Add model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -15,4 +15,68 @@ pipeline_tag: image-segmentation
 tags:
 - text-line-detection
 - text-region-detection
----
+- document-analysis
+- historical-documents
+- handwritten-text
+- rf-detr
+- instance-segmentation
+---
+
+# RF-DETR Seg-Preview: Historical Document Instance Segmentation
+
+This model is trained to detect and segment text lines and text regions from historical handwritten documents spanning from the 16th to the 20th century.
+
+## Model Description
+
+RF-DETR Seg-Preview is an instance segmentation model based on the RF-DETR architecture. It predicts:
+- Bounding boxes for text elements
+- Class labels (text_region or text_line)
+- Instance segmentation masks
+
+### Classes
+
+The model detects two classes:
+- **text_region** (index: 1) - Larger regions of text content
+- **text_line** (index: 2) - Individual lines of text
+
+## Training Data
+
+The model was trained on historical handwritten documents with the following data distribution:
+- **Training set**: 11,495 images
+- **Validation set**: 2,711 images
+- **Test set**: 2,340 images
+
+## Performance Metrics
+
+### Validation Set Performance
+
+| Class | mAP@50:95 | mAP@50 | Precision | Recall |
+|-------|-----------|--------|-----------|--------|
+| text_region | 0.822 | 0.963 | 0.949 | 0.940 |
+| text_line | 0.621 | 0.936 | 0.957 | 0.940 |
+| **Overall** | **0.721** | **0.950** | **0.953** | **0.940** |
+
+### Test Set Performance
+
+| Class | mAP@50:95 | mAP@50 | Precision | Recall |
+|-------|-----------|--------|-----------|--------|
+| text_region | 0.822 | 0.959 | 0.949 | 0.940 |
+| text_line | 0.688 | 0.955 | 0.978 | 0.940 |
+| **Overall** | **0.755** | **0.957** | **0.964** | **0.940** |
+
+## Training Metrics
+
+![Training Metrics](metrics_plot.png)
+
+## Use Cases
+
+This model is particularly suitable for:
+- Text line detection for OCR preprocessing
+- Document digitization projects involving historical manuscripts
+- Historical document understanding and analysis
+
+## Limitations
+
+- The model is specifically trained on historical handwritten documents (16th-20th century)
+- Performance may vary on modern printed documents or documents outside the training distribution
+- Performance depends on image quality and document preservation state

results.json

@@ -0,0 +1,53 @@
+{
+  "class_map": {
+    "valid": [
+      {
+        "class": "text_region",
+        "map@50:95": 0.8215820411796119,
+        "map@50": 0.9632309591819967,
+        "precision": 0.9491835242505484,
+        "recall": 0.9400000000000001
+      },
+      {
+        "class": "text_line",
+        "map@50:95": 0.6208253598742672,
+        "map@50": 0.9359343947812567,
+        "precision": 0.9570004372001946,
+        "recall": 0.9400000000000001
+      },
+      {
+        "class": "all",
+        "map@50:95": 0.7212037005269396,
+        "map@50": 0.9495826769816268,
+        "precision": 0.9530919807253715,
+        "recall": 0.9400000000000001
+      }
+    ],
+    "test": [
+      {
+        "class": "text_region",
+        "map@50:95": 0.8223769265055245,
+        "map@50": 0.9588798259965857,
+        "precision": 0.9490022172949002,
+        "recall": 0.9400000000000001
+      },
+      {
+        "class": "text_line",
+        "map@50:95": 0.6879523939826719,
+        "map@50": 0.954879362766605,
+        "precision": 0.9780753334901381,
+        "recall": 0.9400000000000001
+      },
+      {
+        "class": "all",
+        "map@50:95": 0.7551646602440983,
+        "map@50": 0.9568795943815954,
+        "precision": 0.9635387753925191,
+        "recall": 0.9400000000000001
+      }
+    ]
+  },
+  "map": 0.9495826769816268,
+  "precision": 0.9530919807253715,
+  "recall": 0.9400000000000001
+}

rfdetr_text_seg_model_202510.pth

@@ -0,0 +1,3 @@
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+size 135572819