+354.0 | 72.6+23.3 | 80.9 | 66.4 | 70.6 | 63.4 | 61.5 |
| ViTamin-L2 | Link | 256 | 256 | 24/1024 | 12.8+0.5 | 333.6+354.0 | 94.8+23.3 | 81.5 | 67.4 | 71.9 | 64.1 | 63.1 |
| ViTamin-L2 | Link | 336 | 441 | 24/1024 | 12.8+0.5 | 333.8+354.0 | 163.4+23.3 | 81.8 | 67.8 | 73.0 | 64.5 | 63.6 |
| ViTamin-L2 | Link | 384 | 576 | 24/1024 | 12.8+0.5 | 334.0+354.0 | 213.4+23.3 | 82.1 | 68.1 | 73.4 | 64.8 | 63.7 |
| ViTamin-XL | Link | 256 | 256 | 27/1152 | 12.8+0.5 | 436.1+488.7 | 125.3+33.1 | 82.1 | 67.6 | 72.3 | 65.4 | 62.7 |
| ViTamin-XL | Link | 384 | 576 | 27/1152 | 12.8+0.5 | 436.1+488.7 | 281.9+33.1 | 82.6 | 68.1 | 73.6 | 65.6 | 63.8 |
| ViTamin-XL | Link | 256 | 256 | 27/1152 | 40 | 436.1+488.7 | 125.3+33.1 | 82.3 | 67.5 | 72.8 | 64.0 | 62.1 |
| ViTamin-XL | Link | 336 | 441 | 27/1152 | 40+1 | 436.1+488.7 | 215.9+33.1 | 82.7 | 68.0 | 73.9 | 64.1 | 62.6 |
| ViTamin-XL | Link | 384 | 576 | 27/1152 | 40+1 | 436.1+488.7 | 281.9+33.1 | 82.9 | 68.1 | 74.1 | 64.0 | 62.5 |
Main Results on Downstream tasks
| Open-Vocab Detection | image encoder | detector | OV-COCO (AP50novel) | OV-LVIS (APr) |
|---|---|---|---|---|
| ViT-L/14 | Sliding F-ViT | 36.1 | 32.5 | |
| ViTamin-L | Sliding F-ViT | 37.5 | 35.6 |
Open-Vocab Segmentation
| image encoder | segmentor | ADE | Cityscapes | MV | A-150 | A-847 | PC-459 | PC-59 | PAS-21 |
|---|---|---|---|---|---|---|---|---|---|
| ViT-L/14 | Sliding FC-CLIP | 24.6 | 40.7 | 16.5 | 31.8 | 14.3 | 18.3 | 55.1 | 81.5 |
| ViTamin-L | Sliding FC-CLIP | 27.3 | 44.0 | 18.2 | 35.6 | 16.1 | 20.4 | 58.4 | 83.4 |
Note: Panoptic dataset (ADE, CityScapes, MV) are with the metric of PQ. Semantic dataset (A-150, A-847, PC-459, PC-59, PAS-21) are with the metric of mIoU.
Large Multi-modal Models
| image encoder | image size | VQAv2 | GQA | VizWiz | SQA | T-VQA | POPE | MME | MM-Bench | MM-B-CN | SEED | LLaVA-Wild | MM-Vet |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ViTamin-L | 336 | 78.4 | 61.6 | 51.1 | 66.9 | 58.7 | 84.6 | 1421 | 65.4 | 58.4 | 57.7 | 64.5 | 33.6 |
| ViTamin-L | 384 | 78.9 | 61.6 | 55.4 | 67.6 | 59.8 | 85.5 | 1447 | 64.5 | 58.3 | 57.9 | 66.1 | 33.6 |
Citing ViTamin
@inproceedings{chen2024vitamin,
title={ViTamin: Designing Scalable Vision Models in the Vision-language Era},
author={Chen, Jieneng and Yu, Qihang and Shen, Xiaohui and Yuille, Alan and Chen, Liang-Chieh},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2024}
}