Update README.md

README.md

@@ -8,3 +8,53 @@ library_name: transformers
 
 This repository contains a fine-tuned version of the Table Transformer model, specifically adapted for detecting tables in IFRS (International Financial Reporting Standards) PDFs. The model is based on the Table Transformer architecture, which is designed to extract tables from unstructured documents such as PDFs and images.
 
+# Usage 
+
+```python
+from transformers import DetrForObjectDetection, DetrImageProcessor
+from PIL import Image
+import torch
+
+# Load the image processor and model
+# DetrImageProcessor is used to preprocess the images before feeding them to the model
+image_processor = DetrImageProcessor()
+
+# Load the pre-trained TableTransformer model for object detection
+# This model is specifically trained for detecting tables in IFRS documents
+model = TableTransformerForObjectDetection.from_pretrained(
+    "apkonsta/table-transformer-detection-ifrs",
+)
+
+# Prepare the image
+# Open the image file and convert it to RGB format
+image = Image.open("path/to/your/ifrs_pdf_page.png").convert("RGB")
+
+# Table detection threshold
+# Set a threshold for detecting tables; only detections with a confidence score above this threshold will be considered
+TD_th = 0.5
+
+# Preprocess the image using the image processor
+# The image is encoded into a format that the model can understand
+encoding = image_processor(image, return_tensors="pt")
+
+# Perform inference without computing gradients (saves memory and computations)
+with torch.no_grad():
+    outputs = model(**encoding)
+
+# Get the probabilities for each detected object
+# The softmax function is applied to the logits to get probabilities
+probas = outputs.logits.softmax(-1)[0, :, :-1]
+
+# Keep only the detections with a confidence score above the threshold
+keep = probas.max(-1).values > TD_th
+
+# Get the target sizes for post-processing
+# The target sizes are the dimensions of the original image
+target_sizes = torch.tensor(image.size[::-1]).unsqueeze(0)
+
+# Post-process the model outputs to get the final bounding boxes
+# The bounding boxes are scaled back to the original image size
+postprocessed_outputs = image_processor.post_process(outputs, target_sizes)
+bboxes_scaled = postprocessed_outputs[0]["boxes"][keep]
+```
+