数据处理部分主要有两个模块,captioning(用于生成给定图像的文字描述)和filtering(用于去除噪声图像文本对),两者均以MED进行初始化,并在数据集COCO上微调。最后合并两者的数据集,以新的数据集预训练一个新的模型。 3.3 OFA 论文:OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Seque...
blip-image-captioning-bas是一个用1400W参数训练出来的模型,该模型在huggingface的大小有990M,有两种方式使用该模型,一种是通过API调用的方式,前提是必须在云环境中事先部署好该模型的应用服务,然后提供api key和 Inference Endpoint来供调用,这种方式不占用本地存储空间资源,但会占用网络资源,第二种方式是将blip-...
The image captioning model is implemented using the PyTorch framework and leverages the Hugging Face Transformers library . - GitHub - luv-bansal/Image-Captioning-HuggingFace: The image captioning model is implemented using the PyTorch framework and leve
HuggingFace Demo:https://huggingface.co/spaces/MAGAer13/mPLUG-Owl Youku-mPLUG数据集：ModelScope ...
Caption-Anything is a versatile tool combining image segmentation, visual captioning, and ChatGPT, generating tailored captions with diverse controls for user preferences.https://huggingface.co/spaces/TencentARC/Caption-Anythinghttps://huggingface.co/spaces/VIPLab/Caption-Anything ...
我不看好将visual生硬转成raw text的做法，这样简单，但未必合理，缺陷之一就是引入了captioning model的...
3. After clicking on an image an asynchronous request will be sent to a HuggingFaceSalesforce/blip-image-captioning-baseImageToText model to process and generate a description of the image, it may take a few seconds. 4. Since HuggingFace with its inference API creates a common interface for ...
[LLM && AIGC] visual chatgpt 01 认识 image captioning 及 blip model 五道口纳什 2478 -- 2:44 Meta开源比chatgpt4更强的六模态imagebind模型 NsLearning- 6439 1 1:44 20240524-gpt-4o-GUI-image-file(gpt-4o API的多轮对话GUI,添加图片和文档) blpcx 67 -- 4:21 GPT-4免费用,微软Copilot...
If you want to download the model fromHugging Facedirectly, you can set theoption.model_idparameter in theserving.propertiesfile as the model id of a pre-trained model hosted inside a model repository onhuggingface.co. The container uses this model id to...
out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # unconditional image captioning # inputs = processor(raw_image, return_tensors="pt").to("cuda") # out = model.generate(**inputs) # print(processor.decode(out[0], skip_special_tokens=True)) ...