Add all 6 models, metrics, plots — Run 3 results

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+metrics/y8l_1024/training_curve.png filter=lfs diff=lfs merge=lfs -text
+metrics/y8m_1024/training_curve.png filter=lfs diff=lfs merge=lfs -text
+metrics/y8n_1024/training_curve.png filter=lfs diff=lfs merge=lfs -text
+metrics/y8s_1024/training_curve.png filter=lfs diff=lfs merge=lfs -text
+metrics/y8s_640/training_curve.png filter=lfs diff=lfs merge=lfs -text
+metrics/y8s_768/training_curve.png filter=lfs diff=lfs merge=lfs -text
+plots/comparisons/detection_examples_collage.png filter=lfs diff=lfs merge=lfs -text
+plots/comparisons/precision_recall_scatter.png filter=lfs diff=lfs merge=lfs -text
+plots/training_curves/all_training_curves.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,93 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- object-detection
+- document-analysis
+- yolov8
+- stamps
+- signatures
+language:
+- ru
+- kk
+metrics:
+- map
+pipeline_tag: object-detection
+---
+
+# YOLOv8 Document Inspector
+
+Automatic detection of **signatures**, **stamps**, and **QR codes**
+in business documents (Russian/Kazakh).
+
+## Results (Run 3 — Conservative Augmentation)
+
+| Model | mAP@50 | mAP@50-95 | AP Signature | AP Stamp | ms/img | Params |
+|-------|--------|-----------|--------------|----------|--------|--------|
+| **y8n_1024** ⭐ | **0.881** | **0.669** | 0.355 | 0.982 | **237** | 3.2M |
+| y8m_1024 | 0.872 | 0.649 | 0.378 | 0.920 | 308 | 25.9M |
+| y8l_1024 | 0.858 | 0.642 | 0.302 | 0.981 | 316 | 43.7M |
+| y8s_1024 | 0.831 | 0.630 | 0.354 | 0.905 | 249 | 11.2M |
+| y8s_640  | 0.823 | 0.615 | 0.333 | 0.897 | 225 | 11.2M |
+| y8s_768  | 0.836 | 0.596 | 0.317 | 0.875 | 233 | 11.2M |
+
+## Speed vs Accuracy
+
+![Pareto](plots/comparisons/pareto_speed_accuracy.png)
+
+## Performance Comparison
+
+![Bar Chart](plots/comparisons/final_metrics_bar_chart.png)
+
+## Training Curves
+
+![Curves](plots/training_curves/all_training_curves.png)
+
+## Precision vs Recall
+
+![Scatter](plots/comparisons/precision_recall_scatter.png)
+
+## Detection Examples
+
+![Detections](plots/comparisons/detection_examples_collage.png)
+
+## Key Findings
+
+| Run | Augmentation | mAP@50-95 |
+|-----|-------------|-----------|
+| Run 1 | None | 0.650 |
+| Run 2 | Mosaic + copy-paste | 0.252 ❌ |
+| **Run 3** | **Conservative** | **0.669** ✅ |
+
+**y8n beats y8l:** 3.2M params model outperforms 43.7M params model
+in both speed (237ms vs 316ms) and accuracy (0.669 vs 0.642).
+
+## Usage
+
+```python
+from ultralytics import YOLO
+
+model = YOLO("models/y8n_1024/best.pt")
+results = model.predict("document.jpg", imgsz=1024, conf=0.25)
+results[0].show()
+```
+
+## Training Config (Run 3)
+
+```python
+model.train(
+    imgsz=1024, epochs=150, patience=30,
+    degrees=3.0, translate=0.05, scale=0.2,
+    hsv_v=0.2, hsv_s=0.1,
+    mosaic=0.0,   # OFF — harmful for documents
+    fliplr=0.0,   # OFF — text would mirror
+    flipud=0.0,
+)
+```
+
+## Dataset
+
+Provided during a hackathon competition.
+Contains annotated Russian/Kazakh business documents.
+Dataset is not publicly available due to privacy constraints.
+
+Source code: [github.com/AlihanSDev/digital-inspector](https://github.com/AlihanSDev/digital-inspector)

augmentation_comparison.csv

@@ -0,0 +1,4 @@
+Run,Best_Model,mAP50_best,mAP50-95_best,mAP_excl_QR,Augmentation,Notes
+Run1_No_Aug,y8s_1024,0.81,0.65,,None,Baseline — best overall performance
+Run2_Heavy_Aug,y8s_1024,0.32,0.252,0.367,"mosaic=0.5,copy_paste=0.1",Mosaic harmful for documents (2.5x degradation)
+Run3_Conservative_Aug,y8n_1024,0.8810804207624694,0.6686309516247201,0.6686309516247202,"degrees=3,hsv_v=0.2,mosaic=0",Conservative aug — TBD

master_metrics_comparison.csv

@@ -0,0 +1,7 @@
+Model,Architecture,Resolution,Batch_Size,Best_Epoch,Precision,Recall,mAP50,mAP50-95,AP_Signature,AP_Stamp,AP_QR,Inference_ms,Train_Box_Loss,Val_Box_Loss,Params_M,Params_per_mAP,mAP_excl_QR
+y8n_1024,YOLOV8N,1024,16,48,0.8434193689257665,0.8235294117647058,0.8810804207624694,0.6686309516247201,0.3552619032494403,0.982,,236.71049230238972,0.67127,0.68205,3.2,4.785898696768755,0.6686309516247202
+y8m_1024,YOLOV8M,1024,8,109,0.9495827274593112,0.7529411764705882,0.8718857005063901,0.6491656859925887,0.3782063719851777,0.920125,,307.9606785493739,0.50563,0.65969,25.9,39.89736450779638,0.6491656859925888
+y8l_1024,YOLOV8L,1024,8,53,0.8463207376548569,0.7693780090222759,0.8584909925856841,0.6415920599661721,0.30218411993234434,0.9810000000000001,,316.36700910680435,0.87282,0.8514,43.7,68.1118154771181,0.6415920599661722
+y8s_1024,YOLOV8S,1024,16,89,0.9854746232580573,0.7647058823529411,0.8310901583710407,0.6296196865472865,0.35390603976124,0.9053333333333333,,248.7977673025692,0.52658,0.52007,11.2,17.788516209552864,0.6296196865472867
+y8s_640,YOLOV8S,640,16,44,0.8771017386663466,0.8027068850622838,0.8234999999999999,0.614836111111111,0.3331166666666666,0.8965555555555556,,224.98705807854148,0.65515,0.66632,11.2,18.21623648578438,0.6148361111111111
+y8s_768,YOLOV8S,768,16,86,0.8496492883173588,0.7955523707685128,0.83647167538344,0.5959686751172837,0.31663937043658746,0.8752979797979797,,232.9273644615622,0.58834,0.56733,11.2,18.792934037675543,0.5959686751172836

metrics/y8l_1024/per_class_ap.csv

@@ -0,0 +1,4 @@
+Class,Class_ID,AP
+signature,0,0.30218411993234434
+stamp,1,0.9810000000000001
+qr,2,

metrics/y8l_1024/summary.csv

@@ -0,0 +1,2 @@
+Model,Architecture,Resolution,Batch_Size,Best_Epoch,Precision,Recall,mAP50,mAP50-95,AP_Signature,AP_Stamp,AP_QR,Inference_ms,Train_Box_Loss,Val_Box_Loss,Params_M,Params_per_mAP
+y8l_1024,YOLOV8L,1024,8,53,0.8463207376548569,0.7693780090222759,0.8584909925856841,0.6415920599661721,0.30218411993234434,0.9810000000000001,,316.36700910680435,0.87282,0.8514,43.7,68.1118154771181

metrics/y8l_1024/training_history.csv

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