Update README.md

README.md

@@ -1,51 +1,59 @@
 ---
-license: mit
-license_link: https://huggingface.co/microsoft/Florence-2-large-ft/resolve/main/LICENSE
+license: apache-2.0
 pipeline_tag: image-text-to-text
 tags:
 - vision
+- layout-analysis
+- object-detection
+datasets:
+- ds4sd/DocLayNet-v1.1
+base_model:
+- microsoft/Florence-2-large-ft
 ---
 
-# Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
+# Florence-2-DocLayNet-Fixed
 
 ## Model Summary
 
-This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft.
+We finetuned the Florence-2-large-ft [[HF]](https://huggingface.co/microsoft/Florence-2-large-ft) model using the [[DocLayNet-v1.1]](https://huggingface.co/datasets/ds4sd/DocLayNet-v1.1) dataset. To prevent the model from generating hallucinated class names, we re-mapped all class names to single tokens:
+| Original Class Names | New Class Names |
+|----------------------|-----------------|
+| Caption              | Cap             |
+| Footnote             | Footnote        |
+| Formula              | Math            |
+| List-item            | List            |
+| Page-footer          | Bottom          |
+| Page-header          | Header          |
+| Picture              | Picture         |
+| Section-header       | Section         |
+| Table                | Table           |
+| Text                 | Text            |
+| Title                | Title           |
 
-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. 
+By applying this simple change, we observed **7% improvement** of mAP50-95 score on the DocLayNet test set. The training and inference was also faster thanks to fewer tokens used by the class names.
 
-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)
+From the mAP50-95 score, this model is far from SOTA on the DocLayNet test set (**70%**). Much smaller Yolo models (github.com/ppaanngggg/yolo-doclaynet)[https://github.com/ppaanngggg/yolo-doclaynet] have much better benchmark results (**~79%**). On the subset of scientific articles, this model performed on par with the best Yolo models (**87%**) in terms of mAP50-95.
 
-| 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
+However, after we performed some qualitative analysis (paper coming soon), we found that Florence-2 is much better at drawing bounding boxes with clean edges. Yolo models sometimes cut text in the middle or draw multiple bounding boxes on the same object. These behaviors are not seriously published in mAP50-95 but are painful to deal with in real-world use cases. When calculating the mAP scores, we had to manually set the confidence score as 1 for all Florence-2 output.
+
+We release the finetuned model weights for the community to further investigate related research topics.
  
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
+For non-CUDA environments, please check out this post for a simple patch: [https://huggingface.co/microsoft/Florence-2-base/discussions/4](https://huggingface.co/microsoft/Florence-2-base/discussions/4)
+
 ```python
 import requests
-
 from PIL import Image
 from transformers import AutoProcessor, AutoModelForCausalLM 
-
-
-model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large-ft", trust_remote_code=True)
-processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large-ft", trust_remote_code=True)
-
+model = AutoModelForCausalLM.from_pretrained("yifeihu/Florence-2-DocLayNet-Fixed", trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("yifeihu/Florence-2-DocLayNet-Fixed", 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")
-
 generated_ids = model.generate(
     input_ids=inputs["input_ids"],
     pixel_values=inputs["pixel_values"],
@@ -54,203 +62,29 @@ generated_ids = model.generate(
     num_beams=3
 )
 generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-
 parsed_answer = processor.post_process_generation(generated_text, task="<OD>", image_size=(image.width, image.height))
-
 print(parsed_answer)
-
 ```
 
-
-## 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-large-ft", trust_remote_code=True)
-processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large-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)
-```
-
-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                   |  
-  
+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.
 
 ## 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,
+  author = {Yifei Hu},
+  title = {TF-ID: Table/Figure IDentifier for academic papers},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/ai8hyf/TF-ID}},
+}
+
+@article{doclaynet2022,
+  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},  
+  doi = {10.1145/3534678.353904},
+  url = {https://arxiv.org/abs/2206.01062},
+  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
+  year = {2022}
 }
 ```