Module 1: Image Captioning with Generative AI
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In this post, I provide complete, accurate, and detailed explanations for the answers to Module 1: Image Captioning with Generative AI of Course 8: Building Generative AI-Powered Applications with Python – IBM AI Developer Professional Certificate .
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Module 1 Graded Quiz: Image Captioning with Generative AI
Graded Assignment
1. Which feature of large language models (LLMs) directly impacts their predictive accuracy?
- LLMs are pretrained using transformer-based models.
- LLMs are pretrained on unsupervised, unlabeled data.
- LLMs are pretrained on billions of data parameters. ✅
- LLMs are pretrained using convolutional networks.
Explanation:
The predictive accuracy of LLMs is largely influenced by the number of parameters and the scale of data they are trained on. Models with billions of parameters can capture more nuanced patterns, relationships, and contexts, which enhances their ability to make accurate predictions.
2. What is the primary purpose of the BLIP model in automated image captioning?
- To filter out inappropriate images from the dataset
- To generate textual descriptions of images based on their visual content ✅
- To enhance the color contrast of images for better caption generation
- To improve the resolution of input images before processing
Explanation:
BLIP (Bootstrapped Language Image Pretraining) is a vision-language model designed to generate natural language captions for images. It does this by analyzing visual features and mapping them to relevant language tokens.
3. Which feature of Gradio makes it particularly useful for machine learning practitioners wanting to demonstrate their models to a non-technical audience?
- Ability to increase the accuracy of machine learning models
- Requirement for extensive web hosting experience to share models
- Ease of integrating complex JavaScript and CSS for advanced web applications
- Capability to create user-friendly interfaces for models with just a few lines of code ✅
Explanation:
Gradio allows developers to quickly create web-based UIs for ML models without needing deep frontend knowledge. This makes it easy for non-technical users to interact with models.
4. Which of the following steps is essential to generate captions using the BLIP model with the Hugging Face Transformers library?
- Manually label each image before processing.
- Convert the image to black and white before loading it.
- Increase the contrast of the image to maximum before captioning.
- Load an image and prepare it to use the BLIP processor and model. ✅
Explanation:
To use BLIP in Hugging Face, you load the image, pass it through the BlipProcessor, and feed the processed data to the BLIP model for caption generation. You don’t need to manually alter the image (like converting it to grayscale).
5. Foundation generative AI models are distinct from other generative AI models because they _________.
- Perform only image classification tasks
- Exhibit broad capabilities that can be adapted to a range of different and specific tasks ✅
- Provide a predetermined response to queries
- Are trained on restricted domain data
Explanation:
Foundation models are large-scale pretrained models (like GPT or BERT) that can be fine-tuned or prompted for a wide variety of tasks. They are general-purpose and adaptable across domains.
6. Which of the following generative AI capabilities does Hugging Face offer?
- Image and video generation only
- Spreadsheet management
- Text generation only
- Text, images, audio, and video generation ✅
Explanation:
Hugging Face supports a multimodal ecosystem. It includes models for text (e.g., GPT), images (e.g., Stable Diffusion), audio (e.g., Whisper), and video. It’s a one-stop hub for generative AI development.
7. In the context of using Gradio and the BLIP model for image captioning, what is the primary role of the `BlipProcessor`?
- Generate alternative image captions for comparison.
- Prepare images for processing by standardizing format and size. ✅
- Adjust the contrast and brightness of images before processing manually
- Enhance the resolution of images for better model performance
Explanation:BlipProcessor is used to preprocess inputs (images + optional text) to make them suitable for the BLIP model. It standardizes the input (resizing, normalizing, etc.) just like tokenizers do for text.
Related contents:
Module 2: Create Your Own ChatGPT-Like Website
Module 3: Create a Voice Assistant
Module 4: Generative AI-Powered Meeting Assistant
Module 5: Summarize Your Private Data with Generative AI and RAG
Module 6: Babel Fish (Universal Language Translator) with LLM and STT TTS
Module 7: [Bonus] Module 7: Build an AI Career Coach
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