m-aliabbas1
/

Florence-2-FT-path-vqa

@@ -8,196 +8,74 @@ metrics:
 pipeline_tag: image-text-to-text
 ---
-# Model Card for Model ID
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 pipeline_tag: image-text-to-text
 ---
+# Florence-2 Fine-Tuning with PathVQA
+This document provides an overview of fine-tuning the Florence-2 model with the PathVQA dataset. Florence-2 is a sequence-to-sequence model that excels in various computer vision tasks by leveraging its robust architecture and extensive pre-training dataset.
+## Florence-2 Model Overview
+Florence-2 formulates computer vision problems as sequence-to-sequence tasks. The model takes images and text as inputs and generates text as output. Below is a detailed breakdown of the model and its components:
+### Model Architecture
+- **DaViT Vision Encoder**: Converts images into visual embeddings.
+- **BERT Text Encoder**: Converts text prompts into text and location embeddings.
+- **Transformer Architecture**: A standard encoder-decoder transformer processes the embeddings to generate text and location tokens.
+### Strength of Florence-2
+The model's strength lies not in its architecture but in the extensive dataset it was pre-trained on. The authors created the FLD-5B dataset to address the limitations of existing datasets like WIT and SA-1B, which contain limited information.
+### FLD-5B Dataset
+- **Content**: Over 5 billion annotations for 126 million images, including boxes, masks, captions, and grounding.
+- **Creation Process**: Largely automated using off-the-shelf task-specific models and a set of heuristics and quality checks to clean the obtained results.
+## PathVQA Dataset
+PathVQA is a dataset of question-answer pairs on pathology images, intended for training and testing Medical Visual Question Answering (VQA) systems. It includes both open-ended questions and binary "yes/no" questions.
+### Source
+The dataset is built from two publicly available pathology textbooks:
+- "Textbook of Pathology"
+- "Basic Pathology"
+Additionally, images were sourced from the "Pathology Education Informational Resource" (PEIR) digital library. The copyrights of images and captions belong to the publishers and authors of these two books and the owners of the PEIR digital library.
+### Dataset Summary
+- **Total Images**: 5,004
+- **Total Question-Answer Pairs**: 32,795
+  - **Referenced Images**: 4,289
+  - **Unused Images**: 715
+After removing duplicate image-question-answer triplets, the dataset contains 32,632 question-answer pairs on 4,289 images.
+### Supported Tasks and Leaderboards
+The PathVQA dataset has an active leaderboard on Papers with Code, ranking models based on:
+- **Yes/No Accuracy**: Accuracy of generated answers for binary "yes/no" questions.
+- **Free-form Accuracy**: Accuracy of generated answers for open-ended questions.
+- **Overall Accuracy**: Accuracy of generated answers across all questions.
+## Fine-Tuning Florence-2 with PathVQA
+The Florence-2 model was fine-tuned using the PathVQA dataset to adapt it for medical visual question answering tasks.
+### Methodology
+1. **Dataset Preparation**: The PathVQA dataset was obtained from the updated Google Drive link shared by the authors on February 15, 2023.
+2. **Data Cleaning**: Duplicate image-question-answer triplets were removed.
+3. **Fine-Tuning**: The model was fine-tuned for seven epochs with the training set.
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** Mohammed Ali Abbas
+- **Model type:** VQA
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]