Huggingface's transformers library is a great resource for natural language processing tasks, and it includes an implementation of OpenAI's CLIP model including a pretrained model clip-vit-large-patch14. The CLIP model is a powerful image and text embedding model that can...
虽然Flamingo 不是开源的,但有许多 Flamingo 的开源复制品。 IDEFICS(HuggingFace) mlfoundations/open_flamingo 三、CLIP 与 Flamingo对比
对于第一个问题,使用现有的一些 image captioning 模型就行;对于第二个问题,最直接的想法就是 DDIM inversion. 然而,作者发现 DDIM inversion 在有 guidance 的情形下并不能较好地重建输入,因此作者希望找到更好的 inversion 方式。 摘自论文 如上图上半部分所示,设 DDIM inversion 过程为 z_0^\ast\to z_1^\...
Our live demo is available at https://huggingface.co/spaces/clip-italian/clip-italian-demo. What you will find in the demo: Text to Image: This task is essentially an image retrieval task. The user is asked to input a string of text and CLIP is going to compute the similarity between ...
Model cards with additional model specific details can be found on the Hugging Face Hub under the OpenCLIP library tag:https://huggingface.co/models?library=open_clip. If you found this repository useful, please considerciting. We welcome anyone to submit an issue or send an email if you ha...
from PIL import Image # Define a custom dataset class for Flickr30k class Flickr30kDataset(torch.utils.data.Dataset): def __init__(self): self.dataset = load_dataset("nlphuji/flickr30k", cache_dir="./huggingface_data") self.transform = transforms.Compose([ ...
CAPTION_MODELS定义了各个所需要的模型在huggingface 地址。CACHE_URL_BASE是缓存地址 Config class 首先定义了CLIP和BILP模型 代码语言:text 复制 caption_model = None caption_processor = None clip_model = None clip_preprocess = None 接下来对BLIP和CLIP进行了详细的设置2 ...
研究人员认为: LENS 的视觉能力严重依赖于其底层的视觉组件。这些模型的性能有进一步提升的空间,需要将它们的优势与 LLM 结合起来。 传送门: [1]https://huggingface.co/papers / 2306.16410(论文链接) [2]https://github.com/ContextualAI / lens(代码已开源)...
ClipCap: CLIP Prefix for Image CaptioningAbstractImage captioning is a fundamental task in vision-la… Grounding DINO检测一切 Ctrl CV keep learning 传统的目标检测一般指的是闭集检测,随着语言模型的发展,现在已经发展为了多模态开集检测。闭集检测 Transformer 方向最常用的算法是 DINO,基于 DINO 的改进有 4 ...
如上图所示,研究人员还绘制了除ImageNet之外的所有数据集的平均视觉性能图,并观察到: 更多样本有助于提高性能。同时,冻结LLM的性能与视觉性能之间没有直接关系,而更好的视觉主干有助于提高平均视觉性能。 对于视觉与语言任务,研究人员评估了四个具有代表性的视觉问答任务,并与需要进行额外预训练来对齐视觉和语言模态...