From e05068a73aa71c5155218e36c12881a40c793b3b Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Fri, 16 Dec 2022 20:06:59 +0000 Subject: [PATCH] Update README.md --- README.md | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 7069046..2b62af2 100644 --- a/README.md +++ b/README.md @@ -13,18 +13,29 @@ The model was trained on ICDAR2019 Table Dataset ### How to use ```python -from transformers import DetrFeatureExtractor, DetrForObjectDetection +from transformers import DetrImageProcessor, DetrForObjectDetection +import torch from PIL import Image +import requests -image = Image.open("Image path") +url = "http://images.cocodataset.org/val2017/000000039769.jpg" +image = Image.open(requests.get(url, stream=True).raw) -feature_extractor = DetrFeatureExtractor.from_pretrained('TahaDouaji/detr-doc-table-detection') -model = DetrForObjectDetection.from_pretrained('TahaDouaji/detr-doc-table-detection') +processor = DetrImageProcessor.from_pretrained("TahaDouaji/detr-doc-table-detection") +model = DetrForObjectDetection.from_pretrained("TahaDouaji/detr-doc-table-detection") -inputs = feature_extractor(images=image, return_tensors="pt") +inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) # 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 = feature_extractor.post_process(outputs, target_sizes=target_sizes)[0] +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}" + ) ``` \ No newline at end of file