diff --git a/Karussell/Inference/model/mobilenetv2-220420-152335-karusel.pkl.epoch470 b/Karussell/Inference/model/mobilenetv2-220601-183947-karusel.pkl.epoch500
similarity index 57%
rename from Karussell/Inference/model/mobilenetv2-220420-152335-karusel.pkl.epoch470
rename to Karussell/Inference/model/mobilenetv2-220601-183947-karusel.pkl.epoch500
index a897bc9..bc3772c 100644
Binary files a/Karussell/Inference/model/mobilenetv2-220420-152335-karusel.pkl.epoch470 and b/Karussell/Inference/model/mobilenetv2-220601-183947-karusel.pkl.epoch500 differ
diff --git a/Karussell/Supervisely/.ipynb_checkpoints/Bbox from Pose-checkpoint.ipynb b/Karussell/Supervisely/.ipynb_checkpoints/Bbox from Pose-checkpoint.ipynb
new file mode 100644
index 0000000..b6245c5
--- /dev/null
+++ b/Karussell/Supervisely/.ipynb_checkpoints/Bbox from Pose-checkpoint.ipynb	
@@ -0,0 +1,217 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "07bdc0da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "import numpy as np\n",
+    "from tqdm.notebook import tqdm\n",
+    "path_dataset = r'D:\\downloads\\Nurburg-karussel'\n",
+    "from SuperviselyKeypointsGUI.SuperviselyKeypointsGUI import *\n",
+    "\n",
+    "\n",
+    "keypoints_3d_path = r'SuperviselyKeypointsGUI\\karussel_24kps.csv'\n",
+    "keypoints_3d = pd.read_csv(keypoints_3d_path, index_col=0).astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ca890aa0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_image(id):\n",
+    "    for row in coco['images']:\n",
+    "        if row['id'] == id:\n",
+    "            return row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b8135031",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ltrb_from_cloud(cloud_2d, imgSize, expansion=0.1):\n",
+    "    height, width = imgSize\n",
+    "    ltrb = np.round((cloud_2d[:, 0].min(), cloud_2d[:, 1].min(),\n",
+    "                     cloud_2d[:, 0].max(), cloud_2d[:, 1].max())).astype(int)\n",
+    "    \n",
+    "    if expansion > 0:\n",
+    "        dx = np.round((ltrb[2]-ltrb[0])*expansion/2)\n",
+    "        dy = np.round((ltrb[3]-ltrb[1])*expansion/2)\n",
+    "        ltrb += np.array([-dx, -2*dy, dx, dy], dtype=int)\n",
+    "        \n",
+    "    ltrb[[0,2]] = np.clip(ltrb[[0,2]], 0, width)\n",
+    "    ltrb[[1,3]] = np.clip(ltrb[[1,3]], 0, height)\n",
+    "    \n",
+    "    return ltrb\n",
+    "\n",
+    "def ltrb2ltwh(ltrb):\n",
+    "    return np.array([ltrb[0], ltrb[1], ltrb[2]-ltrb[0], ltrb[3]-ltrb[1]], dtype=int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "79762c08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(os.path.join(path_dataset, 'karusel_COCO.json'), 'r') as file:\n",
+    "    coco = json.load(file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1cfe0e2",
+   "metadata": {},
+   "source": [
+    "### Bboxes from pose"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9440b54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for obj in tqdm(coco['annotations']):\n",
+    "    keypoints_2d = np.array(obj['keypoints']).reshape((-1, 3))[:, :2]\n",
+    "    id, width, height, file_name = find_image(obj['image_id']).values()\n",
+    "    \n",
+    "    bbox_ltrb = ltrb_from_cloud(keypoints_2d, (height, width), 0.4)\n",
+    "    bbox_ltwh = ltrb2ltwh(bbox_ltrb).tolist()\n",
+    "    obj['bbox'] = bbox_ltwh\n",
+    "    obj['area'] = bbox_ltwh[2]*bbox_ltwh[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfcb675b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# with open(os.path.join(path_dataset, 'karusel_COCO.json'), 'w') as file:\n",
+    "#     json.dump(coco, file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8ff698d",
+   "metadata": {},
+   "source": [
+    "### Split COCO json to train/val"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3aec7a2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "import copy\n",
+    "\n",
+    "def split_coco_json(coco, test_size=0.2, random_state=0):\n",
+    "    \n",
+    "    train_idx, test_idx = train_test_split([i['id'] for i in coco['images']],\n",
+    "                                           test_size=test_size, random_state=random_state)\n",
+    "\n",
+    "\n",
+    "    train = copy.deepcopy(coco)\n",
+    "    test = copy.deepcopy(coco)\n",
+    "\n",
+    "    test['images'] = [x for x in coco['images'] if x['id'] in test_idx]\n",
+    "    train['images'] = [x for x in coco['images'] if x['id'] in train_idx]\n",
+    "\n",
+    "    test['annotations'] = [x for x in coco['annotations'] if x['image_id'] in test_idx]\n",
+    "    train['annotations'] = [x for x in coco['annotations'] if x['image_id'] in train_idx]\n",
+    "    return train, test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cdd1a3c",
+   "metadata": {},
+   "source": [
+    "### Create new splited dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aafd54fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train, test = split_coco_json(coco, 0.1, random_state=777)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6af98fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_path_images = [os.path.join(path_dataset, 'images', 'img', x['file_name']) for x in test['images']]\n",
+    "train_path_images = [os.path.join(path_dataset, 'images', 'img', x['file_name']) for x in train['images']]\n",
+    "\n",
+    "import shutil\n",
+    "path_new_dataset = r'C:\\Users\\Kir\\Jupiter\\Nurburg\\OpenPifPaf\\Training\\Karusel_dataset'\n",
+    "\n",
+    "path_train_img = os.path.join(path_new_dataset, 'images', 'train')\n",
+    "path_test_img = os.path.join(path_new_dataset, 'images', 'val')\n",
+    "path_ann = os.path.join(path_new_dataset, 'annotations')\n",
+    "\n",
+    "os.makedirs(path_train_img, exist_ok=True)\n",
+    "os.makedirs(path_test_img, exist_ok=True)\n",
+    "os.makedirs(path_ann, exist_ok=True)\n",
+    "\n",
+    "with open(os.path.join(path_ann, 'train.json'), 'w') as file:\n",
+    "    json.dump(train, file)\n",
+    "    \n",
+    "with open(os.path.join(path_ann, 'val.json'), 'w') as file:\n",
+    "    json.dump(test, file)\n",
+    "\n",
+    "for path in train_path_images:\n",
+    "    shutil.copy(path, path_train_img)\n",
+    "\n",
+    "for path in test_path_images:\n",
+    "    shutil.copy(path, path_test_img)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Karussell/Supervisely/.ipynb_checkpoints/ToCOCO-checkpoint.ipynb b/Karussell/Supervisely/.ipynb_checkpoints/ToCOCO-checkpoint.ipynb
new file mode 100644
index 0000000..96a1fc9
--- /dev/null
+++ b/Karussell/Supervisely/.ipynb_checkpoints/ToCOCO-checkpoint.ipynb
@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "283f6e9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "import json\n",
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "path_dataset = r'D:\\downloads\\Nurburg-karussel'\n",
+    "with open(os.path.join(path_dataset, 'meta.json'), 'r') as j:\n",
+    "    meta = json.load(j)\n",
+    "\n",
+    "imgs = glob.glob(path_dataset + '\\\\images\\\\img\\\\*', recursive=True)\n",
+    "anns = glob.glob(path_dataset + '\\\\images\\\\ann\\\\*', recursive=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7af948d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def label2hash(meta_json, last):\n",
+    "    for clss in meta_json['classes']:\n",
+    "        if clss['title'] == last['classTitle']:\n",
+    "            meta_nodes = clss['geometry_config']['nodes']\n",
+    "            label2hash = {}\n",
+    "            for name in meta_nodes:\n",
+    "                label2hash[meta_nodes[name]['label']] = name\n",
+    "    return label2hash"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7ea0771e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def annotations(meta_json, obj):\n",
+    "    nodes = obj['nodes']\n",
+    "    keypoints_2d = pd.DataFrame(columns=['x', 'y'])\n",
+    "\n",
+    "    for i in range(1, len(nodes)+1):\n",
+    "        keypoints_2d.loc[i] = nodes[label2hash(meta_json, obj)[str(i)]]['loc']\n",
+    "\n",
+    "    keypoints_2d['v'] = 2\n",
+    "    keypoints_2d = keypoints_2d.astype(float).round().astype(int)\n",
+    "    return keypoints_2d[:24]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "26b6abf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ann_json(keypoints, img_id, obj):\n",
+    "    \n",
+    "    annotation = {\n",
+    "        \"id\": obj['id'],\n",
+    "        \"segmentation\": [],\n",
+    "        \"num_keypoints\": len(keypoints),\n",
+    "        \"area\": 0,\n",
+    "        \"iscrowd\": 0,\n",
+    "        \"image_id\": img_id,\n",
+    "        \"bbox\": [],\n",
+    "        \"category_id\": 1,\n",
+    "        \"keypoints\": keypoints.values.flatten().tolist()}\n",
+    "\n",
+    "    return annotation\n",
+    "\n",
+    "def img_json(ann, name, id):\n",
+    "    height, width =  ann['size'].values()\n",
+    "    image = {\n",
+    "        \"id\": id,\n",
+    "        \"width\": width,\n",
+    "        \"height\": height,\n",
+    "        \"file_name\": name,\n",
+    "    }\n",
+    "    return image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "155be42d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ann_img_list(anns, imgs, meta):\n",
+    "    annotations_list = []\n",
+    "    image_list = []\n",
+    "    for i in tqdm(range(len(anns))):\n",
+    "\n",
+    "        with open(anns[i], 'r') as j:\n",
+    "            ann = json.load(j)\n",
+    "        \n",
+    "        image_name = os.path.basename(anns[i])[:-5]\n",
+    "        image = img_json(ann, image_name, i)\n",
+    "        image_list.append(image)\n",
+    "\n",
+    "        for obj in ann['objects']:\n",
+    "            keypoints = annotations(meta, obj)\n",
+    "            annotations_list.append(ann_json(keypoints, i, obj))\n",
+    "    return image_list, annotations_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6593f677",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def COCO(image_list, annotations_list):\n",
+    "    coco = {\n",
+    "\n",
+    "        \"info\": {\n",
+    "            \"description\": \"karusel Dataset\", \"version\": \"2.0\"\n",
+    "        },\n",
+    "\n",
+    "        \"categories\": [\n",
+    "            {\n",
+    "                \"supercategory\": \"NurburgRing\",\n",
+    "                \"id\": 1,\n",
+    "                \"name\": \"karusel\",\n",
+    "                \"keypoints\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
+    "                              12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24],\n",
+    "                \"skeleton\": [\n",
+    "                    [1, 2],[2, 3],[3, 4],[4, 5],[5, 6],[6, 7],[7, 8],[8, 9],[9, 10],[10, 11],\n",
+    "                    [11, 12],[12, 13],[13, 14],[14, 15],[15, 16],[16, 17],[17, 18],[18, 19],[19, 20],\n",
+    "                    [20, 21],[21, 22],[22, 23],[23, 24],[24, 1],[24, 3],[1, 5]\n",
+    "                ]\n",
+    "            }\n",
+    "        ]\n",
+    "    }\n",
+    "\n",
+    "    coco['images'] = image_list\n",
+    "    coco['annotations'] = annotations_list\n",
+    "    return coco"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "38880d13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96021bfc91ce425dbef57ae348321005",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/448 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coco_json = COCO(*ann_img_list(anns, imgs, meta))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "314565c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(os.path.join(path_dataset, 'karusel_COCO.json'), 'w') as file:\n",
+    "    json.dump(coco_json, file)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Karussell/Supervisely/Bbox from Pose.ipynb b/Karussell/Supervisely/Bbox from Pose.ipynb
index 312c500..ebfb09a 100644
--- a/Karussell/Supervisely/Bbox from Pose.ipynb	
+++ b/Karussell/Supervisely/Bbox from Pose.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "07bdc0da",
    "metadata": {},
    "outputs": [],
@@ -11,7 +11,7 @@
     "import json\n",
     "import numpy as np\n",
     "from tqdm.notebook import tqdm\n",
-    "path_dataset = r'D:\\karusel'\n",
+    "path_dataset = r'D:\\downloads\\Nurburg-karussel'\n",
     "from SuperviselyKeypointsGUI.SuperviselyKeypointsGUI import *\n",
     "\n",
     "\n",
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "ca890aa0",
    "metadata": {},
    "outputs": [],
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "b8135031",
    "metadata": {},
    "outputs": [],
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "79762c08",
    "metadata": {},
    "outputs": [],
@@ -79,10 +79,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "f9440b54",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36c51c895f4e4f6b806c2af9e7bc14b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/448 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "for obj in tqdm(coco['annotations']):\n",
     "    keypoints_2d = np.array(obj['keypoints']).reshape((-1, 3))[:, :2]\n",
@@ -96,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "dfcb675b",
    "metadata": {},
    "outputs": [],
@@ -115,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "3aec7a2b",
    "metadata": {},
    "outputs": [],
@@ -150,17 +165,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "aafd54fb",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train, test = split_coco_json(coco, 0.1, random_state=777)"
+    "train, test = split_coco_json(coco, 0.1, random_state=77)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "f6af98fc",
    "metadata": {},
    "outputs": [],
@@ -169,7 +184,7 @@
     "train_path_images = [os.path.join(path_dataset, 'images', 'img', x['file_name']) for x in train['images']]\n",
     "\n",
     "import shutil\n",
-    "path_new_dataset = r'C:\\Users\\Kir\\Jupiter\\Nurburg\\OpenPifPaf\\Training\\Karusel_dataset'\n",
+    "path_new_dataset = r'C:\\Users\\Kir\\Jupiter\\Drivecast\\place6D_Nurburgring\\Karussell\\Training\\Karusel_dataset_v2'\n",
     "\n",
     "path_train_img = os.path.join(path_new_dataset, 'images', 'train')\n",
     "path_test_img = os.path.join(path_new_dataset, 'images', 'val')\n",
diff --git a/Karussell/Supervisely/SuperviselyKeypointsGUI/__pycache__/SuperviselyKeypointsGUI.cpython-39.pyc b/Karussell/Supervisely/SuperviselyKeypointsGUI/__pycache__/SuperviselyKeypointsGUI.cpython-39.pyc
new file mode 100644
index 0000000..7966bae
Binary files /dev/null and b/Karussell/Supervisely/SuperviselyKeypointsGUI/__pycache__/SuperviselyKeypointsGUI.cpython-39.pyc differ
diff --git a/Karussell/Supervisely/ToCOCO.ipynb b/Karussell/Supervisely/ToCOCO.ipynb
index 378f8cf..96a1fc9 100644
--- a/Karussell/Supervisely/ToCOCO.ipynb
+++ b/Karussell/Supervisely/ToCOCO.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "283f6e9c",
    "metadata": {},
    "outputs": [],
@@ -14,7 +14,7 @@
     "import json\n",
     "from tqdm.notebook import tqdm\n",
     "\n",
-    "path_dataset = r'D:\\karusel'\n",
+    "path_dataset = r'D:\\downloads\\Nurburg-karussel'\n",
     "with open(os.path.join(path_dataset, 'meta.json'), 'r') as j:\n",
     "    meta = json.load(j)\n",
     "\n",
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "7af948d4",
    "metadata": {},
    "outputs": [],
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "7ea0771e",
    "metadata": {},
    "outputs": [],
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "26b6abf4",
    "metadata": {},
    "outputs": [],
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "155be42d",
    "metadata": {},
    "outputs": [],
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "6593f677",
    "metadata": {},
    "outputs": [],
@@ -127,7 +127,7 @@
     "    coco = {\n",
     "\n",
     "        \"info\": {\n",
-    "            \"description\": \"karusel Dataset\", \"version\": \"1.0\"\n",
+    "            \"description\": \"karusel Dataset\", \"version\": \"2.0\"\n",
     "        },\n",
     "\n",
     "        \"categories\": [\n",
@@ -153,17 +153,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "38880d13",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96021bfc91ce425dbef57ae348321005",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/448 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "coco_json = COCO(*ann_img_list(anns, imgs, meta))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "314565c2",
    "metadata": {},
    "outputs": [],
diff --git a/Karussell/Training/Karusel_dataset-COCO/annotations/train.json b/Karussell/Training/Karusel_dataset-COCO/annotations/train.json
deleted file mode 100644
index ca75e80..0000000
--- a/Karussell/Training/Karusel_dataset-COCO/annotations/train.json
+++ /dev/null
@@ -1 +0,0 @@
-{"info": {"description": "karusel Dataset", "version": "1.0"}, "categories": [{"supercategory": "NurburgRing", "id": 1, "name": "karusel", "keypoints": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], "skeleton": [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13], [13, 14], [14, 15], [15, 16], [16, 17], [17, 18], [18, 19], [19, 20], [20, 21], [21, 22], [22, 23], [23, 24], [24, 1], [24, 3], [1, 5]]}], "images": [{"id": 0, "width": 1920, "height": 1080, "file_name": "frame_00000_03.png"}, {"id": 1, "width": 1920, "height": 1080, "file_name": "frame_00000_04.png"}, {"id": 2, "width": 1920, "height": 1080, "file_name": "frame_00000_05.png"}, {"id": 3, "width": 1920, "height": 1080, "file_name": "frame_00025_03.png"}, {"id": 4, "width": 1920, "height": 1080, "file_name": "frame_00025_04.png"}, {"id": 7, "width": 1920, "height": 1080, "file_name": "frame_00050_01.png"}, {"id": 8, "width": 1920, "height": 1080, "file_name": "frame_00050_12.png"}, {"id": 9, "width": 1920, "height": 1080, "file_name": "frame_00075_01.png"}, {"id": 10, "width": 1920, "height": 1080, "file_name": "frame_00075_03.png"}, {"id": 13, "width": 1920, "height": 1080, "file_name": "frame_00075_12.png"}, {"id": 14, "width": 1920, "height": 1080, "file_name": "frame_00100.png"}, {"id": 15, "width": 1920, "height": 1080, "file_name": "frame_00100_01.png"}, {"id": 16, "width": 1920, "height": 1080, "file_name": "frame_00100_02.png"}, {"id": 17, "width": 1920, "height": 1080, "file_name": "frame_00100_03.png"}, {"id": 18, "width": 1920, "height": 1080, "file_name": "frame_00100_05.png"}, {"id": 19, "width": 1920, "height": 1080, "file_name": "frame_00100_12.png"}, {"id": 20, "width": 1920, "height": 1080, "file_name": "frame_00125.png"}, {"id": 21, "width": 1920, "height": 1080, "file_name": "frame_00125_01.png"}, {"id": 22, "width": 1920, "height": 1080, "file_name": "frame_00125_02.png"}, {"id": 23, "width": 1920, "height": 1080, "file_name": "frame_00125_11.png"}, {"id": 24, "width": 1920, "height": 1080, "file_name": "frame_00125_12.png"}, {"id": 25, "width": 1920, "height": 1080, "file_name": "frame_00150.png"}, {"id": 26, "width": 1920, "height": 1080, "file_name": "frame_00150_01.png"}, {"id": 27, "width": 1920, "height": 1080, "file_name": "frame_00150_02.png"}, {"id": 28, "width": 1920, "height": 1080, "file_name": "frame_00150_04.png"}, {"id": 29, "width": 1920, "height": 1080, "file_name": "frame_00150_11.png"}, {"id": 30, "width": 1920, "height": 1080, "file_name": "frame_00150_12.png"}, {"id": 31, "width": 1920, "height": 1080, "file_name": "frame_00175.png"}, {"id": 32, "width": 1920, "height": 1080, "file_name": "frame_00175_01.png"}, {"id": 33, "width": 1920, "height": 1080, "file_name": "frame_00175_02.png"}, {"id": 34, "width": 1920, "height": 1080, "file_name": "frame_00175_11.png"}, {"id": 36, "width": 1920, "height": 1080, "file_name": "frame_00200.png"}, {"id": 37, 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diff --git a/Karussell/Training/Karusel_dataset-COCO/annotations/val.json b/Karussell/Training/Karusel_dataset-COCO/annotations/val.json
deleted file mode 100644
index 2b3ecac..0000000
--- a/Karussell/Training/Karusel_dataset-COCO/annotations/val.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/Karussell/Training/Karusel_dataset_v2/annotations/train.json b/Karussell/Training/Karusel_dataset_v2/annotations/train.json
new file mode 100644
index 0000000..bb17e90
--- /dev/null
+++ b/Karussell/Training/Karusel_dataset_v2/annotations/train.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/Karussell/Training/Karusel_dataset_v2/annotations/val.json b/Karussell/Training/Karusel_dataset_v2/annotations/val.json
new file mode 100644
index 0000000..d8d55e3
--- /dev/null
+++ b/Karussell/Training/Karusel_dataset_v2/annotations/val.json
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rename from Karussell/Training/Karusel_dataset-COCO/images/train/frame_00575_03.png
rename to Karussell/Training/Karusel_dataset_v2/images/val/frame_00575_03.png
diff --git a/Karussell/Training/Karusel_dataset-COCO/images/train/frame_00600_03.png b/Karussell/Training/Karusel_dataset_v2/images/val/frame_00600_03.png
similarity index 100%
rename from Karussell/Training/Karusel_dataset-COCO/images/train/frame_00600_03.png
rename to Karussell/Training/Karusel_dataset_v2/images/val/frame_00600_03.png
diff --git a/Karussell/Training/Karusel_dataset-COCO/images/train/frame_01200.png b/Karussell/Training/Karusel_dataset_v2/images/val/frame_01200.png
similarity index 100%
rename from Karussell/Training/Karusel_dataset-COCO/images/train/frame_01200.png
rename to Karussell/Training/Karusel_dataset_v2/images/val/frame_01200.png
diff --git a/Karussell/Training/Karusel_dataset-COCO/images/train/frame_01425.png b/Karussell/Training/Karusel_dataset_v2/images/val/frame_01425.png
similarity index 100%
rename from Karussell/Training/Karusel_dataset-COCO/images/train/frame_01425.png
rename to Karussell/Training/Karusel_dataset_v2/images/val/frame_01425.png
diff --git a/Karussell/Training/Karusel_dataset-COCO/images/val/frame_01675.png b/Karussell/Training/Karusel_dataset_v2/images/val/frame_01675.png
similarity index 100%
rename from Karussell/Training/Karusel_dataset-COCO/images/val/frame_01675.png
rename to Karussell/Training/Karusel_dataset_v2/images/val/frame_01675.png
diff --git a/Karussell/Training/Karusel_dataset-COCO/images/train/frame_01700.png b/Karussell/Training/Karusel_dataset_v2/images/val/frame_01700.png
similarity index 100%
rename from Karussell/Training/Karusel_dataset-COCO/images/train/frame_01700.png
rename to Karussell/Training/Karusel_dataset_v2/images/val/frame_01700.png
diff --git a/Karussell/Training/openpifpaf_formulaE/__init__.py b/Karussell/Training/openpifpaf_formulaE/__init__.py
new file mode 100644
index 0000000..9e3387a
--- /dev/null
+++ b/Karussell/Training/openpifpaf_formulaE/__init__.py
@@ -0,0 +1,16 @@
+import openpifpaf
+
+from . import formulaE_kp
+
+
+def register():
+    openpifpaf.DATAMODULES['formulaE'] = formulaE_kp.FormulaE_Kp
+    openpifpaf.CHECKPOINT_URLS['shufflenetv2k16-apollo-24'] = \
+        "http://github.com/DuncanZauss/openpifpaf_assets/releases/" \
+        "download/v0.1.0/shufflenetv2k16-201113-135121-apollo.pkl.epoch290"
+    openpifpaf.CHECKPOINT_URLS['shufflenetv2k16-apollo-66'] = \
+        "http://github.com/DuncanZauss/openpifpaf_assets/releases/" \
+        "download/v0.1.0/sk16_apollo_66kp.pkl"
+    openpifpaf.CHECKPOINT_URLS['shufflenetv2k30-apollo-66'] = \
+        "http://github.com/DuncanZauss/openpifpaf_assets/releases/" \
+        "download/v0.1.0/sk30_apollo_66kp.pkl"
diff --git a/Karussell/Training/openpifpaf_formulaE/__pycache__/__init__.cpython-38.pyc b/Karussell/Training/openpifpaf_formulaE/__pycache__/__init__.cpython-38.pyc
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diff --git a/Karussell/Training/openpifpaf_formulaE/constants.py b/Karussell/Training/openpifpaf_formulaE/constants.py
new file mode 100644
index 0000000..60e14fe
--- /dev/null
+++ b/Karussell/Training/openpifpaf_formulaE/constants.py
@@ -0,0 +1,294 @@
+import os
+
+import numpy as np
+try:
+    import matplotlib.cm as mplcm
+    from matplotlib.animation import FuncAnimation
+    from mpl_toolkits.mplot3d import Axes3D
+except ImportError:
+    pass
+
+import openpifpaf
+
+CAR_KEYPOINTS_24 = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12',
+                    '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24']
+
+CAR_SKELETON_24 = [[1, 2],
+                 [1, 9],
+                 [1, 12],
+                 [1, 18],
+                 [1, 17],
+                 [1, 8],
+                 [12, 18],
+                 [2, 9],
+                 [17, 7],
+                 [8, 7],
+                 [2, 10],
+                 [9, 10],
+                 [18, 19],
+                 [12, 19],
+                 [19, 13],
+                 [10, 3],
+                 [13, 14],
+                 [3, 4],
+                 [4, 11],
+                 [14, 20],
+                 [20, 15],
+                 [20, 16],
+                 [11, 6],
+                 [11, 5],
+                 [5, 23],
+                 [15, 22],
+                 [22, 23],
+                 [22, 21],
+                 [21, 24],
+                 [23, 24]]
+
+CAR_CATEGORIES_24 = ['gen 2']
+
+
+CAR_SCORE_WEIGHTS_24 = [5,3,1,1,2,0.5,5,1,3,3,3,3,1,1,2,0.5,1,3,3,3,0.5,0.5,0.5,0.5]
+
+CAR_SIGMAS_24 = [0.05] * len(CAR_KEYPOINTS_24)
+
+CAR_POSE_24 = np.array([[ 3.16610e-02,  1.26484e+02, -7.90370e+00],
+       [ 3.70193e+01,  1.00028e+02, -1.67113e+01],
+       [ 4.41759e+01,  7.71484e+00, -1.67119e+01],
+       [ 4.40693e+01, -6.23645e+01, -1.68886e+01],
+       [ 4.49225e+01, -1.14833e+02,  2.25995e+01],
+       [ 4.25990e+01, -4.79440e+01,  4.99305e+00],
+       [ 1.97800e-03, -1.82719e+01,  3.22667e+01],
+       [ 1.99550e+01,  2.51571e+01,  1.61091e+01],
+       [ 3.63039e+01,  1.03593e+02, -3.92045e+00],
+       [ 4.54021e+01,  7.21575e+01, -4.14269e+00],
+       [ 4.53684e+01, -9.26003e+01, -4.14376e+00],
+       [-3.69654e+01,  1.00044e+02, -1.67116e+01],
+       [-4.41584e+01,  7.73299e+00, -1.67122e+01],
+       [-4.40825e+01, -6.23463e+01, -1.68889e+01],
+       [-4.49579e+01, -1.14815e+02,  2.25992e+01],
+       [-4.26056e+01, -4.79265e+01,  4.99274e+00],
+       [-1.99304e+01,  2.51653e+01,  1.61090e+01],
+       [-3.63039e+01,  1.03593e+02, -3.92045e+00],
+       [-4.53610e+01,  7.21734e+01, -4.14302e+00],
+       [-4.53947e+01, -9.25844e+01, -4.14409e+00],
+       [-2.73159e+01, -1.26582e+02, -2.83129e+00],
+       [-2.68627e+01, -1.15836e+02,  2.03855e+01],
+       [ 2.68268e+01, -1.15847e+02,  2.03857e+01],
+       [ 2.72785e+01, -1.26593e+02, -2.83109e+00]])
+
+CAR_POSE_24[:, [0, 1, 2]] = CAR_POSE_24[:, [0, 2, 1]]
+
+HFLIP_24 = {
+    '1': '1',
+    '2': '12',
+    '3': '13',
+    '4': '14',
+    '5': '15',
+    '6': '16',
+    '7': '7',
+    '8': '17',
+    '9': '18',
+    '10': '19',
+    '11': '20',
+    '12': '2',
+    '13': '3',
+    '14': '4',
+    '15': '5',
+    '16': '6',
+    '17': '8',
+    '18': '9',
+    '19': '10',
+    '20': '11',
+    '21': '24',
+    '22': '23',
+    '23': '22',
+    '24': '21'
+}
+
+training_weights_local_centrality = [
+    0.890968488270775,
+    0.716506138617812,
+    1.05674590410869,
+    0.764774195768455,
+    0.637682585483328,
+    0.686680807728366,
+    0.955422595797394,
+    0.936714585642375,
+    1.34823795445326,
+    1.38308992581967,
+    1.32689945125819,
+    1.38838655605483,
+    1.18980184904613,
+    1.02584355494795,
+    0.90969156732068,
+    1.24732068576104,
+    1.11338768064342,
+    0.933815217550391,
+    0.852297518872114,
+    1.04167641424727,
+    1.01668968075247,
+    1.34625964088011,
+    0.911796331039028,
+    0.866206536337413,
+    1.55957820407853,
+    0.730844382675724,
+    0.651138644197359,
+    0.758018559633786,
+    1.31842501396691,
+    1.32186116654782,
+    0.744347016851606,
+    0.636390683664723,
+    0.715244950821949,
+    1.63122349407032,
+    0.849835699185461,
+    0.910488007220499,
+    1.44244151650561,
+    1.14150437331681,
+    1.19808610191343,
+    0.960186788642886,
+    1.05023623286937,
+    1.19761709710598,
+    1.3872216313401,
+    1.01256700741214,
+    1.1167909667759,
+    1.27893496336199,
+    1.54475684725655,
+    1.40343733870633,
+    1.45552060866114,
+    1.47264222155031,
+    0.970060423999993,
+    0.944450314768933,
+    0.623987071240172,
+    0.5745237907704,
+    0.66890646050993,
+    0.978411632994504,
+    0.587396395188292,
+    0.76307999741129,
+    0.609793563449648,
+    0.67983566494545,
+    0.685883538168462,
+    0.753587600664775,
+    0.770335133588157,
+    0.764713638033368,
+    0.792364155965385,
+    0.796435233566833
+]
+
+
+def get_constants(num_kps):
+    if num_kps == 24:
+        CAR_POSE_24[:, 2] = 2.0
+        return [CAR_KEYPOINTS_24, CAR_SKELETON_24, HFLIP_24, CAR_SIGMAS_24,
+                CAR_POSE_24, CAR_CATEGORIES_24, CAR_SCORE_WEIGHTS_24]
+    if num_kps == 66:
+        CAR_POSE_66[:, 2] = 2.0
+        return [CAR_KEYPOINTS_66, CAR_SKELETON_66, HFLIP_66, CAR_SIGMAS_66,
+                CAR_POSE_66, CAR_CATEGORIES_66, CAR_SCORE_WEIGHTS_66]
+    # using no if-elif-else construction due to pylint no-else-return error
+    raise Exception("Only poses with 24 or 66 keypoints are available.")
+
+
+def draw_ann(ann, *, keypoint_painter, filename=None, margin=0.5, aspect=None, **kwargs):
+    from openpifpaf import show  # pylint: disable=import-outside-toplevel
+
+    bbox = ann.bbox()
+    xlim = bbox[0] - margin, bbox[0] + bbox[2] + margin
+    ylim = bbox[1] - margin, bbox[1] + bbox[3] + margin
+    if aspect == 'equal':
+        fig_w = 5.0
+    else:
+        fig_w = 5.0 / (ylim[1] - ylim[0]) * (xlim[1] - xlim[0])
+
+    with show.canvas(filename, figsize=(fig_w, 5), nomargin=True, **kwargs) as ax:
+        ax.set_axis_off()
+        ax.set_xlim(*xlim)
+        ax.set_ylim(*ylim)
+
+        if aspect is not None:
+            ax.set_aspect(aspect)
+
+        keypoint_painter.annotation(ax, ann)
+
+
+def draw_skeletons(pose, sigmas, skel, kps, scr_weights):
+    from openpifpaf.annotation import Annotation  # pylint: disable=import-outside-toplevel
+    from openpifpaf import show  # pylint: disable=import-outside-toplevel
+
+    scale = np.sqrt(
+        (np.max(pose[:, 0]) - np.min(pose[:, 0]))
+        * (np.max(pose[:, 1]) - np.min(pose[:, 1]))
+    )
+
+    show.KeypointPainter.show_joint_scales = True
+    keypoint_painter = show.KeypointPainter()
+    ann = Annotation(keypoints=kps, skeleton=skel, score_weights=scr_weights)
+    ann.set(pose, np.array(sigmas) * scale)
+    os.makedirs('docs', exist_ok=True)
+    draw_ann(ann, filename='docs/skeleton_car.png', keypoint_painter=keypoint_painter)
+
+
+def plot3d_red(ax_2D, p3d, skeleton):
+    skeleton = [(bone[0] - 1, bone[1] - 1) for bone in skeleton]
+
+    rot_p90_x = np.array([[1, 0, 0], [0, 0, 1], [0, 1, 0]])
+    p3d = p3d @ rot_p90_x
+
+    fig = ax_2D.get_figure()
+    ax = Axes3D(fig, auto_add_to_figure=False)
+    fig.add_axes(ax)
+    ax.set_axis_off()
+    ax_2D.set_axis_off()
+
+    ax.view_init(azim=-90, elev=20)
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    ax.set_zlabel('Z')
+    max_range = np.array([p3d[:, 0].max() - p3d[:, 0].min(),
+                          p3d[:, 1].max() - p3d[:, 1].min(),
+                          p3d[:, 2].max() - p3d[:, 2].min()]).max() / 2.0
+    mid_x = (p3d[:, 0].max() + p3d[:, 0].min()) * 0.5
+    mid_y = (p3d[:, 1].max() + p3d[:, 1].min()) * 0.5
+    mid_z = (p3d[:, 2].max() + p3d[:, 2].min()) * 0.5
+
+    ax.set_xlim(mid_x - max_range, mid_x + max_range)
+    ax.set_ylim(mid_y - max_range, mid_y + max_range)
+    ax.set_zlim(mid_z - max_range, mid_z + max_range)  # pylint: disable=no-member
+
+    for ci, bone in enumerate(skeleton):
+        c = mplcm.get_cmap('tab20')((ci % 20 + 0.05) / 20)  # Same coloring as Pifpaf preds
+        ax.plot(p3d[bone, 0], p3d[bone, 1], p3d[bone, 2], color=c)
+
+    def animate(i):
+        ax.view_init(elev=10., azim=i)
+        return fig
+
+    return FuncAnimation(fig, animate, frames=360, interval=100)
+
+
+def print_associations():
+    print("\nAssociations of the car skeleton with 24 keypoints")
+    for j1, j2 in CAR_SKELETON_24:
+        print(CAR_KEYPOINTS_24[j1 - 1], '-', CAR_KEYPOINTS_24[j2 - 1])
+    print("\nAssociations of the car skeleton with 66 keypoints")
+    for j1, j2 in CAR_SKELETON_66:
+        print(CAR_KEYPOINTS_66[j1 - 1], '-', CAR_KEYPOINTS_66[j2 - 1])
+
+
+def main():
+#     print_associations()
+# =============================================================================
+#     draw_skeletons(CAR_POSE_24, sigmas = CAR_SIGMAS_24, skel = CAR_SKELETON_24,
+#                    kps = CAR_KEYPOINTS_24, scr_weights = CAR_SCORE_WEIGHTS_24)
+#     draw_skeletons(CAR_POSE_66, sigmas = CAR_SIGMAS_66, skel = CAR_SKELETON_66,
+#                    kps = CAR_KEYPOINTS_66, scr_weights = CAR_SCORE_WEIGHTS_66)
+# =============================================================================
+#     with openpifpaf.show.Canvas.blank(nomargin=True) as ax_2D:
+#         anim_66 = plot3d_red(ax_2D, CAR_POSE_66, CAR_SKELETON_66)
+#         anim_66.save('./CAR_66_Pose.gif', fps=30)
+    with openpifpaf.show.Canvas.blank(nomargin=True) as ax_2D:
+        anim_24 = plot3d_red(ax_2D, CAR_POSE_24, CAR_SKELETON_24)
+        anim_24.save('./CAR_24_Pose.gif', fps=30)
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/Karussell/Training/openpifpaf_formulaE/formulaE_kp.py b/Karussell/Training/openpifpaf_formulaE/formulaE_kp.py
new file mode 100644
index 0000000..46577fe
--- /dev/null
+++ b/Karussell/Training/openpifpaf_formulaE/formulaE_kp.py
@@ -0,0 +1,332 @@
+"""
+Interface for custom data.
+
+This module handles datasets and is the class that you need to inherit from for your custom dataset.
+This class gives you all the handles so that you can train with a new –dataset=mydataset.
+The particular configuration of keypoints and skeleton is specified in the headmeta instances
+"""
+
+
+import argparse
+import torch
+import numpy as np
+try:
+    from pycocotools.coco import COCO
+except ImportError:
+    COCO = None
+
+from openpifpaf.datasets import DataModule
+from openpifpaf import encoder, headmeta, metric, transforms
+from openpifpaf.datasets import collate_images_anns_meta, collate_images_targets_meta
+from openpifpaf.plugins.coco import CocoDataset as CocoLoader
+
+from .constants import get_constants, training_weights_local_centrality
+from .metrics import MeanPixelError
+
+
+class FormulaE_Kp(DataModule):
+    """
+    DataModule for the Formula E Dataset.
+    """
+
+    train_annotations = 'FormulaE-dataset-COCO/annotations/train.json'
+    val_annotations = 'FormulaE-dataset-COCO/annotations/val.json'
+    eval_annotations = val_annotations
+    train_image_dir = 'FormulaE-dataset-COCO/images/train/'
+    val_image_dir = 'FormulaE-dataset-COCO/images/val/'
+    eval_image_dir = val_image_dir
+
+    n_images = None
+    square_edge = 513
+    extended_scale = False
+    orientation_invariant = 0.0
+    blur = 0.0
+    augmentation = True
+    rescale_images = 1.0
+    upsample_stride = 1
+    min_kp_anns = 1
+    b_min = 1  # 1 pixel
+
+    eval_annotation_filter = True
+    eval_long_edge = 0  # set to zero to deactivate rescaling
+    eval_orientation_invariant = 0.0
+    eval_extended_scale = False
+
+    def __init__(self):
+        super().__init__()
+        if self.weights is not None:
+            caf_weights = []
+            for bone in self.CAR_SKELETON:
+                caf_weights.append(max(self.weights[bone[0] - 1],
+                                       self.weights[bone[1] - 1]))
+            w_np = np.array(caf_weights)
+            caf_weights = list(w_np / np.sum(w_np) * len(caf_weights))
+        else:
+            caf_weights = None
+        cif = headmeta.Cif('cif', 'apollo',
+                           keypoints=self.CAR_KEYPOINTS,
+                           sigmas=self.CAR_SIGMAS,
+                           pose=self.CAR_POSE,
+                           draw_skeleton=self.CAR_SKELETON,
+                           score_weights=self.CAR_SCORE_WEIGHTS,
+                           training_weights=self.weights)
+        caf = headmeta.Caf('caf', 'apollo',
+                           keypoints=self.CAR_KEYPOINTS,
+                           sigmas=self.CAR_SIGMAS,
+                           pose=self.CAR_POSE,
+                           skeleton=self.CAR_SKELETON,
+                           training_weights=caf_weights)
+
+        cif.upsample_stride = self.upsample_stride
+        caf.upsample_stride = self.upsample_stride
+        self.head_metas = [cif, caf]
+
+    @classmethod
+    def cli(cls, parser: argparse.ArgumentParser):
+        group = parser.add_argument_group('data module Apollo')
+
+        group.add_argument('--formula-train-annotations',
+                           default=cls.train_annotations)
+        group.add_argument('--formula-val-annotations',
+                           default=cls.val_annotations)
+        group.add_argument('--formula-train-image-dir',
+                           default=cls.train_image_dir)
+        group.add_argument('--formula-val-image-dir',
+                           default=cls.val_image_dir)
+
+        group.add_argument('--formula-square-edge',
+                           default=cls.square_edge, type=int,
+                           help='square edge of input images')
+        assert not cls.extended_scale
+        group.add_argument('--formula-extended-scale',
+                           default=False, action='store_true',
+                           help='augment with an extended scale range')
+        group.add_argument('--formula-orientation-invariant',
+                           default=cls.orientation_invariant, type=float,
+                           help='augment with random orientations')
+        group.add_argument('--formula-blur',
+                           default=cls.blur, type=float,
+                           help='augment with blur')
+        assert cls.augmentation
+        group.add_argument('--formula-no-augmentation',
+                           dest='formula_augmentation',
+                           default=True, action='store_false',
+                           help='do not apply data augmentation')
+        group.add_argument('--formula-rescale-images',
+                           default=cls.rescale_images, type=float,
+                           help='overall rescale factor for images')
+        group.add_argument('--formula-upsample',
+                           default=cls.upsample_stride, type=int,
+                           help='head upsample stride')
+        group.add_argument('--formula-min-kp-anns',
+                           default=cls.min_kp_anns, type=int,
+                           help='filter images with fewer keypoint annotations')
+        group.add_argument('--formula-bmin',
+                           default=cls.b_min, type=int,
+                           help='b minimum in pixels')
+        group.add_argument('--formula-apply-local-centrality-weights',
+                           dest='formula_apply_local_centrality',
+                           default=False, action='store_true',
+                           help='Weigh the CIF and CAF head during training.')
+
+        # evaluation
+        assert cls.eval_annotation_filter
+        group.add_argument('--formula-no-eval-annotation-filter',
+                           dest='formula_eval_annotation_filter',
+                           default=True, action='store_false')
+        group.add_argument('--formula-eval-long-edge', default=cls.eval_long_edge, type=int,
+                           help='set to zero to deactivate rescaling')
+        assert not cls.eval_extended_scale
+        group.add_argument('--formula-eval-extended-scale', default=False, action='store_true')
+        group.add_argument('--formula-eval-orientation-invariant',
+                           default=cls.eval_orientation_invariant, type=float)
+        group.add_argument('--formula-use-24-kps', default=False, action='store_true',
+                           help=('The ApolloCar3D dataset can '
+                                 'be trained with 24 or 66 kps. If you want to train a model '
+                                 'with 24 kps activate this flag. Change the annotations '
+                                 'path to the json files with 24 kps.'))
+
+    @classmethod
+    def configure(cls, args: argparse.Namespace):
+        # extract global information
+        cls.debug = args.debug
+        cls.pin_memory = args.pin_memory
+
+        # Apollo specific
+        cls.train_annotations = args.formula_train_annotations
+        cls.val_annotations = args.formula_val_annotations
+        cls.eval_annotations = cls.val_annotations
+        cls.train_image_dir = args.formula_train_image_dir
+        cls.val_image_dir = args.formula_val_image_dir
+        cls.eval_image_dir = cls.val_image_dir
+
+        cls.square_edge = args.formula_square_edge
+        cls.extended_scale = args.formula_extended_scale
+        cls.orientation_invariant = args.formula_orientation_invariant
+        cls.blur = args.formula_blur
+        cls.augmentation = args.formula_augmentation  # loaded by the dest name
+        cls.rescale_images = args.formula_rescale_images
+        cls.upsample_stride = args.formula_upsample
+        cls.min_kp_anns = args.formula_min_kp_anns
+        cls.b_min = args.formula_bmin
+        if args.formula_use_24_kps:
+            (cls.CAR_KEYPOINTS, cls.CAR_SKELETON, cls.HFLIP, cls.CAR_SIGMAS, cls.CAR_POSE,
+             cls.CAR_CATEGORIES, cls.CAR_SCORE_WEIGHTS) = get_constants(24)
+        else:
+            (cls.CAR_KEYPOINTS, cls.CAR_SKELETON, cls.HFLIP, cls.CAR_SIGMAS, cls.CAR_POSE,
+             cls.CAR_CATEGORIES, cls.CAR_SCORE_WEIGHTS) = get_constants(66)
+        # evaluation
+        cls.eval_annotation_filter = args.formula_eval_annotation_filter
+        cls.eval_long_edge = args.formula_eval_long_edge
+        cls.eval_orientation_invariant = args.formula_eval_orientation_invariant
+        cls.eval_extended_scale = args.formula_eval_extended_scale
+        if args.formula_apply_local_centrality:
+            if args.formula_use_24_kps:
+                raise Exception("Applying local centrality weights only works with 66 kps.")
+            cls.weights = training_weights_local_centrality
+        else:
+            cls.weights = None
+
+    def _preprocess(self):
+        encoders = (encoder.Cif(self.head_metas[0], bmin=self.b_min),
+                    encoder.Caf(self.head_metas[1], bmin=self.b_min))
+
+        if not self.augmentation:
+            return transforms.Compose([
+                transforms.NormalizeAnnotations(),
+                transforms.RescaleAbsolute(self.square_edge),
+                transforms.CenterPad(self.square_edge),
+                transforms.EVAL_TRANSFORM,
+                transforms.Encoders(encoders),
+            ])
+
+        if self.extended_scale:
+            rescale_t = transforms.RescaleRelative(
+                scale_range=(0.2 * self.rescale_images,
+                             2.0 * self.rescale_images),
+                power_law=True, stretch_range=(0.75, 1.33))
+        else:
+            rescale_t = transforms.RescaleRelative(
+                scale_range=(0.33 * self.rescale_images,
+                             1.33 * self.rescale_images),
+                power_law=True, stretch_range=(0.75, 1.33))
+
+        return transforms.Compose([
+            transforms.NormalizeAnnotations(),
+            # transforms.AnnotationJitter(),
+            transforms.RandomApply(transforms.HFlip(self.CAR_KEYPOINTS, self.HFLIP), 0.5),
+            rescale_t,
+            transforms.RandomApply(transforms.Blur(), self.blur),
+            transforms.RandomChoice(
+                [transforms.RotateBy90(),
+                 transforms.RotateUniform(30.0)],
+                [self.orientation_invariant, 0.2],
+            ),
+            transforms.Crop(self.square_edge, use_area_of_interest=True),
+            transforms.CenterPad(self.square_edge),
+            transforms.MinSize(min_side=32.0),
+            transforms.TRAIN_TRANSFORM,
+            transforms.Encoders(encoders),
+        ])
+
+    def train_loader(self):
+        train_data = CocoLoader(
+            image_dir=self.train_image_dir,
+            ann_file=self.train_annotations,
+            preprocess=self._preprocess(),
+            annotation_filter=True,
+            min_kp_anns=self.min_kp_anns,
+            category_ids=[1],
+        )
+        return torch.utils.data.DataLoader(
+            train_data, batch_size=self.batch_size, shuffle=not self.debug,
+            pin_memory=self.pin_memory, num_workers=self.loader_workers, drop_last=True,
+            collate_fn=collate_images_targets_meta)
+
+    def val_loader(self):
+        val_data = CocoLoader(
+            image_dir=self.val_image_dir,
+            ann_file=self.val_annotations,
+            preprocess=self._preprocess(),
+            annotation_filter=True,
+            min_kp_anns=self.min_kp_anns,
+            category_ids=[1],
+        )
+        return torch.utils.data.DataLoader(
+            val_data, batch_size=self.batch_size, shuffle=False,
+            pin_memory=self.pin_memory, num_workers=self.loader_workers, drop_last=True,
+            collate_fn=collate_images_targets_meta)
+
+    @classmethod
+    def common_eval_preprocess(cls):
+        rescale_t = None
+        if cls.eval_extended_scale:
+            assert cls.eval_long_edge
+            rescale_t = [
+                transforms.DeterministicEqualChoice([
+                    transforms.RescaleAbsolute(cls.eval_long_edge),
+                    transforms.RescaleAbsolute((cls.eval_long_edge - 1) // 2 + 1),
+                ], salt=1)
+            ]
+        elif cls.eval_long_edge:
+            rescale_t = transforms.RescaleAbsolute(cls.eval_long_edge)
+
+        if cls.batch_size == 1:
+            padding_t = transforms.CenterPadTight(16)
+        else:
+            assert cls.eval_long_edge
+            padding_t = transforms.CenterPad(cls.eval_long_edge)
+
+        orientation_t = None
+        if cls.eval_orientation_invariant:
+            orientation_t = transforms.DeterministicEqualChoice([
+                None,
+                transforms.RotateBy90(fixed_angle=90),
+                transforms.RotateBy90(fixed_angle=180),
+                transforms.RotateBy90(fixed_angle=270),
+            ], salt=3)
+
+        return [
+            transforms.NormalizeAnnotations(),
+            rescale_t,
+            padding_t,
+            orientation_t,
+        ]
+
+    def _eval_preprocess(self):
+        return transforms.Compose([
+            *self.common_eval_preprocess(),
+            transforms.ToAnnotations([
+                transforms.ToKpAnnotations(
+                    self.CAR_CATEGORIES,
+                    keypoints_by_category={1: self.head_metas[0].keypoints},
+                    skeleton_by_category={1: self.head_metas[1].skeleton},
+                ),
+                transforms.ToCrowdAnnotations(self.CAR_CATEGORIES),
+            ]),
+            transforms.EVAL_TRANSFORM,
+        ])
+
+    def eval_loader(self):
+        eval_data = CocoLoader(
+            image_dir=self.eval_image_dir,
+            ann_file=self.eval_annotations,
+            preprocess=self._eval_preprocess(),
+            annotation_filter=self.eval_annotation_filter,
+            min_kp_anns=self.min_kp_anns if self.eval_annotation_filter else 0,
+            category_ids=[1] if self.eval_annotation_filter else [],
+        )
+        return torch.utils.data.DataLoader(
+            eval_data, batch_size=self.batch_size, shuffle=False,
+            pin_memory=self.pin_memory, num_workers=self.loader_workers, drop_last=False,
+            collate_fn=collate_images_anns_meta)
+
+# TODO: make sure that 24kp flag is activated when evaluating a 24kp model
+    def metrics(self):
+        return [metric.Coco(
+            COCO(self.eval_annotations),
+            max_per_image=20,
+            category_ids=[1],
+            iou_type='keypoints',
+            keypoint_oks_sigmas=self.CAR_SIGMAS
+        ), MeanPixelError()]
diff --git a/Karussell/Training/openpifpaf_formulaE/metrics.py b/Karussell/Training/openpifpaf_formulaE/metrics.py
new file mode 100644
index 0000000..e797b85
--- /dev/null
+++ b/Karussell/Training/openpifpaf_formulaE/metrics.py
@@ -0,0 +1,131 @@
+import logging
+
+import numpy as np
+
+from openpifpaf.metric.base import Base
+from openpifpaf.annotation import Annotation
+
+try:
+    import scipy
+except ImportError:
+    scipy = None
+
+LOG = logging.getLogger(__name__)
+
+
+class MeanPixelError(Base):
+    """
+    Calculate mean pixel error and detection rate for a given image
+    and category in an "all-vs-all setting"
+    """
+    predictions = []
+    image_ids = []
+    errors = []  # mean pixel errors
+    detections = []  # detection rate
+    errors_scaled = []  # mean pixel errors
+    detections_scaled = []  # detection rate
+    px_ref = 368  # CPM crop size in pixels
+
+    def accumulate(self, predictions, image_meta, *, ground_truth=None):
+        errors = []
+        detections = []
+        errors_scaled = []
+        detections_scaled = []
+
+        # Filter ground-truth
+        for annotation in ground_truth:
+            if not isinstance(annotation, Annotation):
+                continue
+            indices_gt = np.nonzero(annotation.data[:, 2] > 1.0)
+            if indices_gt[0].size <= 3:
+                continue
+            gts = annotation.data[indices_gt, 0:2].squeeze()
+            width = float(annotation.fixed_bbox[2])
+            height = float(annotation.fixed_bbox[3])
+            scale = np.array([self.px_ref / width, self.px_ref / height]).reshape(1, 2)
+
+            # Evaluate each keypoint
+            for idx, gt in zip(indices_gt[0], gts):
+                preds = np.array([p.data[idx] for p in predictions]).reshape(-1, 3)[:, 0:2]
+                if preds.size <= 0:
+                    continue
+                i = np.argmin(np.linalg.norm(preds - gt, axis=1))
+                dist = preds[i:i + 1] - gt
+                dist_scaled = dist * scale
+                d = float(np.linalg.norm(dist, axis=1))
+                d_scaled = float(np.linalg.norm(dist_scaled, axis=1))
+
+                # Prediction correct if error less than 10 pixels
+                if d < 10:
+                    errors.append(d)
+                    detections.append(1)
+                else:
+                    detections.append(0)
+                if d_scaled < 10:
+                    errors_scaled.append(d)
+                    detections_scaled.append(1)
+                else:
+                    detections_scaled.append(0)
+
+        # Stats for a single image
+        mpe = average(errors)
+        mpe_scaled = average(errors_scaled)
+        det_rate = 100 * average(detections)
+        det_rate_scaled = 100 * average(detections_scaled)
+        LOG.info('Mean Pixel Error (scaled): %s (%s)    Det. Rate (scaled): %s (%s)',
+                 str(mpe)[:4], str(mpe_scaled)[:4], str(det_rate)[:4], str(det_rate_scaled)[:4])
+
+        # Accumulate stats
+        self.errors.extend(errors)
+        self.detections.extend(detections)
+        self.errors_scaled.extend(errors_scaled)
+        self.detections_scaled.extend(detections_scaled)
+
+    def write_predictions(self, filename, *, additional_data=None):
+        raise NotImplementedError
+
+    def stats(self):
+        mpe = average(self.errors)
+        mpe_scaled = average(self.errors_scaled)
+        det_rate = 100 * average(self.detections)
+        det_rate_scaled = 100 * average(self.detections_scaled)
+        LOG.info('Final Results: \nMean Pixel Error [scaled] : %f [%f] '
+                 '\nDetection Rate [scaled]: %f [%f]',
+                 mpe, mpe_scaled, det_rate, det_rate_scaled)
+        data = {
+            'stats': [mpe, mpe_scaled, det_rate, det_rate_scaled],
+            'text_labels': ['Mean Pixel Error',
+                            'Mean Pixel Error Scaled',
+                            'Detection Rate [%]',
+                            'Detection Rate Scaled[%]'],
+        }
+        return data
+
+
+def hungarian_matching(gts, predictions, thresh=0.5):
+    cost = np.zeros((len(gts), len(predictions)))
+
+    for i, (dg, vg) in enumerate(gts):
+        for j, pred in enumerate(predictions):
+            p = np.array(pred.data)
+            dp = p[:, 0:2][vg > 1.0]
+            vp = p[:, 2][vg > 1.0]
+
+            dp[vp < thresh] = -100
+            dp[vp < thresh] = -100
+
+            # measure the per-keypoint distance
+            distances = np.clip(np.linalg.norm(dp - dg, axis=1), 0, 10)
+            cost[i, j] = float(np.mean(distances))
+
+    assert np.max(cost) < 11
+    row, cols = scipy.optimize.linear_sum_assignment(cost)
+    return row, cols, cost
+
+
+def average(my_list, *, empty_value=0.0):
+    """calculate mean of a list"""
+    if not my_list:
+        return empty_value
+
+    return sum(my_list) / float(len(my_list))