WebCOCO is a large-scale object detection, segmentation, and captioning dataset. Detailed information (Images): ⇒ [ Paper] [ Website ] Number of different categories: 91 Number of images: 120k ( Training: 80k. Testing: 40k.) Detailed information (Text Descriptions): ⇒ [ Paper] [ Download ] Descriptions per image: 5 Captions Multi-Modal-CelebA-HQ WebCOCO Captions contains over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated …
Generating Diverse and Meaningful Captions SpringerLink
WebThe model and the tuning of its hyperparamaters are based on ideas presented in the paper Show and Tell: A Neural Image Caption Generator and Show, Attend and Tell: Neural … WebDownload scientific diagram Examples of out-of-domain captions generated on MS COCO using our base model (Base), and our base model guided by four tag predictions (Base + LC4). Novel objects ... pubs with accommodation bath
GitHub - karpathy/neuraltalk2: Efficient Image Captioning code in …
Web4 Nov 2024 · Let’s Build our Image Caption Generator! Step 1:- Import the required libraries Here we will be making use of the Keras library for creating our model and training it. You can make use of Google Colab or Kaggle notebooks if you want a GPU to train it. Web23 Jul 2024 · Automatic caption generation example: For this project, I use the Microsoft Common Objects in COntext (MS COCO) dataset. It is a large-scale dataset for scene understanding. The dataset is commonly used to train and benchmark object detection, segmentation, and captioning algorithms. Step 1 - Initialize the COCO API Web12 Mar 2024 · Just two years ago, text generation models were so unreliable that you needed to generate hundreds of samples in hopes of finding even one plausible sentence. Nowadays, OpenAI’s pre-trained language model can generate relatively coherent news articles given only two sentence of context. Other approaches like Generative Adversarial … seat images