diff --git a/examples/multimodal/Vision_Fine_tuning_on_GPT4o_for_Visual_Question_Answering_on_Medical_Images.ipynb b/examples/multimodal/Vision_Fine_tuning_on_GPT4o_for_Visual_Question_Answering_on_Medical_Images.ipynb new file mode 100644 index 0000000000..f5a2ecde2a --- /dev/null +++ b/examples/multimodal/Vision_Fine_tuning_on_GPT4o_for_Visual_Question_Answering_on_Medical_Images.ipynb @@ -0,0 +1,913 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Vision Fine-tuning on GPT-4o for Visual Question Answering on Medical Images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're excited to announce the launch of [Vision Fine-Tuning on GPT-4o](https://openai.com/index/introducing-vision-to-the-fine-tuning-api/), a cutting-edge multimodal fine-tuning capability that empowers developers to fine-tune GPT-4o using both **images** and **text**. With this new feature, you can customize models to have stronger image understanding capabilities, unlocking possibilities across various industries and applications.\n", + "\n", + "From **advanced visual search** to **improved object detection** for autonomous vehicles or smart cities, and even **precision in medical image analysis**, vision fine-tuning enables you to craft solutions tailored to your specific needs. By combining text and image inputs, this product is uniquely positioned for tasks like **visual question answering**, where detailed, context-aware answers are derived from analyzing images. In general, this seems to be most effective when the model is presented with questions and images that resemble the training data as we are able to teach the model how to search and identify relevant parts of the image to answer the question correctly. Similarly to fine-tuning on text inputs, vision fine-tuning is not as useful for teaching the model new information.\n", + "\n", + "In this guide, we’ll walk you through the steps to fine-tune GPT-4o with multimodal inputs. Specifically, we’ll demonstrate how to train a model for answering questions related to **medical radiology images**, but the potential applications span countless domains—from **web design** and **education** to **healthcare** and **research**.\n", + "\n", + "Whether you're looking to build smarter defect detection models for manufacturing, enhance complex document processing and diagram understanding, or develop applications with better visual comprehension for a variety of other use cases, this guide will show you just how fast and easy it is to get started.\n", + "\n", + "For more information, check out the full [Documentation](https://platform.openai.com/docs/guides/fine-tuning/vision)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI, ChatCompletion\n", + "import json\n", + "import re\n", + "import os\n", + "from tqdm import tqdm\n", + "from datasets import load_dataset\n", + "import base64\n", + "from PIL import Image\n", + "from IPython.display import display\n", + "from io import BytesIO\n", + "from pydantic import BaseModel, Field\n", + "from typing import List\n", + "import matplotlib.pyplot as plt\n", + "from collections import Counter\n", + "\n", + "\n", + "client = OpenAI()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will work with a dataset of question-answer pairs on radiology images from the [VQA-RAD dataset](https://huggingface.co/datasets/flaviagiammarino/vqa-rad), accessible through HuggingFace. This dataset is specifically designed for **Medical Visual Question Answering (VQA)** systems, allowing us to fine-tune models for real-world medical applications.\n", + "\n", + "Curated by a team of clinicians, the dataset consists of manually generated samples, offering both open-ended questions and binary \"yes/no\" questions about radiology images. These carefully crafted questions help train and evaluate models on their ability to interpret and analyze medical imagery, a critical task in healthcare and diagnostics." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# load dataset\n", + "ds = load_dataset(\"flaviagiammarino/vqa-rad\")\n", + "ds_train = ds['train']\n", + "ds_test = ds['test']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's inspect the first sample from the training set.\n", + "\n", + "In this example, the question prompts the model to analyze a brain scan and determine whether any regions of the brain show signs of infarction. A brain infarction occurs when brain tissue is damaged or dies due to a lack of blood flow, which can lead to stroke or other serious conditions.\n", + "\n", + "This type of task is typical in medical visual question answering, where the model must interpret complex medical images and provide accurate, context-specific responses. By training on these kinds of questions, we can enhance the model's ability to perform detailed image analysis in critical fields like radiology." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "QUESTION: are regions of the brain infarcted?\n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ANSWER: yes\n" + ] + } + ], + "source": [ + "# display first training example\n", + "print('QUESTION:', ds_train[0]['question'])\n", + "display(ds_train[0]['image'])\n", + "print('ANSWER:', ds_train[0]['answer'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To ensure successful fine-tuning of our model, it’s crucial to properly structure the training data. Correctly formatting the data helps avoid validation errors during training and ensures the model can effectively learn from both text and image inputs. The good news is, this process is quite straightforward.\n", + "\n", + "Each example in the training dataset should be a conversation in the same format as the **Chat Completions API**. Specifically, this means structuring the data as a series of **messages**, where each message includes a `role` (such as \"user\" or \"assistant\") and the `content` of the message.\n", + "\n", + "Since we are working with both text and images for vision fine-tuning, we’ll construct these messages to include both content types. For each training sample, the question about the image is presented as a user message, and the corresponding answer is provided as an assistant message.\n", + "\n", + "Images can be included in one of two ways:\n", + "* As **HTTP URLs**, referencing the location of the image.\n", + "* As **data URLs** containing the image encoded in **base64**.\n", + "\n", + "Here’s an example of how the message format should look:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "{\n", + " \"messages\": \n", + " [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": \"Use the image to answer the question.\"\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"are regions of the brain infarcted?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": \"data:image/jpeg;base64,\"}}\n", + " ]\n", + " }\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by defining the **system instructions** for our model. These instructions provide the model with important context, guiding how it should behave when processing the training data. Clear and concise system instructions are particularly useful to make sure the model reasons well on both text and images." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "SYSTEM_PROMPT = \"\"\"\n", + "Generate an answer to the question based on the radiology image provided.\n", + "Questions will include both open-ended questions and binary \"yes/no\" questions.\n", + "The question-answer pairs were manually generated by a team of clinicians.\n", + "\n", + "You will read the question and examine the corresponding radiology image to provide an accurate answer.\n", + "\n", + "# Steps\n", + "\n", + "1. **Read the Question:** Carefully analyze the question to understand what information is being asked.\n", + "2. **Examine the Radiology Image:** Look at the provided radiology image to extract relevant details needed to answer the question.\n", + "3. **Formulate a Reasoned Answer:**\n", + " - For binary questions (yes/no), determine the presence or absence of the mentioned condition or feature.\n", + " - For open-ended questions, identify and describe the specific details asked (e.g., abnormality, location, modality).\n", + "\n", + "# Output Format\n", + "\n", + "- Provide your answer in a concise and clear manner. If the question is binary, answer with \"Yes\" or \"No.\" For open-ended questions, provide a brief and accurate description in one sentence or less.\n", + "\n", + "# Notes\n", + "\n", + "- Always prioritize accuracy and clarity in your responses.\n", + "- Be mindful of the specifics in the questions, such as anatomical locations or specific medical terms.\n", + "- Ensure reasoning steps logically lead to the conclusions before stating your final answer.\n", + "\n", + "# Examples\n", + "You will be provided with examples of questions and corresponding radiology images, along with the reasoning and conclusion for each example. Use these examples to guide your reasoning process.\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To ensure our images are properly formatted for vision fine-tuning, they must be in **base64 format** and either **RGB or RGBA**. This ensures the model can accurately process the images during training. Below is a function that handles the encoding of images, while also converting them to the correct format if necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def encode_image(image):\n", + " if image.mode != 'RGB':\n", + " image = image.convert('RGB') # Convert to RGB\n", + " buffered = BytesIO()\n", + " image.save(buffered, format=\"JPEG\")\n", + " return base64.b64encode(buffered.getvalue()).decode(\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also include **Few-Shot examples** from the training set as user and assistant messages to help guide the model's reasoning process.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "FEW_SHOT_EXAMPLES = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Example 1:**\\n\\n**Question:** Are regions of the brain infarcted?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(ds_train[0]['image'])}\"}}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Answer:**\\n\\n**Reasoning:** After examining the radiology image, I can see multiple areas of hypodensity in the cerebral region, which suggests restricted blood flow.\\n\\n**Conclusion:** Yes\"}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Example 2:**\\n\\n**Question:** What is the location of the mass?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(ds_train[7]['image'])}\"}}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Answer:**\\n\\n**Reasoning:** After examining the radiology image, I see that the central area shows some asymmetry or altered density, which may hint at a mass in the pineal region near the center of the brain. There is also slight distortion around the third ventricle, which could suggest the mass compressing or displacing the ventricles, which is consistent with a mass in the pineal region. The image also shows a region with different contrast from surrounding brain tissue, which might indicate abnormal tissue.\\n\\n**Conclusion:** pineal region\"}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Example 3:**\\n\\n**Question:** What is the image modality?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(ds_train[61]['image'])}\"}}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Answer:**\\n\\n**Reasoning:** The image modality is MRI FLAIR (Fluid-Attenuated Inversion Recovery). This is evident from the dark cerebrospinal fluid (CSF) areas, as FLAIR sequences are designed to suppress CSF signals, allowing for better visualization of lesions or abnormal fluid in the brain. Additionally, the distinct differentiation between gray and white matter is characteristic of MRI FLAIR imaging.\\n\\n**Conclusion:** mri/flair\"}\n", + " ]\n", + " }\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have our system instructions, few-shot examples, and the image encoding function in place, the next step is to iterate through the training set and construct the messages required for fine-tuning. As a reminder, each training example must be formatted as a conversation and must include both the image (in base64 format) and the corresponding question and answer.\n", + "\n", + "To fine-tune GPT-4o, we recommend providing at least **10 examples**, but you’ll typically see noticeable improvements with **50 to 100** training examples. In this case, we'll go all-in and fine-tune the model using the full VQA-RAD dataset, which consists of 1,793 samples." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1793/1793 [00:15<00:00, 118.94it/s]\n" + ] + } + ], + "source": [ + "# constructing the training set\n", + "json_data = []\n", + "\n", + "for idx, example in enumerate(tqdm(ds_train)):\n", + " system_message = {\n", + " \"role\": \"system\",\n", + " \"content\": [{\"type\": \"text\", \"text\": SYSTEM_PROMPT}]\n", + " }\n", + " \n", + " user_message = {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": f\"Question [{idx}]: {example['question']}\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(example['image'])}\"}}\n", + " ]\n", + " }\n", + " \n", + " assistant_message = {\n", + " \"role\": \"assistant\",\n", + " \"content\": [{\"type\": \"text\", \"text\": example[\"answer\"]}]\n", + " }\n", + "\n", + " all_messages = [system_message] + FEW_SHOT_EXAMPLES + [user_message, assistant_message]\n", + " \n", + " json_data.append({\"messages\": all_messages})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We save our final training set in a `.jsonl` file where each line in the file represents a single example in the training dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# save the JSON data to a file\n", + "with open(\"vqa-rad-train.jsonl\", \"w\") as f:\n", + " for message in json_data:\n", + " json.dump(message, f)\n", + " f.write(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like the training set, we need to structure our test set in the same message format. However, there's a key difference: since the test set is used for evaluation, we do not include the assistant's message (i.e., the answer). This ensures the model generates its own answers, which we can later compare to the ground truth for performance evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 451/451 [00:03<00:00, 114.08it/s]\n" + ] + } + ], + "source": [ + "# constructing the test set\n", + "json_data = []\n", + "\n", + "for idx, example in enumerate(tqdm(ds_test)):\n", + " system_message = {\n", + " \"role\": \"system\",\n", + " \"content\": [{\"type\": \"text\", \"text\": SYSTEM_PROMPT}]\n", + " }\n", + " \n", + " user_message = {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": f\"Question [{idx}]: {example['question']}\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(example['image'])}\"}}\n", + " ]\n", + " }\n", + "\n", + " all_messages = [system_message] + FEW_SHOT_EXAMPLES + [user_message]\n", + " \n", + " json_data.append({\"messages\": all_messages})" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# save the JSON data to a file\n", + "with open(\"vqa-rad-test.jsonl\", \"w\") as f:\n", + " for message in json_data:\n", + " json.dump(message, f)\n", + " f.write(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fine-tuning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have prepared our training dataset in the right format, we can upload our training file using the [Files API](https://platform.openai.com/docs/api-reference/files/create) for fine-tuning." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FileObject(id='file-kfuTVfoqBBwUpqcQzdgRi3sV', bytes=320051115, created_at=1730133656, filename='vqa-rad-train.jsonl', object='file', purpose='fine-tune', status='processed', status_details=None)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# upload training file\n", + "client.files.create(\n", + " file=open(\"vqa-rad-train.jsonl\", \"rb\"),\n", + " purpose=\"fine-tune\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the file is uploaded, we're ready to proceed to the next step: starting the fine-tuning job.\n", + "\n", + "To create a fine-tuning job, we use the fine-tuning API. You can track the progress of the fine-tuning job in the [Platform UI](https://platform.openai.com/finetune/)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FineTuningJob(id='ftjob-8euOSR5ForiIKV3Z1qqWOQwN', created_at=1730133739, error=Error(code=None, message=None, param=None), fine_tuned_model=None, finished_at=None, hyperparameters=Hyperparameters(n_epochs='auto', batch_size='auto', learning_rate_multiplier='auto'), model='gpt-4o-2024-08-06', object='fine_tuning.job', organization_id='org-l89177bnhkme4a44292n5r3j', result_files=[], seed=1841286653, status='validating_files', trained_tokens=None, training_file='file-kfuTVfoqBBwUpqcQzdgRi3sV', validation_file=None, estimated_finish=None, integrations=[], user_provided_suffix=None, method=None)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create fine tuning job\n", + "file = 'file-kfuTVfoqBBwUpqcQzdgRi3sV'\n", + "client.fine_tuning.jobs.create(\n", + " training_file=file, \n", + " model=\"gpt-4o-2024-08-06\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the fine-tuning job is complete, it’s time to evaluate the performance of our model by running inference on the test set. This step involves using the fine-tuned model to generate responses to the questions in the test set and comparing its predictions to the ground truth answers for evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 451/451 [37:32<00:00, 4.99s/it]\n" + ] + } + ], + "source": [ + "# evalute fine-tuned mdoel\n", + "model = \"ft:gpt-4o-2024-08-06:openai::ANOi5P2A\"\n", + "\n", + "# load the test data from JSONL file\n", + "test_data = []\n", + "with open(\"vqa-rad-test.jsonl\", \"r\") as f:\n", + " for line in f:\n", + " test_data.append(json.loads(line))\n", + "\n", + "# run the prompts through the finetuned model and store the results\n", + "results = []\n", + "for example in tqdm(test_data):\n", + " response = client.chat.completions.create(\n", + " model=model,\n", + " messages=example[\"messages\"],\n", + " store=True,\n", + " metadata = {'dataset': 'vqa-rad-test'}\n", + " )\n", + " predicted_answer = response.choices[0].message.content.strip()\n", + " \n", + " # regex to get the question ID\n", + " match = re.search(r'\\[(\\d+)\\]', example[\"messages\"][-1][\"content\"][0][\"text\"])\n", + " if match:\n", + " example_id = int(match.group(1))\n", + " else:\n", + " example_id = -1\n", + " \n", + " actual_answer = ds_test[example_id]['answer']\n", + "\n", + " results.append({\n", + " \"example_id\": example_id,\n", + " \"predicted_answer\": predicted_answer,\n", + " \"actual_answer\": actual_answer\n", + " })\n", + "\n", + "# save the results to a file\n", + "with open(\"vqa-rad-ft-results.jsonl\", \"w\") as f:\n", + " for result in results:\n", + " json.dump(result, f)\n", + " f.write(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we’ve run inference using our fine-tuned model, let’s inspect a few specific examples to understand how well the model performed compared to the actual answers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Q: which ventricle is the mass located in?\n", + "{\"example_id\": 262, \"predicted_answer\": \"fourth ventricle\", \"actual_answer\": \"4th ventricle\"}\n", + "# Q: what modality was used for this image?\n", + "{\"example_id\": 310, \"predicted_answer\": \"mri - flair\", \"actual_answer\": \"mri\"}\n", + "# Q: which blood vessels are affected?\n", + "{\"example_id\": 233, \"predicted_answer\": \"left mca\", \"actual_answer\": \"left aca and mca\"}\n", + "# Q: how wide is the heart compared to the thorax?\n", + "{\"example_id\": 434, \"predicted_answer\": \"less than half\", \"actual_answer\": \"it is less than half the width of the thorax\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we’ve seen, while many of the model’s **predicted answers** are close to the **ground truth**, they don’t always match exactly, particularly in open-ended questions where phrasing or details may differ. To assess the quality of these predictions, we will use GPT-4o to evaluate the similarity between the predicted responses and the ground truth labels from the dataset.\n", + "\n", + "In order to evaluate our model responses, we will use GPT-4o to determine the similarity between the ground truth and our predicted responses. We will rank our predicted answers based on the following criteria:\n", + "* **Very Similar**: The predicted answer exactly matches the ground truth and there is no important information omitted, although wording and phrasing of the answer may differ.\n", + "\n", + "* **Mostly Similar**: The predicted answer closely aligns with the ground truth in terms of medical diagnosis and recommendations, with some minor discrepancies.\n", + "\n", + "* **Somewhat Similar**: Although the predicted answer has noticeable differences to the ground truth, the core content is accurate, perhaps with some missing information.\n", + "\n", + "* **Incorrect**: The predicted answer is completely incorrect, irrelevant, or contains critical errors or omissions from the ground truth.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "results = []\n", + "with open(\"vqa-rad-ft-results.jsonl\", \"r\") as f:\n", + " for line in f:\n", + " results.append(json.loads(line))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "results = []\n", + "with open(\"vqa-rad-4o-results.jsonl\", \"r\") as f:\n", + " for line in f:\n", + " results.append(json.loads(line))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 451/451 [05:24<00:00, 1.39it/s]\n" + ] + } + ], + "source": [ + "# define output schema\n", + "class Result(BaseModel):\n", + " example_id: int = Field(description=\"The unique ID of the question\")\n", + " rating: str = Field(description=\"The assigned similarity rating. One of [Very Similar | Mostly Similar | Somewhat Similar | Incorrect]\")\n", + " type: str = Field(description=\"The type of question. Open if the question is binary yes/no, otherwise Closed. One of [Open | Closed]\")\n", + "\n", + "EVAL_PROMPT = \"\"\"\n", + "Evaluate the closeness between the predicted answer and the ground truth for each provided result using a medical expert perspective.\n", + "Rank the predicted answer based on the following criteria:\n", + "\n", + "1. **Very Similar**: The predicted answer exactly matches the ground truth and there is no important information omitted, although wording and phrasing of the answer may differ.\n", + "2. **Mostly Similar**: The predicted answer closely aligns with the ground truth in terms of medical diagnosis and recommendations, with some minor discrepancies.\n", + "3. **Somewhat Similar**: Although the predicted answer has noticeable differences to the ground truth, the core content is accurate, perhaps with some missing information.\n", + "4. **Incorrect**: The predicted answer is completely incorrect, irrelevant, or contains critical errors or omissions from the ground truth.\n", + "\n", + "Ensure to consider both open-ended and yes/no questions.\n", + "\n", + "# Steps\n", + "1. **Analyze the Answers**: Read the predicted answer, and ground truth carefully.\n", + "2. **Evaluate Similarity**:\n", + " - Check if the predicted answer contains the same core information and medical correctness as the ground truth.\n", + " - Determine if there are any important omissions or errors.\n", + "3. **Assign a Rating**: Based on your evaluation, assign the appropriate rating: Very Similar, Mostly Similar, Somewhat Similar, or Incorrect.\n", + "\n", + "# Output Format\n", + "```json\n", + "[\n", + " {\n", + " \"example_id\": [example_id],\n", + " \"rating\": \"[Very Similar | Mostly Similar | Somewhat Similar | Incorrect]\",\n", + " \"type\": \"[Open | Closed]\n", + " }\n", + "]\n", + "```\n", + "\n", + "# Examples\n", + "\n", + "**Input:**\n", + "```json\n", + "{ \"example_id\": 262, \"predicted_answer\": \"fourth ventricle\", \"actual_answer\": \"4th ventricle\" }\n", + "```\n", + "**Reasoning:**\n", + "The predicted answer \"fourth ventricle\" exactly matches the core information in the actual answer \"4th ventricle\" with only a difference in wording.\n", + "**Output:**\n", + "```json\n", + "{ \"example_id\": 262, \"rating\": \"Very Similar\", \"type\": \"Open\" }\n", + "```\n", + "\n", + "**Input:**\n", + "```json\n", + "{ \"example_id\": 310, \"predicted_answer\": \"mri-flair\", \"actual_answer\": \"mri\" }\n", + "```\n", + "**Reasoning:**\n", + "The predicted answer \"mri-flair\" specifies a type of MRI, whereas the actual answer is more general. There is a minor discrepancy in specificity.\n", + "**Output:**\n", + "```json\n", + "{ \"example_id\": 310, \"rating\": \"Mostly Similar\", \"type\": \"Open\" }\n", + "```\n", + "\n", + "**Input:**\n", + "```json\n", + "{ \"example_id\": 233, \"predicted_answer\": \"distal left mca\", \"actual_answer\": \"left aca and mca\" }\n", + "```\n", + "**Reasoning:**\n", + "The predicted answer mentions only \"distal left mca,\" whereas the actual answer includes \"left aca and mca.\" There is a noticeable difference with missing information.\n", + "**Output:**\n", + "```json\n", + "{ \"example_id\": 233, \"rating\": \"Somewhat Similar\", \"type\": \"Open\" }\n", + "```\n", + "\n", + "**Input:**\n", + "```json\n", + "{ \"example_id\": 417, \"predicted_answer\": \"yes\", \"actual_answer\": \"no\" }\n", + "```\n", + "**Reasoning:**\n", + "The predicted answer \"yes\" is completely incorrect compared to the actual answer \"no.\"\n", + "**Output:**\n", + "```json\n", + "{ \"example_id\": 417, \"rating\": \"Incorrect\", \"type\": \"Closed\" }\n", + "```\n", + "\"\"\"\n", + "\n", + "results_w_scores = []\n", + "for result in tqdm(results):\n", + " messages = [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": EVAL_PROMPT\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": str(result)\n", + " }\n", + " ]\n", + "\n", + " response = client.beta.chat.completions.parse(\n", + " model='gpt-4o-2024-08-06',\n", + " messages=messages,\n", + " temperature=0,\n", + " response_format=Result\n", + " )\n", + "\n", + " results_w_scores.append(json.loads(response.choices[0].message.content))\n", + " \n", + " # save the results to a file\n", + " with open(\"vqa-rad-ft-similarity.jsonl\", \"w\") as f:\n", + " for score in results_w_scores:\n", + " json.dump(score, f)\n", + " f.write(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To fully understand the impact of fine-tuning, we also evaluated the same set of test questions using the **non-fine-tuned GPT-4o** model. While the code for this process is not shown here for brevity, it is identical to the evaluation method shown earlier (with the exception of switching the model parameter).\n", + "\n", + "Let's start by comparing the performance of the fine-tuned model vs the non-fine-tuned model for **Closed** form (Yes/No) questions.\n", + "\n", + "Note that with the fine-tuned model, we can check for exact matches between the predicted and actual answers because the model has learned to produce consistent answers. However, for the non-fine-tuned model, we need to account for variations in phrasing and wording in the predicted answers." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fine-tuned model accuracy: 75.7%\n", + "Non-fine-tuned model accuracy: 69.32%\n" + ] + } + ], + "source": [ + "# read in results\n", + "results_ft = []\n", + "with open(\"vqa-rad-ft-results.jsonl\", \"r\") as f:\n", + " for line in f:\n", + " results_ft.append(json.loads(line))\n", + "\n", + "results_4o = []\n", + "with open(\"vqa-rad-4o-results.jsonl\", \"r\") as f:\n", + " for line in f:\n", + " results_4o.append(json.loads(line))\n", + "\n", + "# filter results for yes/no questions\n", + "results_ft = [result for result in results_ft if result['actual_answer'] in ['yes', 'no']]\n", + "results_4o = [result for result in results_4o if result['actual_answer'] in ['yes', 'no']]\n", + "\n", + "# check for correct predictions\n", + "correct_ft = [result for result in results_ft if result['predicted_answer'] == result['actual_answer']]\n", + "correct_4o = [\n", + " result for result in results_4o \n", + " if result['predicted_answer'].lower() == result['actual_answer'] \n", + " or result['actual_answer'] in result['predicted_answer'].lower()\n", + "]\n", + "print(f\"Fine-tuned model accuracy: {round(100*len(correct_ft) / len(results_ft), 2)}%\")\n", + "print(f\"Non-fine-tuned model accuracy: {round(100*len(correct_4o) / len(results_4o), 2)}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With a generous allowance for variations in phrasing and wording for the non-fine-tuned model including ignoring case and allowing for partial matches, the fine-tuned model still outperforms the non-fine-tuned model by a margin of **6.38%** on this set of questions.\n", + "\n", + "Now, let's compare the performance of the fine-tuned model vs the non-fine-tuned model over all questions." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# extract ratings\n", + "ratings_ft = [result['rating'] for result in results_w_scores]\n", + "ratings_4o = [result['rating'] for result in results_w_scores_4o]\n", + "\n", + "# count occurrences of each rating\n", + "rating_counts_ft = Counter(ratings_ft)\n", + "rating_counts_4o = Counter(ratings_4o)\n", + "\n", + "# define the order of ratings\n", + "rating_order = [\"Very Similar\", \"Mostly Similar\", \"Somewhat Similar\", \"Incorrect\"]\n", + "\n", + "# create bar chart\n", + "bar_width = 0.35\n", + "index = range(len(rating_order))\n", + "\n", + "fig, ax = plt.subplots()\n", + "bar1 = ax.bar(index, [rating_counts_ft.get(rating, 0) for rating in rating_order], bar_width, label='FT GPT-4o')\n", + "bar2 = ax.bar([i + bar_width for i in index], [rating_counts_4o.get(rating, 0) for rating in rating_order], bar_width, label='GPT-4o')\n", + "\n", + "ax.set_xlabel('Ratings')\n", + "ax.set_ylabel('Count')\n", + "ax.set_title('Ratings Distribution')\n", + "ax.set_xticks([i + bar_width / 2 for i in index])\n", + "ax.set_xticklabels(rating_order)\n", + "ax.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results provide a clear picture of the benefits gained through fine-tuning, without any other modifications.\n", + "Comparing the distribution of ratings between the **fine-tuned GPT-4o** model and **GPT-4o without fine-tuning**, we see that the fine-tuned model gets more responses exactly correct, as well as getting more responses correct overall.\n", + "### Key Takeaways" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* **Improved Precision**: Fine-tuning helped the model produce more precise answers that matched the ground truth, especially in highly domain-specific tasks like radiology image analysis.\n", + "\n", + "* **Better Generalization**: While the non-fine-tuned GPT-4o performed well on some questions, it was less consistent. The fine-tuned model exhibited better generalization across a variety of test questions, thanks to the exposure to multimodal medical data during training." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While the results from vision fine-tuning are promising, there are still several incorrect responses that highlight opportunities for improvement. Much like fine-tuning on text, the effectiveness of vision fine-tuning depends heavily on the **quality, diversity, and representativeness** of the training data. In particular, models benefit from focusing on cases where errors occur most frequently, allowing for targeted improvements.\n", + "\n", + "Upon reviewing the incorrect results, a recurring issue is the model's difficulty in accurately identifying locations or specific structures based on radiology images. This suggests that the model might require more diverse training examples, especially for location-based tasks, or clearer, expert-informed instructions to guide the model’s understanding.\n", + "\n", + "### Next Steps:\n", + "* **Expand the Training Dataset**: Adding more varied examples that cover the model’s weaker areas, such as location identification in medical images, could significantly enhance performance.\n", + "\n", + "* **Expert-Informed Prompts**: Incorporating domain-specific instructions from experts into the training prompts may further refine the model’s ability to accurately interpret and respond to complex medical queries.\n", + "\n", + "Although there is still progress to be made on this particular task, the initial results are encouraging. With minimal setup and effort, we’ve already observed an uplift in overall accuracy with vision fine-tuning, indicating that this approach holds great potential. Vision fine-tuning opens up possibilities for improvement across a wide range of visual question answering tasks, as well as other tasks that rely on strong visual understanding." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/registry.yaml b/registry.yaml index b221875a2b..73efbf6fbc 100644 --- a/registry.yaml +++ b/registry.yaml @@ -1625,6 +1625,9 @@ authors: - charuj tags: + - latency + - cost + - prompt caching - completions - title: Leveraging model distillation to fine-tune a model @@ -1672,3 +1675,13 @@ tags: - evals - completions + +- title: Vision Fine-tuning on GPT-4o for Visual Question Answering on Medical Images + path: examples/multimodal/Vision_Fine_tuning_on_GPT4o_for_Visual_Question_Answering_on_Medical_Images.ipynb + date: 2024-10-02 + authors: + - danial-openai + tags: + - vision + - fine-tuning + - completions \ No newline at end of file