Update README.md

README.md

@@ -1,12 +1,17 @@
 ---
 library_name: transformers
 tags:
-- generated_from_trainer
+- object-detection
+- Document
+- Layout
+- Analysis
+- DocLayNet
+- mAP
 datasets:
-- doc_lay_net
-model-index:
-- name: Deformable-DETR-Document-Layout-Analyzer
-  results: []
+- ds4sd/DocLayNet
+license: apache-2.0
+base_model:
+- SenseTime/deformable-detr
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -14,32 +19,99 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Deformable-DETR-Document-Layout-Analyzer
 
-This model was trained from scratch on the doc_lay_net dataset.
+This model was fine-tuned on the doc_lay_net dataset for Document Layout Analysis using full-sized DocLayNet Public Dataset.
 
 ## Model description
 
-More information needed
+The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. 
+
+The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png)
 
 ## Intended uses & limitations
 
-More information needed
+You can use the model to predict Bounding Box for 11 different Classes of Document Layout Analysis.
+
+### How to use
+
+```python
+from transformers import AutoImageProcessor, DeformableDetrForObjectDetection
+import torch
+from PIL import Image
+import requests
+
+url = "string-url-of-a-Document_page"
+image = Image.open(requests.get(url, stream=True).raw)
 
-## Training and evaluation data
+processor = AutoImageProcessor.from_pretrained("pascalrai/Deformable-DETR-Document-Layout-Analyzer")
+model = DeformableDetrForObjectDetection.from_pretrained("pascalrai/Deformable-DETR-Document-Layout-Analyzer")
 
-More information needed
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
 
-## Training procedure
+# convert outputs (bounding boxes and class logits) to COCO API
+target_sizes = torch.tensor([image.size[::-1]])
+results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.5)[0]
+
+for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+    box = [round(i, 2) for i in box.tolist()]
+    print(
+            f"Detected {model.config.id2label[label.item()]} with confidence "
+            f"{round(score.item(), 3)} at location {box}"
+    )
+```
+
+## Evaluation on DocLayNet
+
+Evaluation of the Trained model on Test Dataset of DocLayNet (On 3 epoch):
+```
+{'map': 0.6086,
+ 'map_50': 0.836,
+ 'map_75': 0.6662,
+ 'map_small': 0.3269,
+ 'map_medium': 0.501,
+ 'map_large': 0.6712,
+ 'mar_1': 0.3336,
+ 'mar_10': 0.7113,
+ 'mar_100': 0.7596,
+ 'mar_small': 0.4667,
+ 'mar_medium': 0.6717,
+ 'mar_large': 0.8436,
+ 'map_0': 0.5709,
+ 'mar_100_0': 0.7639,
+ 'map_1': 0.4685,
+ 'mar_100_1': 0.7468,
+ 'map_2': 0.5776,
+ 'mar_100_2': 0.7163,
+ 'map_3': 0.7143,
+ 'mar_100_3': 0.8251,
+ 'map_4': 0.4056,
+ 'mar_100_4': 0.533,
+ 'map_5': 0.5095,
+ 'mar_100_5': 0.6686,
+ 'map_6': 0.6826,
+ 'mar_100_6': 0.8387,
+ 'map_7': 0.5859,
+ 'mar_100_7': 0.7308,
+ 'map_8': 0.7871,
+ 'mar_100_8': 0.8852,
+ 'map_9': 0.7898,
+ 'mar_100_9': 0.8617,
+ 'map_10': 0.6034,
+ 'mar_100_10': 0.7854}
+```
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 24
-- eval_batch_size: 24
+- eff_train_batch_size: 12
+- eff_eval_batch_size: 12
 - seed: 42
 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
-- num_epochs: 2
+- num_epochs: 10
 
 ### Framework versions
 
@@ -47,3 +119,33 @@ The following hyperparameters were used during training:
 - Pytorch 2.6.0+cu124
 - Datasets 2.21.0
 - Tokenizers 0.21.0
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2010.04159,
+  doi = {10.48550/ARXIV.2010.04159},
+  url = {https://arxiv.org/abs/2010.04159}, 
+  author = {Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng},
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {Deformable DETR: Deformable Transformers for End-to-End Object Detection},
+  publisher = {arXiv},
+  year = {2020},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+@article{doclaynet2022,
+  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
+  doi = {10.1145/3534678.353904},
+  url = {https://doi.org/10.1145/3534678.3539043},
+  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
+  year = {2022},
+  isbn = {9781450393850},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
+  pages = {3743–3751},
+  numpages = {9},
+  location = {Washington DC, USA},
+  series = {KDD '22}
+}
+```