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README.md

@@ -1,11 +1,49 @@
-# Usage
+---
+base_model: ragavsachdeva/magiv3
+---
+
+# Magiv3
+
+A model for comics understanding.
+
+## DISCLAIMER
+
+This is a model duplicated from [ragavsachdeva](https://huggingface.co/ragavsachdeva/magiv3). Please refer to the original model or its [paper](https://arxiv.org/abs/2503.23344) for more information.
+
+## Usage
 
 ```python
-model = AutoModelForCausalLM.from_pretrained("ragavsachdeva/magiv3", torch_dtype=torch.float16, trust_remote_code=True).cuda().eval()
-processor = AutoProcessor.from_pretrained("ragavsachdeva/magiv3", trust_remote_code=True)
+from transformers import AutoModelForCausalLM, AutoProcessor
+from PIL import Image
+import numpy as np
+import torch
+
+def load_image(path):
+    with open(path, "rb") as file:
+        image = Image.open(file).convert("L").convert("RGB")
+        image = np.array(image)
+
+    return image
+
+images = ["01.jpg", "02.jpg"]
+images = [load_image(image) for image in images]
+
+# All panels from images, not provided by model
+panels = splitImagesToPanels(images)
+
+# The generated captions for each panels, not provided by model
+captions = generateCaptionsFromPanels(panels) 
+
+model = AutoModelForCausalLM.from_pretrained('mrfish233/magiv3', torch_dtype=torch.float16, trust_remote_code=True).cuda().eval()
+processor = AutoProcessor.from_pretrained('mrfish233/magiv3', trust_remote_code=True)
+
+with torch.no_grad():
+    # detections from 
+    detections = model.predict_detections_and_associations(images, processor)
 
-model.predict_detections_and_associations(images, processor)
-model.predict_ocr(images, processor)
-model.predict_character_grounding(images, captions, processor)
+    # OCR for each page
+    ocr_results = model.predict_ocr(images, processor)
 
+    # get character grounding with captions provided
+    grounding = model.predict_character_grounding(panels, captions, processor)
 ```