Update README.md

README.md

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 tags:
 - object-detection
+
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+# Model Card for detr-doc-table-detection
+ 
+# Model Details
+ 
+detr-doc-table-detection is a model trained to detect both **Bordered** and **Borderless** tables in documents, based on [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50).
+ 
+- **Developed by:** Taha Douaji
+- **Shared by [Optional]:** Taha Douaji
+- **Model type:** Object Detection 
+- **Language(s) (NLP):** More information needed
+- **License:** More information needed 
+- **Parent Model:** [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)
+- **Resources for more information:**
+    - [Model Demo Space](https://huggingface.co/spaces/trevbeers/pdf-table-extraction)
+   - [Associated Paper](https://arxiv.org/abs/2005.12872)
+ 	
 
-## Model description
-detr-doc-table-detection is a model trained to detect both Bordered and Borderless tables in documents based on [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)
 
-## Training data
+# Uses
+ 
+
+## Direct Use
+This model can be used for the task of object detection.
+ 
+## Out-of-Scope Use
+ 
+The model should not be used to intentionally create hostile or alienating environments for people. 
+ 
+# Bias, Risks, and Limitations
+ 
+ 
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+
+
+
+## Recommendations
+ 
+ 
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+# Training Details
+ 
+## Training Data
+ 
 The model was trained on ICDAR2019 Table Dataset
 
-### How to use
+ 
+# Environmental Impact
+ 
+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).
+
+ 
+# Citation
+
+ 
+**BibTeX:**
+ 
+ 
+```bibtex
+@article{DBLP:journals/corr/abs-2005-12872,
+  author    = {Nicolas Carion and
+               Francisco Massa and
+               Gabriel Synnaeve and
+               Nicolas Usunier and
+               Alexander Kirillov and
+               Sergey Zagoruyko},
+  title     = {End-to-End Object Detection with Transformers},
+  journal   = {CoRR},
+  volume    = {abs/2005.12872},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2005.12872},
+  archivePrefix = {arXiv},
+  eprint    = {2005.12872},
+  timestamp = {Thu, 28 May 2020 17:38:09 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+ 
+# Model Card Authors [optional]
+ 
+Taha Douaji in collaboration with Ezi Ozoani and the Hugging Face team
+
+
+# Model Card Contact
+ 
+More information needed
+ 
+# How to Get Started with the Model
+ 
+Use the code below to get started with the model.
+
 
 ```python
-from transformers import DetrFeatureExtractor, DetrForObjectDetection
+from transformers import DetrImageProcessor, DetrForObjectDetection
+import torch
 from PIL import Image
 import requests
-image = Image.open("Image path")
-feature_extractor = DetrFeatureExtractor.from_pretrained('TahaDouaji/detr-doc-table-detection')
-model = DetrForObjectDetection.from_pretrained('TahaDouaji/detr-doc-table-detection')
-inputs = feature_extractor(images=image, return_tensors="pt")
+
+image = Image.open("IMAGE_PATH")
+
+processor = DetrImageProcessor.from_pretrained("TahaDouaji/detr-doc-table-detection")
+model = DetrForObjectDetection.from_pretrained("TahaDouaji/detr-doc-table-detection")
+
+inputs = processor(images=image, return_tensors="pt")
 outputs = model(**inputs)
-# model predicts bounding boxes and corresponding COCO classes
-logits = outputs.logits
-bboxes = outputs.pred_boxes
+
+# convert outputs (bounding boxes and class logits) to COCO API
+# let's only keep detections with score > 0.9
+target_sizes = torch.tensor([image.size[::-1]])
+results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[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}"
+    )
 ```