New version available. Download now!Midv-550 »
Experience a new level of gameplay, completely undetectable ghost features, and stunning UI design.
Fabric 1.20+ / Injectable 1.20+
Windows 10+
Windows 10+
New version available. Download now!Experience a new level of gameplay, completely undetectable ghost features, and stunning UI design.
We provide the perfect settings and personalisation options, allowing you to cheat your way. Whether it’s blatant, ghost, or near-legit, the choice is yours.
Prestige client is a client not only of stunning visuals and customisable modules, but it is also a client of performance. Experience high FPS and general smoothness while using Prestige.
Our client's ghost features are unmatched. With the right configuration, you’ll never be detected or noticed. Our undetectability is what makes us so popular.
Four videos demonstrating our user interface, the operation of the Minecraft client, and the process of injection. Check them out below.
Begin interacting with our client pronto. You can commence using it in an instant. Peak velocities, elite advantages, thats us.
Our client is easy to set up and easily integrated with your minecraft.
️We aim to empower individuals and players to reach their full potential.
Prestige client makes it easy to set up the client. Simply download it and inject.
Your cheat has been downloaded.
You are now ready to hack. Enjoy.
YOLOv8‑x attains the highest detection recall (98 %) while maintaining real‑time speed on mobile‑grade CPUs (≈ 150 ms per image using TensorRT). | Model | Mean IoU (all fields) | MRZ IoU | Portrait IoU | |-------|----------------------|----------|--------------| | Mask RCNN (ResNeXt‑101) | 0.78 | 0.84 | 0.71 | | DETR‑Doc (ViT‑B) | 0.74 | 0.80 | 0.68 | | Mask RCNN + Geometric Refine (baseline) | 0.82 | 0.88 | 0.75 |
Existing public benchmarks (e.g., [1], IDDoc [2], SROIE [3]) either contain a limited number of document classes, provide only coarse bounding‑box annotations, or lack realistic mobile acquisition conditions. Consequently, progress in robust MIV systems has been hindered by a mismatch between training data and real‑world deployment scenarios.
: Sequence‑to‑sequence models (CRNN [10]), Transformer‑based recognizers (SATRN [11]), and large‑scale pre‑trained vision‑language models (TrOCR [12]) have set the state‑of‑the‑art on clean scanned documents but degrade sharply on mobile captures.
: Recent works use instance‑segmentation (Mask RCNN [8]) or keypoint‑based approaches (DETR‑Doc [9]) to isolate MRZ, portrait, and signature regions.
A composite score is reported for overall ranking. 5. Experimental Results 5.1 Document Detection | Model | mAP@0.5 | Inference (ms / img) | |-------|---------|----------------------| | Faster R‑CNN (ResNet‑101) | 0.89 | 128 | | EfficientDet‑D4 | 0.92 | 71 | | YOLOv8‑x (baseline) | 0.95 | 38 |
: Object detectors such as Faster R‑CNN [5], YOLOv8 [6], and EfficientDet [7] have become de‑facto standards. However, their performance on low‑resolution, heavily distorted ID images remains under‑explored.
Data augmentation (random motion blur, brightness jitter, perspective warp) during OCR training yields a 22 % relative CER reduction. | Pipeline | E2E Accuracy | Composite Score (S) | |----------|--------------|---------------------| | YOLOv8
Geometric refinement (enforcing known field layout) reduces out‑of‑order predictions by 12 % and improves the MRZ IoU substantially. | OCR Model | Avg. CER (all fields) | MRZ CER | Name‑field CER | |-----------|----------------------|---------|----------------| | CRNN (ResNet‑34) | 0.074 | 0.058 | 0.089 | | TrOCR‑large | 0.058 | 0.042 | 0.074 | | TrOCR‑large + Data Aug (baseline) | 0.045 | 0.032 | 0.058 |
YOLOv8‑x attains the highest detection recall (98 %) while maintaining real‑time speed on mobile‑grade CPUs (≈ 150 ms per image using TensorRT). | Model | Mean IoU (all fields) | MRZ IoU | Portrait IoU | |-------|----------------------|----------|--------------| | Mask RCNN (ResNeXt‑101) | 0.78 | 0.84 | 0.71 | | DETR‑Doc (ViT‑B) | 0.74 | 0.80 | 0.68 | | Mask RCNN + Geometric Refine (baseline) | 0.82 | 0.88 | 0.75 |
Existing public benchmarks (e.g., [1], IDDoc [2], SROIE [3]) either contain a limited number of document classes, provide only coarse bounding‑box annotations, or lack realistic mobile acquisition conditions. Consequently, progress in robust MIV systems has been hindered by a mismatch between training data and real‑world deployment scenarios.
: Sequence‑to‑sequence models (CRNN [10]), Transformer‑based recognizers (SATRN [11]), and large‑scale pre‑trained vision‑language models (TrOCR [12]) have set the state‑of‑the‑art on clean scanned documents but degrade sharply on mobile captures. MIDV-550
: Recent works use instance‑segmentation (Mask RCNN [8]) or keypoint‑based approaches (DETR‑Doc [9]) to isolate MRZ, portrait, and signature regions.
A composite score is reported for overall ranking. 5. Experimental Results 5.1 Document Detection | Model | mAP@0.5 | Inference (ms / img) | |-------|---------|----------------------| | Faster R‑CNN (ResNet‑101) | 0.89 | 128 | | EfficientDet‑D4 | 0.92 | 71 | | YOLOv8‑x (baseline) | 0.95 | 38 | YOLOv8‑x attains the highest detection recall (98 %)
: Object detectors such as Faster R‑CNN [5], YOLOv8 [6], and EfficientDet [7] have become de‑facto standards. However, their performance on low‑resolution, heavily distorted ID images remains under‑explored.
Data augmentation (random motion blur, brightness jitter, perspective warp) during OCR training yields a 22 % relative CER reduction. | Pipeline | E2E Accuracy | Composite Score (S) | |----------|--------------|---------------------| | YOLOv8 Data augmentation (random motion blur
Geometric refinement (enforcing known field layout) reduces out‑of‑order predictions by 12 % and improves the MRZ IoU substantially. | OCR Model | Avg. CER (all fields) | MRZ CER | Name‑field CER | |-----------|----------------------|---------|----------------| | CRNN (ResNet‑34) | 0.074 | 0.058 | 0.089 | | TrOCR‑large | 0.058 | 0.042 | 0.074 | | TrOCR‑large + Data Aug (baseline) | 0.045 | 0.032 | 0.058 |
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