feat: udpate readme

01_predict.ipynb

@@ -2,51 +2,56 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ultralyticsplus import YOLO, render_result\n",
-    "import cv2"
+    "from ultralyticsplus import YOLO, render_result"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...\n"
+      "Downloading https://dl.ndl.go.jp/api/iiif/1879314/R0000039/full/640,640/0/default.jpg to 'default.jpg'...\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 6.23M/6.23M [00:00<00:00, 40.2MB/s]\n"
+      "100%|██████████| 152k/152k [00:00<00:00, 6.69MB/s]\n"
      ]
     }
    ],
    "source": [
     "# load model\n",
-    "model = YOLO('nakamura196/yolov8s-layout-detection')\n",
+    "model = YOLO('best.pt')\n",
+    "# model = YOLO(\"nakamura196/yolov8s-layout-detection\")\n",
     "\n",
-    "# set model parameters\n",
-    "model.overrides['conf'] = 0.25  # NMS confidence threshold\n",
-    "model.overrides['iou'] = 0.45  # NMS IoU threshold\n",
-    "model.overrides['agnostic_nms'] = False  # NMS class-agnostic\n",
-    "model.overrides['max_det'] = 1000  # maximum number of detections per image"
+    "# set image\n",
+    "image = \"https://dl.ndl.go.jp/api/iiif/1879314/R0000039/full/640,640/0/default.jpg\"\n",
+    "\n",
+    "filename = image.split(\"/\")[-1]\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "if image.startswith(\"http\"):\n",
+    "\n",
+    "    os.remove(filename)\n",
+    "\n",
+    "# perform inference\n",
+    "results = model.predict(image, conf=0.25, verbose=False)\n",
+    "\n",
+    "# observe results\n",
+    "render = render_result(model=model, image=image, result=results[0])\n",
+    "render.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

README.md

@@ -1,19 +1,55 @@
 ---
 tags:
-- ultralyticsplus
-- yolov8
-- ultralytics
+  - ultralyticsplus
+  - yolov8
+  - ultralytics
 library_name: ultralytics
 library_version: 8.0.43
 license: cc-by-4.0
 model-index:
-- name: nakamura196/yolov8s-layout-detection
-  results:
-  - task:
-      type: object-detection
-    metrics:
-    - type: precision
-      value: 0.671
-      name: mAP50-90(B)
+  - name: nakamura196/yolov8s-layout-detection
+    results:
+      - task:
+          type: object-detection
+        metrics:
+          - type: precision
+            value: 0.671
+            name: mAP50-95(B)
+          - type: precision
+            value: 0.947
+            name: mAP50(B)
 pipeline_tag: object-detection
----
+---
+
+# YOLOv8 model to detect layout of 寛政重修諸家譜
+
+## Inference
+
+### Supported Labels
+
+```python
+["h1", "h2", "head", "long", "other", "page", "sode", "text", "vol"]
+```
+
+### How to use
+
+```bash
+pip install ultralyticsplus
+```
+
+```python
+from ultralyticsplus import YOLO, render_result
+
+# load model
+model = YOLO('best.pt')
+
+# set image
+image = "https://dl.ndl.go.jp/api/iiif/1879314/R0000039/full/640,640/0/default.jpg"
+
+# perform inference
+results = model.predict(image, verbose=False)
+
+# observe results
+render = render_result(model=model, image=image, result=results[0])
+render.show()
+```

config.json

@@ -4,5 +4,6 @@
     "ultralyticsplus_version": "0.0.24",
     "ultralytics_version": "8.0.23",
     "model_type": "v8",
-    "score_map50": 0.20933
+    "mAP@50": 0.947,
+    "mAp@50-95": 0.671
 }

requirements.txt

@@ -1,2 +1 @@
-ultralyticsplus
-ultralytics
+ultralyticsplus