Vision-language models (GPT-4V) in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a GPT-4V vision-language model.

Prompt Engineering / GenAI

model = GPT4VModel.from_pretrained([1])

Drag options to blanks, or click blank then click option'

A"gpt4v-text"

B"gpt4v-base"

C"gpt4v-vision"

D"gpt4v-audio"

Attempts:

3 left

💡 Hint

Common Mistakes

Using a text-only model name instead of the vision-language model.

Confusing audio or base models with vision-language models.

The correct model identifier for the GPT-4V vision-language model is "gpt4v-vision".

2fill in blank

medium

Complete the code to preprocess an image for GPT-4V input.

Prompt Engineering / GenAI

processed_image = processor.preprocess([1])

Drag options to blanks, or click blank then click option'

Araw_text

Bimage_path

Caudio_clip

Dvideo_frame

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw text or audio instead of an image.

Using a video frame variable without conversion.

Images must be loaded from a path or image object before preprocessing for GPT-4V.

3fill in blank

hard

Fix the error in the code to generate a caption from an image using GPT-4V.

Prompt Engineering / GenAI

outputs = model.generate([1])

Drag options to blanks, or click blank then click option'

Aaudio_input

Braw_text

Cvideo_input

Dprocessed_image

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw text or audio instead of processed image tensor.

Using unprocessed image data.

The model's generate method requires the processed image tensor as input, not raw text or other media.

4fill in blank

hard

Fill both blanks to create a dictionary of images and their features.

Prompt Engineering / GenAI

features = {img: [1] for img in images if img [2] None}

Drag options to blanks, or click blank then click option'

Amodel.extract_features(img)

Bmodel.generate_caption(img)

Cis not

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of 'is not' to check for None.

Calling generate_caption instead of extract_features.

We extract features from each image that is not None using model.extract_features and check with 'is not'.

5fill in blank

hard

Fill all three blanks to filter images and generate captions for valid inputs.

Prompt Engineering / GenAI

captions = [model.generate_caption([1]) for [2] in images if [3] is not None]

Drag options to blanks, or click blank then click option'

Aimg

Bimage

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing variable names inconsistently.

Checking the wrong variable for None.

We iterate over images using 'image' as the variable, generate captions for each 'image', and check 'image is not None'.

Practice

1. What is the main capability of vision-language models like GPT-4V?

easy

A. They understand and generate responses based on both images and text.

B. They only process text data without images.

C. They only analyze images without any text understanding.

D. They translate languages without any image input.

Solution

1. Step 1: Understand the model's input types

   Vision-language models take both images and text as input to understand context.

2. Step 2: Recognize the model's output capabilities

   They generate responses that relate to both the visual content and the text prompt.

3. Final Answer:

   They understand and generate responses based on both images and text. -> Option A

4. Quick Check:

   Vision + Language = Both inputs [OK]

Hint: Vision-language means both image and text understanding [OK]

Common Mistakes:

• Thinking the model only works with text
• Assuming it only processes images
• Confusing translation with vision-language tasks

2. Which of the following is the correct way to prompt GPT-4V to describe an image?

easy

A. Translate the text: [image]

B. Describe the image: [image]

C. Calculate the sum: [image]

D. Play music from: [image]

Solution

1. Step 1: Identify the prompt that asks for image description

   Only Describe the image: [image] clearly requests a description of the image content.

2. Step 2: Eliminate unrelated commands

   Options B, C, and D ask for translation, calculation, or music playing, which are unrelated to image description.

3. Final Answer:

   <code>Describe the image: [image]</code> -> Option B

4. Quick Check:

   Prompt matches task: describe image [OK]

Hint: Look for prompt asking to describe the image [OK]

Common Mistakes:

• Choosing prompts unrelated to images
• Confusing translation with description
• Ignoring the image context in the prompt

3. Given the following code snippet using GPT-4V API, what will be the output?

response = gpt4v.ask(image='cat.jpg', prompt='What animal is in the picture?')
print(response)

medium

A. SyntaxError: missing argument

B. "I cannot see any animal in the picture."

C. "The animal in the picture is a cat."

D. "The picture shows a dog."

Solution

1. Step 1: Understand the prompt and image input

   The prompt asks what animal is in the image named 'cat.jpg', which likely contains a cat.

2. Step 2: Predict the model's response

   GPT-4V will analyze the image and respond with the correct animal, which is a cat.

3. Final Answer:

   "The animal in the picture is a cat." -> Option C

4. Quick Check:

   Image name + prompt = cat answer [OK]

Hint: Match image content with prompt question [OK]

Common Mistakes:

• Assuming the model cannot see images
• Expecting error due to missing arguments
• Confusing animal types in output

4. Identify the error in this GPT-4V usage code snippet:

response = gpt4v.ask(prompt='Describe this image.')
print(response)

medium

A. Missing image input argument in the ask function.

B. The prompt text is too short.

C. The print statement is incorrect syntax.

D. The ask function does not exist in GPT-4V.

Solution

1. Step 1: Check required inputs for vision-language query

   GPT-4V requires both an image and a prompt to answer about the image.

2. Step 2: Identify missing argument

   The code only provides a prompt but no image, which is necessary for vision understanding.

3. Final Answer:

   Missing image input argument in the ask function. -> Option A

4. Quick Check:

   Image missing in ask() call [OK]

Hint: Vision queries need both image and prompt [OK]

Common Mistakes:

• Ignoring the need for image input
• Thinking prompt length causes error
• Assuming print syntax is wrong

5. You want GPT-4V to find all objects in a complex image and list them with counts. Which approach is best?

hard

A. Send multiple images without prompts and combine answers manually.

B. Send only the image without any prompt and expect a list.

C. Use a prompt asking to translate the image content to another language.

D. Use a prompt like List all objects and their counts in this image: [image] and parse the response.

Solution

1. Step 1: Understand the task requirements

   The task is to identify and count objects in one image, so a clear prompt is needed.

2. Step 2: Choose the prompt that requests object listing and counting

   Use a prompt like List all objects and their counts in this image: [image] and parse the response, which explicitly asks for listing objects and counts, which GPT-4V can handle.

3. Step 3: Eliminate other options

   Sending only the image without any prompt lacks specific task instructions. Using a prompt to translate the image content is unrelated to object detection. Sending multiple images without prompts and combining answers manually is inefficient and unclear.

4. Final Answer:

   Use a prompt like <code>List all objects and their counts in this image: [image]</code> and parse the response. -> Option D

5. Quick Check:

   Clear prompt + image = correct object list [OK]

Hint: Always include clear prompt with image for object detection [OK]

Common Mistakes:

• Sending image without prompt expecting detailed output
• Confusing translation with object detection
• Using multiple images without clear instructions