add initiate model

README.md

@@ -1,3 +1,65 @@
 ---
-license: mit
+tags:
+- ultralyticsplus
+- yolov5
+- ultralytics
+- yolo
+- vision
+- object-detection
+- pytorch
+- awesome-yolov8-models
+- indonesia
+- layout detector
+
+model-index:
+- name: hermanshid/yolo-layout-detector
+  results:
+  - task:
+      type: object-detection
+
+    metrics:
+      - type: precision  # since mAP@0.5 is not available on hf.co/metrics
+        value: 0.979  # min: 0.0 - max: 1.0
+        name: mAP@0.5(box)
+inference: false
 ---
+
+# YOLOv5 for Layout Detection
+
+
+## Dataset
+Dataset available in [kaggle](https://www.kaggle.com/datasets/hermansugiharto/book-layout)
+## Supported Labels
+```python
+["caption", "chart", "image", "image_caption", "table", "table_caption", "text", "title"]
+```
+
+## How to use
+- Install library
+
+`pip install yolov5==7.0.5 torch`
+
+## Load model and perform prediction
+```python
+import yolov5
+from PIL import Image
+
+model = yolov5.load(models_id)
+
+model.overrides['conf'] = 0.25  # NMS confidence threshold
+model.overrides['iou'] = 0.45  # NMS IoU threshold
+model.overrides['max_det'] = 1000  # maximum number of detections per image
+
+# set image
+image = 'https://huggingface.co/spaces/hermanshid/yolo-layout-detector-space/raw/main/test_images/example1.jpg'
+
+# perform inference
+results = model.predict(image)
+
+# observe results
+print(results[0].boxes)
+render = render_result(model=model, image=image, result=results[0])
+render.show()
+
+```
+

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fee4a91b957e40d09472fb7ba7326aa496e20653250b0b13cc0ca17075e9a650
+size 18503238

config.json

@@ -0,0 +1,5 @@
+{
+    "input_size": 640,
+    "task": "object-detection",
+    "model_type": "yolosv5"
+}

preprocessor_config.json

@@ -0,0 +1,18 @@
+{
+    "do_normalize": true,
+    "do_resize": true,
+    "feature_extractor_type": "YolosFeatureExtractor",
+    "format": "coco_detection",
+    "image_mean": [
+        0.485,
+        0.456,
+        0.406
+    ],
+    "image_std": [
+        0.229,
+        0.224,
+        0.225
+    ],
+    "max_size": 1333,
+    "size": 640
+}