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Cataloged from BAAI/EVA-CLIP-8B
All models by BAAI βQuan Sun1*, Jinsheng Wang1*, Qiying Yu1,2*, Yufeng Cui1, Fan Zhang1, Xiaosong Zhang1, Xinlong Wang1
Scaling up contrastive language-image pretraining (CLIP) is critical for empowering both vision and multimodal models. We present EVA-CLIP-18B, the largest and most powerful open-source CLIP model to date, with 18-billion parameters. With only 6-billion training samples seen, EVA-CLIP-18B achieves an exceptional 80.7% zero-shot top-1 accuracy averaged across 27 widely recognized image classification benchmarks, outperforming its forerunner EVA-CLIP (5-billion parameters) and other open-source CLIP models by a large margin. Remarkably, we observe a consistent performance improvement with the model size scaling of EVA-CLIP, despite maintaining a constant training dataset of 2-billion image-text pairs from LAION-2B and COYO-700M. This dataset is openly available and much smaller than the in-house datasets (e.g., DFN-5B, WebLI-10B) employed in other state-of-the-art CLIP models. EVA-CLIP-18B demonstrates the potential of EVA-style weak-to-strong visual model scaling. With our model weights made publicly available, we hope to facilitate future research in vision and multimodal foundation models.
Table of Contents
[Image blocked: summary_tab]
Scaling behavior of EVA-CLIP with zero-shot classification performance averaged across 27 image classification benchmarks, compared with the current state-of-the-art and largest CLIP models (224px). The diameter of each circle demonstrates the forward GFLOPs Γ the number of training samples seen. The performance of EVA-CLIP consistently improves as scaling up.
Image encoder MIM teacher: EVA02_CLIP_E_psz14_plus_s9B.
Image encoder MIM teacher: EVA02_CLIP_E_psz14_plus_s9B.
It's important to note that all results presented in the paper are evaluated using PyTorch weights. There may be differences in performance when using Hugging Face (hf) models.
We use CLIP-Benchmark to evaluate the zero-shot performance of EVA-CLIP models. Following vissl, we evauate the zero-shot video classification using 1 middle frame. Further details regarding the evaluation datasets can be found in our paper, particularly in Table 11.
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You can leverage deepspeed.zero.Init() with deepspeed zero stage 3 if you have limited CPU memory. For loading a pretrained checkpoint in the context of using deepspeed.zero.Init(), it's advised to use the load_zero_partitions() function in eva_clip/factory.py.
from PIL import Imagefrom transformers import AutoModel, AutoConfigfrom transformers import CLIPImageProcessor, pipeline, CLIPTokenizerimport torchimport torchvision.transforms as Tfrom torchvision.transforms import InterpolationMode
image_path = "CLIP.png"model_name_or_path = "BAAI/EVA-CLIP-8B" # or /path/to/local/EVA-CLIP-8Bimage_size = 224
processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
# use image processor with conig# processor = CLIPImageProcessor(size={"shortest_edge":image_size}, do_center_crop=True, crop_size=image_size)
## you can also directly use the image processor by torchvision## squash# processor = T.Compose(# [# T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),# T.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC),# T.ToTensor(),# T.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))# ]# )## shortest## processor = T.Compose(# [# T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),# T.Resize(image_size, interpolation=InterpolationMode.BICUBIC),# T.CenterCrop(image_size),# T.ToTensor(),# T.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))# ]# )
model = AutoModel.from_pretrained( model_name_or_path, torch_dtype=torch.float16, trust_remote_code=True).to('cuda').eval()
image = Image.open(image_path)captions = ["a diagram", "a dog", "a cat"]tokenizer = CLIPTokenizer.from_pretrained(model_name_or_path)input_ids = tokenizer(captions, return_tensors="pt", padding=True).input_ids.to('cuda')input_pixels = processor(images=image, return_tensors="pt", padding=True).pixel_values.to('cuda')
with torch.no_grad(), torch.cuda.amp.autocast(): image_features = model.encode_image(input_pixels) text_features = model.encode_text(input_ids) image_features /= image_features.norm(dim=-1, keepdim=True) text_features /= text_features.norm(dim=-1, keepdim=True)
label_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)print(f"Label probs: {label_probs}")import torchfrom eva_clip import create_model_and_transforms, get_tokenizerfrom PIL import Image
model_name = "EVA-CLIP-8B" pretrained = "eva_clip" # or "/path/to/EVA_CLIP_8B_psz14_s9B.pt"
image_path = "CLIP.png"caption = ["a diagram", "a dog", "a cat"]
device = "cuda" if torch.cuda.is_available() else "cpu"model, _, processor = create_model_and_transforms(model_name, pretrained, force_custom_clip=True)tokenizer = get_tokenizer(model_name)model = model.to(device)
image = processor(Image.open(image_path)).unsqueeze(0).to(device)text = tokenizer(["a diagram", "a dog", "a cat"]).to(device)
with torch.no_grad(), torch.cuda.amp.autocast(): image_features = model.encode_image(image) text_features = model.encode_text(text) image_features /= image_features.norm(dim=-1, keepdim=True) text_features /= text_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print("Label probs:", text_probs)@article{EVA-CLIP-18B, title={EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters}, author={Quan Sun and Jinsheng Wang and Qiying Yu and Yufeng Cui and Fan Zhang and Xiaosong Zhang and Xinlong Wang}, journal={arXiv preprint arXiv:2402.04252}, year={2023}}