Update README.md

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}"
+    )
 ```