Update README.md

README.md

@@ -4,26 +4,53 @@ license_link: https://huggingface.co/microsoft/Florence-2-base-ft/resolve/main/L
 pipeline_tag: image-text-to-text
 tags:
 - vision
+- ocr
+- segmentation
 ---
-
-# Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
+# TF-ID: Table/Figure IDentifier for academic papers
 
 ## Model Summary
 
-This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft.
+TF-ID (Table/Figure IDentifier) is a family of object detection models finetuned to extract tables and figures in academic papers created by [Yifei Hu](https://x.com/hu_yifei). They come in four versions:
+| Model   | Model size | Model Description | 
+| ------- | ------------- |   ------------- |  
+| TF-ID-base[[HF]](https://huggingface.co/yifeihu/TF-ID-base) | 0.23B  | Extract tables/figures and their caption text  
+| TF-ID-large[[HF]](https://huggingface.co/yifeihu/TF-ID-large) | 0.77B  | Extract tables/figures and their caption text  
+| TF-ID-base-no-caption[[HF]](https://huggingface.co/yifeihu/TF-ID-base-no-caption) | 0.23B  | Extract tables/figures without caption text
+| TF-ID-large-no-caption[[HF]](https://huggingface.co/yifeihu/TF-ID-large-no-caption) | 0.77B  | Extract tables/figures without caption text
+All TF-ID models are finetuned from [microsoft/Florence-2](https://huggingface.co/microsoft/Florence-2-large-ft) checkpoints.
 
-Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks.  Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. 
+The models were finetuned with papers from Hugging Face Daily Papers. All bounding boxes are manually annotated and checked by humans.
 
-Resources and Technical Documentation:
-+ [Florence-2 technical report](https://arxiv.org/abs/2311.06242). 
-+ [Jupyter Notebook for inference and visualization of Florence-2-large model](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb)
+TF-ID models take an image of a single paper page as the input, and return bounding boxes for all tables and figures in the given page. 
 
-| Model   | Model size | Model Description | 
-| ------- | ------------- |   ------------- |  
-| Florence-2-base[[HF]](https://huggingface.co/microsoft/Florence-2-base) | 0.23B  | Pretrained model with FLD-5B  
-| Florence-2-large[[HF]](https://huggingface.co/microsoft/Florence-2-large) | 0.77B  | Pretrained model with FLD-5B  
-| Florence-2-base-ft[[HF]](https://huggingface.co/microsoft/Florence-2-base-ft) | 0.23B  | Finetuned model on a colletion of downstream tasks
-| Florence-2-large-ft[[HF]](https://huggingface.co/microsoft/Florence-2-large-ft) | 0.77B  | Finetuned model on a colletion of downstream tasks
+TF-ID-base and TF-ID-large draw bounding boxes around tables/figures and their caption text.
+
+TF-ID-base-no-caption and TF-ID-large-no-caption draw bounding boxes around tables/figures without their caption text.
+
+![image/png](https://huggingface.co/yifeihu/TF-ID-base/resolve/main/td-id-caption.png)
+
+Object Detection results format: 
+{'\<OD>': {'bboxes': [[x1, y1, x2, y2], ...], 
+'labels': ['label1', 'label2', ...]} }
+
+## Benchmarks
+
+We tested the models on paper pages outside the training dataset. The papers are a subset of huggingface daily paper.
+
+Correct output - the model draws correct bounding boxes for every table/figure in the given page.
+
+| Model                                                         | Total Images | Correct Output | Success Rate |
+|---------------------------------------------------------------|--------------|----------------|--------------|
+| TF-ID-base[[HF]](https://huggingface.co/yifeihu/TF-ID-base)   | 258          | 251            | 97.29%       |
+| TF-ID-large[[HF]](https://huggingface.co/yifeihu/TF-ID-large) | 258          | 253            | 98.06%       |
+
+| Model                                                         | Total Images | Correct Output | Success Rate |
+|---------------------------------------------------------------|--------------|----------------|--------------|
+| TF-ID-base-no-caption[[HF]](https://huggingface.co/yifeihu/TF-ID-base-no-caption)   | 261          | 253            | 96.93%       |
+| TF-ID-large-no-caption[[HF]](https://huggingface.co/yifeihu/TF-ID-large-no-caption) | 261          | 254            | 97.32%       |
+
+Depending on the use cases, some "incorrect" output could be totally usable. For example, the model draw two bounding boxes for one figure with two child components.
  
 ## How to Get Started with the Model
 
@@ -31,17 +58,15 @@ Use the code below to get started with the model.
 
 ```python
 import requests
-
 from PIL import Image
 from transformers import AutoProcessor, AutoModelForCausalLM 
 
-
-model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
-processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("yifeihu/TF-ID-base-no-caption", trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("yifeihu/TF-ID-base-no-caption", trust_remote_code=True)
 
 prompt = "<OD>"
 
-url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
+url = "https://huggingface.co/yifeihu/TF-ID-base/resolve/main/arxiv_2305_10853_5.png?download=true"
 image = Image.open(requests.get(url, stream=True).raw)
 
 inputs = processor(text=prompt, images=image, return_tensors="pt")
@@ -58,198 +83,20 @@ generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False
 parsed_answer = processor.post_process_generation(generated_text, task="<OD>", image_size=(image.width, image.height))
 
 print(parsed_answer)
-
 ```
 
+To visualize the results, see [this tutorial notebook](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb) for more details.
 
-## Tasks
-
-This model is capable of performing different tasks through changing the prompts.
-
-First, let's define a function to run a prompt.
-
-<details>
-<summary> Click to expand </summary>
-
-```python
-import requests
-
-from PIL import Image
-from transformers import AutoProcessor, AutoModelForCausalLM 
-
-
-model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
-processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
-
-url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
-image = Image.open(requests.get(url, stream=True).raw)
-
-def run_example(task_prompt, text_input=None):
-    if text_input is None:
-        prompt = task_prompt
-    else:
-        prompt = task_prompt + text_input
-    inputs = processor(text=prompt, images=image, return_tensors="pt")
-    generated_ids = model.generate(
-      input_ids=inputs["input_ids"],
-      pixel_values=inputs["pixel_values"],
-      max_new_tokens=1024,
-      num_beams=3
-    )
-    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-
-    parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
-
-    print(parsed_answer)
-```
-</details>
-
-Here are the tasks `Florence-2` could perform:
-
-<details>
-<summary> Click to expand </summary>
-
-
-### Caption
-```python
-prompt = "<CAPTION>"
-run_example(prompt)
-```
-
-### Detailed Caption
-```python
-prompt = "<DETAILED_CAPTION>"
-run_example(prompt)
-```
-
-### More Detailed Caption
-```python
-prompt = "<MORE_DETAILED_CAPTION>"
-run_example(prompt)
-```
-
-### Caption to Phrase Grounding 
-caption to phrase grounding task requires additional text input, i.e. caption. 
-
-Caption to phrase grounding results format: 
-{'\<CAPTION_TO_PHRASE_GROUNDING>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}}
-```python
-task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>"
-results = run_example(task_prompt, text_input="A green car parked in front of a yellow building.")
-```
-
-### Object Detection
-
-OD results format: 
-{'\<OD>': {'bboxes': [[x1, y1, x2, y2], ...], 
-'labels': ['label1', 'label2', ...]} }
-
-```python
-prompt = "<OD>"
-run_example(prompt)
-```
-
-### Dense Region Caption
-Dense region caption results format: 
-{'\<DENSE_REGION_CAPTION>' : {'bboxes': [[x1, y1, x2, y2], ...], 
-'labels': ['label1', 'label2', ...]} }
-```python
-prompt = "<DENSE_REGION_CAPTION>"
-run_example(prompt)
-```
-
-### Region proposal
-Dense region caption results format: 
-{'\<REGION_PROPOSAL>': {'bboxes': [[x1, y1, x2, y2], ...], 
-'labels': ['', '', ...]}}
-```python
-prompt = "<REGION_PROPOSAL>"
-run_example(prompt)
-```
-
-
-### OCR 
-
-```python
-prompt = "<OCR>"
-run_example(prompt)
-```
-
-### OCR with Region
-OCR with region output format:
-{'\<OCR_WITH_REGION>': {'quad_boxes': [[x1, y1, x2, y2, x3, y3, x4, y4], ...], 'labels': ['text1', ...]}}
-```python
-prompt = "<OCR_WITH_REGION>"
-run_example(prompt)
-```
+## Finetuning Code and Dataset
 
-for More detailed examples, please refer to [notebook](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb)
-</details>
-
-# Benchmarks
-
-## Florence-2 Zero-shot performance
-  
-The following table presents the zero-shot performance of generalist vision foundation models on image captioning and object detection evaluation tasks. These models have not been exposed to the training data of the evaluation tasks during their training phase.  
-  
-| Method | #params | COCO Cap. test CIDEr | NoCaps val CIDEr | TextCaps val CIDEr | COCO Det. val2017 mAP |  
-|--------|---------|----------------------|------------------|--------------------|-----------------------|
-| Flamingo | 80B | 84.3 | - | - | - | 
-| Florence-2-base| 0.23B | 133.0 | 118.7 | 70.1 | 34.7 | 
-| Florence-2-large| 0.77B | 135.6 | 120.8 | 72.8 | 37.5 |
-
-  
-The following table continues the comparison with performance on other vision-language evaluation tasks.  
-  
-| Method | Flickr30k test R@1 | Refcoco val Accuracy | Refcoco test-A Accuracy | Refcoco test-B Accuracy | Refcoco+ val Accuracy | Refcoco+ test-A Accuracy | Refcoco+ test-B Accuracy | Refcocog val Accuracy | Refcocog test Accuracy | Refcoco RES val mIoU |  
-|--------|----------------------|----------------------|-------------------------|-------------------------|-----------------------|--------------------------|--------------------------|-----------------------|------------------------|----------------------|  
-| Kosmos-2 | 78.7 | 52.3 | 57.4 | 47.3 | 45.5 | 50.7 | 42.2 | 60.6 | 61.7 | - |  
-| Florence-2-base | 83.6 | 53.9 | 58.4 | 49.7 | 51.5 | 56.4 | 47.9 | 66.3 | 65.1 | 34.6 |  
-| Florence-2-large | 84.4 | 56.3 | 61.6 | 51.4 | 53.6 | 57.9 | 49.9 | 68.0 | 67.0 | 35.8 |  
-
-
-
-## Florence-2 finetuned performance 
-
-We finetune Florence-2 models with a collection of downstream tasks, resulting two generalist models *Florence-2-base-ft* and *Florence-2-large-ft* that can conduct a wide range of downstream tasks. 
-  
-The table below compares the performance of specialist and generalist models on various captioning and Visual Question Answering (VQA) tasks. Specialist models are fine-tuned specifically for each task, whereas generalist models are fine-tuned in a task-agnostic manner across all tasks. The symbol "▲" indicates the usage of external OCR as input.  
-  
-| Method         | # Params | COCO Caption Karpathy test CIDEr | NoCaps val CIDEr | TextCaps val CIDEr | VQAv2 test-dev Acc | TextVQA test-dev Acc | VizWiz VQA test-dev Acc |  
-|----------------|----------|-----------------------------------|------------------|--------------------|--------------------|----------------------|-------------------------|  
-| **Specialist Models**   |          |                                   |                  |                    |                    |                      |                         |  
-| CoCa           | 2.1B     | 143.6                              | 122.4            | -                  | 82.3               | -                    | -                       |  
-| BLIP-2         | 7.8B     | 144.5                              | 121.6            | -                  | 82.2               | -                    | -                       |  
-| GIT2           | 5.1B     | 145.0                              | 126.9            | 148.6              | 81.7               | 67.3                 | 71.0                    |  
-| Flamingo       | 80B      | 138.1                              | -                | -                  | 82.0               | 54.1                 | 65.7                    |  
-| PaLI           | 17B      | 149.1                              | 127.0            | 160.0▲             | 84.3               | 58.8 / 73.1▲         | 71.6 / 74.4▲            |  
-| PaLI-X         | 55B      | 149.2                              | 126.3            | 147.0 / 163.7▲     | 86.0               | 71.4 / 80.8▲         | 70.9 / 74.6▲            |  
-| **Generalist Models**   |          |                                   |                  |                    |                    |                      |                         |  
-| Unified-IO     | 2.9B     | -                                  | 100.0            | -                  | 77.9               | -                    | 57.4                    |  
-| Florence-2-base-ft | 0.23B  | 140.0                              | 116.7            | 143.9              | 79.7               | 63.6                 | 63.6                    |  
-| Florence-2-large-ft | 0.77B  | 143.3                              | 124.9            | 151.1              | 81.7               | 73.5                 | 72.6                    |  
-  
-  
-| Method               | # Params | COCO Det. val2017 mAP | Flickr30k test R@1 | RefCOCO val Accuracy | RefCOCO test-A Accuracy | RefCOCO test-B Accuracy | RefCOCO+ val Accuracy | RefCOCO+ test-A Accuracy | RefCOCO+ test-B Accuracy | RefCOCOg val Accuracy | RefCOCOg test Accuracy | RefCOCO RES val mIoU |  
-|----------------------|----------|-----------------------|--------------------|----------------------|-------------------------|-------------------------|------------------------|---------------------------|---------------------------|------------------------|-----------------------|------------------------|  
-| **Specialist Models** |          |                       |                    |                      |                         |                         |                        |                           |                           |                        |                       |                        |  
-| SeqTR                | -        | -                     | -                  | 83.7                 | 86.5                    | 81.2                    | 71.5                   | 76.3                      | 64.9                      | 74.9                   | 74.2                  | -                      |  
-| PolyFormer           | -        | -                     | -                  | 90.4                 | 92.9                    | 87.2                    | 85.0                   | 89.8                      | 78.0                      | 85.8                   | 85.9                  | 76.9                   |  
-| UNINEXT              | 0.74B    | 60.6                  | -                  | 92.6                 | 94.3                    | 91.5                    | 85.2                   | 89.6                      | 79.8                      | 88.7                   | 89.4                  | -                      |  
-| Ferret               | 13B      | -                     | -                  | 89.5                 | 92.4                    | 84.4                    | 82.8                   | 88.1                      | 75.2                      | 85.8                   | 86.3                  | -                      |  
-| **Generalist Models** |          |                       |                    |                      |                         |                         |                        |                           |                           |                        |                       |                        |  
-| UniTAB               | -        | -                     | -                  | 88.6                 | 91.1                    | 83.8                    | 81.0                   | 85.4                      | 71.6                      | 84.6                   | 84.7                  | -                      |  
-| Florence-2-base-ft | 0.23B    | 41.4                  | 84.0                | 92.6                 | 94.8                    | 91.5                   | 86.8                   | 91.7                      | 82.2                      | 89.8                   | 82.2                  | 78.0                  |  
-| Florence-2-large-ft| 0.77B    | 43.4                  | 85.2               | 93.4                 | 95.3                    | 92.0                    | 88.3                   | 92.9                      | 83.6                      | 91.2                   | 91.7                  | 80.5                   |  
-  
+Coming soon!
 
 ## BibTex and citation info
 
 ```
-@article{xiao2023florence,
-  title={Florence-2: Advancing a unified representation for a variety of vision tasks},
-  author={Xiao, Bin and Wu, Haiping and Xu, Weijian and Dai, Xiyang and Hu, Houdong and Lu, Yumao and Zeng, Michael and Liu, Ce and Yuan, Lu},
-  journal={arXiv preprint arXiv:2311.06242},
-  year={2023}
+@misc{TF-ID, 
+      url={[https://huggingface.co/yifeihu/TF-ID-base](https://huggingface.co/yifeihu/TF-ID-base)}, 
+      title={TF-ID: Table/Figure IDentifier for academic papers}, 
+      author={"Yifei Hu"}
 }
 ```