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v0.0.90 experiments
This commit is contained in:
Sefik Ilkin Serengil
parent
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benchmarks/Evaluate-Results.ipynb
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benchmarks/Evaluate-Results.ipynb
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benchmarks/Perform-Experiments.ipynb
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benchmarks/Perform-Experiments.ipynb
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@ -10,7 +10,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 11,
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"id": "5aab0cbe",
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"metadata": {},
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"outputs": [],
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@ -30,7 +30,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"id": "64c9ed9a",
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"metadata": {},
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"outputs": [
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@ -38,7 +38,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"This experiment is done with pip package of deepface with 0.0.89 version\n"
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"This experiment is done with pip package of deepface with 0.0.90 version\n"
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]
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}
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],
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@ -56,15 +56,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"id": "453104b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# all configuration alternatives for 4 dimensions of arguments\n",
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"alignment = [True, False]\n",
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"models = [\"Facenet\", \"Facenet512\", \"VGG-Face\", \"ArcFace\", \"Dlib\", \"GhostFaceNet\", \"SFace\", \"OpenFace\", \"DeepFace\", \"DeepID\"]\n",
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"detectors = [\"retinaface\", \"mtcnn\", \"dlib\", \"yolov8\", \"yunet\", \"mediapipe\", \"ssd\", \"opencv\", \"skip\"]\n",
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"models = [\"Facenet512\", \"Facenet\", \"VGG-Face\", \"ArcFace\", \"Dlib\", \"GhostFaceNet\", \"SFace\", \"OpenFace\", \"DeepFace\", \"DeepID\"]\n",
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"detectors = [\"retinaface\", \"mtcnn\", \"fastmtcnn\", \"dlib\", \"yolov8\", \"yunet\", \"centerface\", \"mediapipe\", \"ssd\", \"opencv\", \"skip\"]\n",
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"metrics = [\"euclidean\", \"euclidean_l2\", \"cosine\"]\n",
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"expand_percentage = 0"
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]
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@ -79,12 +79,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"id": "671d8a00",
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"metadata": {},
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"outputs": [],
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"source": [
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"target_paths = [\"lfwe\", \"dataset\", \"outputs\", \"results\"]\n",
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"target_paths = [\"lfwe\", \"dataset\", \"outputs\", \"outputs/test\", \"results\"]\n",
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"for target_path in target_paths:\n",
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" if os.path.exists(target_path) != True:\n",
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" os.mkdir(target_path)\n",
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@ -101,24 +101,24 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"id": "721a7d70",
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"metadata": {},
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"outputs": [],
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"source": [
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"pairs_touch = \"outputs/lfwe.txt\"\n",
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"pairs_touch = \"outputs/test_lfwe.txt\"\n",
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"instances = 1000 #pairs.shape[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"id": "010184d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"target_path = \"dataset/lfw.npy\"\n",
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"labels_path = \"dataset/labels.npy\"\n",
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"target_path = \"dataset/test_lfw.npy\"\n",
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"labels_path = \"dataset/test_labels.npy\"\n",
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"\n",
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"if os.path.exists(target_path) != True:\n",
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" fetch_lfw_pairs = fetch_lfw_pairs(subset = 'test', color = True\n",
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@ -148,7 +148,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 7,
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"id": "5bc23313",
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"metadata": {},
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"outputs": [
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@ -156,14 +156,14 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 1000/1000 [00:00<00:00, 219746.63it/s]\n"
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"100%|██████████| 1000/1000 [00:00<00:00, 190546.25it/s]\n"
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]
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}
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],
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"source": [
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"for i in tqdm(range(0, instances)):\n",
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" img1_target = f\"lfwe/{i}_1.jpg\"\n",
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" img2_target = f\"lfwe/{i}_2.jpg\"\n",
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" img1_target = f\"lfwe/test/{i}_1.jpg\"\n",
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" img2_target = f\"lfwe/test/{i}_2.jpg\"\n",
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" \n",
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" if os.path.exists(img1_target) != True:\n",
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" img1 = pairs[i][0]\n",
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@ -191,7 +191,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 8,
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"id": "e7fba936",
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"metadata": {},
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"outputs": [],
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@ -207,15 +207,15 @@
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" \n",
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" alignment_text = \"aligned\" if align is True else \"unaligned\"\n",
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" task = f\"{model_name}_{detector_backend}_{distance_metric}_{alignment_text}\"\n",
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" output_file = f\"outputs/{task}.csv\"\n",
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" output_file = f\"outputs/test/{task}.csv\"\n",
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" if os.path.exists(output_file) is True:\n",
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" #print(f\"{output_file} is available already\")\n",
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" continue\n",
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" \n",
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" distances = []\n",
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" for i in tqdm(range(0, instances), desc = task):\n",
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" img1_target = f\"lfwe/{i}_1.jpg\"\n",
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" img2_target = f\"lfwe/{i}_2.jpg\"\n",
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" img1_target = f\"lfwe/test/{i}_1.jpg\"\n",
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" img2_target = f\"lfwe/test/{i}_2.jpg\"\n",
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" result = DeepFace.verify(\n",
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" img1_path=img1_target,\n",
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" img2_path=img2_target,\n",
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@ -246,7 +246,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 9,
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"id": "67376e76",
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"metadata": {},
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"outputs": [],
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@ -257,7 +257,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 10,
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"id": "f2cc536b",
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"metadata": {},
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"outputs": [
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@ -293,7 +293,7 @@
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" # Alignment is not possible for a skipped detector configuration\n",
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" align = \"unaligned\"\n",
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"\n",
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" source_file = f\"outputs/{model_name}_{detector_backend}_{distance_metric}_{align}.csv\"\n",
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" source_file = f\"outputs/test/{model_name}_{detector_backend}_{distance_metric}_{align}.csv\"\n",
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" df = pd.read_csv(source_file)\n",
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" \n",
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" positive_mean = df[(df[\"actuals\"] == True) | (df[\"actuals\"] == 1)][\"distances\"].mean()\n",
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@ -316,7 +316,8 @@
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"\n",
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" pivot_df = pd.DataFrame(items, columns = [\"distance\", \"accuracy\"])\n",
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" pivot_df = pivot_df.sort_values(by = [\"accuracy\"], ascending = False)\n",
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" # threshold = pivot_df.iloc[0][\"distance\"]\n",
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" threshold = pivot_df.iloc[0][\"distance\"]\n",
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" # print(f\"threshold for {model_name}/{detector_backend} is {threshold}\")\n",
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" accuracy = pivot_df.iloc[0][\"accuracy\"]\n",
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"\n",
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" # print(source_file, round(accuracy, 1))\n",
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@ -19,86 +19,98 @@ In summary, FaceNet-512d surpasses human-level accuracy, while FaceNet-128d reac
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Please note that humans achieve a 97.5% accuracy score on the same dataset. Configurations that outperform this benchmark are highlighted in bold.
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### Performance Matrix for Euclidean while alignment is True
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## Performance Matrix for euclidean while alignment is True
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| | Facenet |Facenet512 |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
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| | Facenet512 |Facenet |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| retinaface |93.5 |95.9 |95.8 |85.2 |88.9 |85.9 |80.2 |69.4 |67.0 |65.6 |
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| mtcnn |93.8 |95.2 |95.9 |83.7 |89.4 |83.0 |77.4 |70.2 |66.5 |63.3 |
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| dlib |90.8 |96.0 |94.5 |88.6 |96.8 |65.7 |66.3 |75.8 |63.4 |60.4 |
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| yolov8 |91.9 |94.4 |95.0 |84.1 |89.2 |77.6 |73.4 |68.7 |69.0 |66.5 |
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| yunet |96.1 |97.3 |96.0 |84.9 |92.2 |84.0 |79.4 |70.9 |65.8 |65.2 |
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| mediapipe |88.6 |95.1 |92.9 |73.2 |93.1 |63.2 |72.5 |78.7 |61.8 |62.2 |
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| ssd |85.6 |88.9 |87.0 |75.8 |83.1 |79.1 |76.9 |66.8 |63.4 |62.5 |
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| opencv |84.2 |88.2 |87.3 |73.0 |84.4 |83.8 |81.1 |66.4 |65.5 |59.6 |
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| skip |64.1 |92.0 |90.6 |56.6 |69.0 |75.1 |81.4 |57.4 |60.8 |60.7 |
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| retinaface |95.9 |93.5 |95.8 |85.2 |88.9 |85.9 |80.2 |69.4 |67.0 |65.6 |
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| mtcnn |95.2 |93.8 |95.9 |83.7 |89.4 |83.0 |77.4 |70.2 |66.5 |63.3 |
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| fastmtcnn |96.0 |93.4 |95.8 |83.5 |91.1 |82.8 |77.7 |69.4 |66.7 |64.0 |
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| dlib |96.0 |90.8 |94.5 |88.6 |96.8 |65.7 |66.3 |75.8 |63.4 |60.4 |
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| yolov8 |94.4 |91.9 |95.0 |84.1 |89.2 |77.6 |73.4 |68.7 |69.0 |66.5 |
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| yunet |97.3 |96.1 |96.0 |84.9 |92.2 |84.0 |79.4 |70.9 |65.8 |65.2 |
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| centerface |**97.6** |95.8 |95.7 |83.6 |90.4 |82.8 |77.4 |68.9 |65.5 |62.8 |
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| mediapipe |95.1 |88.6 |92.9 |73.2 |93.1 |63.2 |72.5 |78.7 |61.8 |62.2 |
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| ssd |88.9 |85.6 |87.0 |75.8 |83.1 |79.1 |76.9 |66.8 |63.4 |62.5 |
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| opencv |88.2 |84.2 |87.3 |73.0 |84.4 |83.8 |81.1 |66.4 |65.5 |59.6 |
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| skip |92.0 |64.1 |90.6 |56.6 |69.0 |75.1 |81.4 |57.4 |60.8 |60.7 |
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### Performance Matrix for Euclidean while alignment is False
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## Performance Matrix for euclidean while alignment is False
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| | Facenet |Facenet512 |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
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| | Facenet512 |Facenet |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| retinaface |92.8 |96.1 |95.7 |84.1 |88.3 |83.2 |78.6 |70.8 |67.4 |64.3 |
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| mtcnn |92.5 |95.9 |95.5 |81.8 |89.3 |83.2 |76.3 |70.9 |65.9 |63.2 |
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| dlib |89.0 |96.0 |94.1 |82.6 |96.3 |65.6 |73.1 |75.9 |61.8 |61.9 |
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| yolov8 |90.8 |94.8 |95.2 |83.2 |88.4 |77.6 |71.6 |68.9 |68.2 |66.3 |
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| yunet |96.5 |**97.9** |96.3 |84.1 |91.4 |82.7 |78.2 |71.7 |65.5 |65.2 |
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| mediapipe |87.1 |94.9 |93.1 |71.1 |91.9 |61.9 |73.2 |77.6 |61.7 |62.4 |
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| ssd |94.9 |97.2 |96.7 |83.9 |88.6 |84.9 |82.0 |69.9 |66.7 |64.0 |
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| opencv |90.2 |94.1 |95.8 |89.8 |91.2 |91.0 |86.9 |71.1 |68.4 |61.1 |
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| skip |64.1 |92.0 |90.6 |56.6 |69.0 |75.1 |81.4 |57.4 |60.8 |60.7 |
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| retinaface |96.1 |92.8 |95.7 |84.1 |88.3 |83.2 |78.6 |70.8 |67.4 |64.3 |
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| mtcnn |95.9 |92.5 |95.5 |81.8 |89.3 |83.2 |76.3 |70.9 |65.9 |63.2 |
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| fastmtcnn |96.3 |93.0 |96.0 |82.2 |90.0 |82.7 |76.8 |71.2 |66.5 |64.3 |
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| dlib |96.0 |89.0 |94.1 |82.6 |96.3 |65.6 |73.1 |75.9 |61.8 |61.9 |
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| yolov8 |94.8 |90.8 |95.2 |83.2 |88.4 |77.6 |71.6 |68.9 |68.2 |66.3 |
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| yunet |**97.9** |96.5 |96.3 |84.1 |91.4 |82.7 |78.2 |71.7 |65.5 |65.2 |
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| centerface |97.4 |95.4 |95.8 |83.2 |90.3 |82.0 |76.5 |69.9 |65.7 |62.9 |
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| mediapipe |94.9 |87.1 |93.1 |71.1 |91.9 |61.9 |73.2 |77.6 |61.7 |62.4 |
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| ssd |97.2 |94.9 |96.7 |83.9 |88.6 |84.9 |82.0 |69.9 |66.7 |64.0 |
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| opencv |94.1 |90.2 |95.8 |89.8 |91.2 |91.0 |86.9 |71.1 |68.4 |61.1 |
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| skip |92.0 |64.1 |90.6 |56.6 |69.0 |75.1 |81.4 |57.4 |60.8 |60.7 |
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### Performance Matrix for L2 normalized Euclidean while alignment is True
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## Performance Matrix for euclidean_l2 while alignment is True
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| | Facenet |Facenet512 |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
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| | Facenet512 |Facenet |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| retinaface |96.4 |**98.4** |95.8 |96.6 |89.1 |90.5 |92.4 |69.4 |67.7 |64.4 |
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| mtcnn |96.8 |**97.6** |95.9 |96.0 |90.0 |89.8 |90.5 |70.2 |66.4 |64.0 |
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| dlib |92.6 |97.0 |94.5 |95.1 |96.4 |63.3 |69.8 |75.8 |66.5 |59.5 |
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| yolov8 |95.7 |97.3 |95.0 |95.5 |88.8 |88.9 |91.9 |68.7 |67.5 |66.0 |
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| yunet |97.4 |**97.9** |96.0 |96.7 |91.6 |89.1 |91.0 |70.9 |66.5 |63.6 |
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| mediapipe |90.6 |96.1 |92.9 |90.3 |92.6 |64.4 |75.4 |78.7 |64.7 |63.0 |
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| ssd |87.5 |88.7 |87.0 |86.2 |83.3 |82.2 |84.6 |66.8 |64.1 |62.6 |
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| opencv |84.8 |87.6 |87.3 |84.6 |84.0 |85.0 |83.6 |66.4 |63.8 |60.9 |
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| skip |67.6 |91.4 |90.6 |57.2 |69.3 |78.4 |83.4 |57.4 |62.6 |61.6 |
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| retinaface |**98.4** |96.4 |95.8 |96.6 |89.1 |90.5 |92.4 |69.4 |67.7 |64.4 |
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| mtcnn |**97.6** |96.8 |95.9 |96.0 |90.0 |89.8 |90.5 |70.2 |66.4 |64.0 |
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| fastmtcnn |**98.1** |97.2 |95.8 |96.4 |91.0 |89.5 |90.0 |69.4 |67.4 |64.1 |
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| dlib |97.0 |92.6 |94.5 |95.1 |96.4 |63.3 |69.8 |75.8 |66.5 |59.5 |
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| yolov8 |97.3 |95.7 |95.0 |95.5 |88.8 |88.9 |91.9 |68.7 |67.5 |66.0 |
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| yunet |**97.9** |97.4 |96.0 |96.7 |91.6 |89.1 |91.0 |70.9 |66.5 |63.6 |
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| centerface |**97.7** |96.8 |95.7 |96.5 |90.9 |87.5 |89.3 |68.9 |67.8 |64.0 |
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| mediapipe |96.1 |90.6 |92.9 |90.3 |92.6 |64.4 |75.4 |78.7 |64.7 |63.0 |
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| ssd |88.7 |87.5 |87.0 |86.2 |83.3 |82.2 |84.6 |66.8 |64.1 |62.6 |
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| opencv |87.6 |84.8 |87.3 |84.6 |84.0 |85.0 |83.6 |66.4 |63.8 |60.9 |
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| skip |91.4 |67.6 |90.6 |57.2 |69.3 |78.4 |83.4 |57.4 |62.6 |61.6 |
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### Performance Matrix for L2 normalized Euclidean while alignment is False
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## Performance Matrix for euclidean_l2 while alignment is False
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| | Facenet |Facenet512 |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
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| | Facenet512 |Facenet |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| retinaface |95.9 |**98.0** |95.7 |95.7 |88.4 |89.5 |90.6 |70.8 |67.7 |64.6 |
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| mtcnn |96.2 |**97.8** |95.5 |95.9 |89.2 |88.0 |91.1 |70.9 |67.0 |64.0 |
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| dlib |89.9 |96.5 |94.1 |93.8 |95.6 |63.0 |75.0 |75.9 |62.6 |61.8 |
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| yolov8 |95.8 |**97.7** |95.2 |95.0 |88.1 |88.7 |89.8 |68.9 |68.9 |65.3 |
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| yunet |96.8 |**98.3** |96.3 |96.1 |91.7 |88.0 |90.5 |71.7 |67.6 |63.2 |
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| mediapipe |90.0 |96.3 |93.1 |89.3 |91.8 |65.6 |74.6 |77.6 |64.9 |61.6 |
|
||||
| ssd |97.0 |**97.9** |96.7 |96.6 |89.4 |91.5 |93.0 |69.9 |68.7 |64.9 |
|
||||
| opencv |92.9 |96.2 |95.8 |93.2 |91.5 |93.3 |91.7 |71.1 |68.3 |61.6 |
|
||||
| skip |67.6 |91.4 |90.6 |57.2 |69.3 |78.4 |83.4 |57.4 |62.6 |61.6 |
|
||||
| retinaface |**98.0** |95.9 |95.7 |95.7 |88.4 |89.5 |90.6 |70.8 |67.7 |64.6 |
|
||||
| mtcnn |**97.8** |96.2 |95.5 |95.9 |89.2 |88.0 |91.1 |70.9 |67.0 |64.0 |
|
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| fastmtcnn |**97.7** |96.6 |96.0 |95.9 |89.6 |87.8 |89.7 |71.2 |67.8 |64.2 |
|
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| dlib |96.5 |89.9 |94.1 |93.8 |95.6 |63.0 |75.0 |75.9 |62.6 |61.8 |
|
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| yolov8 |**97.7** |95.8 |95.2 |95.0 |88.1 |88.7 |89.8 |68.9 |68.9 |65.3 |
|
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| yunet |**98.3** |96.8 |96.3 |96.1 |91.7 |88.0 |90.5 |71.7 |67.6 |63.2 |
|
||||
| centerface |97.4 |96.3 |95.8 |95.8 |90.2 |86.8 |89.3 |69.9 |68.4 |63.1 |
|
||||
| mediapipe |96.3 |90.0 |93.1 |89.3 |91.8 |65.6 |74.6 |77.6 |64.9 |61.6 |
|
||||
| ssd |**97.9** |97.0 |96.7 |96.6 |89.4 |91.5 |93.0 |69.9 |68.7 |64.9 |
|
||||
| opencv |96.2 |92.9 |95.8 |93.2 |91.5 |93.3 |91.7 |71.1 |68.3 |61.6 |
|
||||
| skip |91.4 |67.6 |90.6 |57.2 |69.3 |78.4 |83.4 |57.4 |62.6 |61.6 |
|
||||
|
||||
### Performance Matrix for cosine while alignment is True
|
||||
## Performance Matrix for cosine while alignment is True
|
||||
|
||||
| | Facenet |Facenet512 |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
|
||||
| | Facenet512 |Facenet |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| retinaface |96.4 |**98.4** |95.8 |96.6 |89.1 |90.5 |92.4 |69.4 |67.7 |64.4 |
|
||||
| mtcnn |96.8 |**97.6** |95.9 |96.0 |90.0 |89.8 |90.5 |70.2 |66.3 |63.0 |
|
||||
| dlib |92.6 |97.0 |94.5 |95.1 |96.4 |63.3 |69.8 |75.8 |66.5 |58.7 |
|
||||
| yolov8 |95.7 |97.3 |95.0 |95.5 |88.8 |88.9 |91.9 |68.7 |67.5 |65.9 |
|
||||
| yunet |97.4 |**97.9** |96.0 |96.7 |91.6 |89.1 |91.0 |70.9 |66.5 |63.5 |
|
||||
| mediapipe |90.6 |96.1 |92.9 |90.3 |92.6 |64.3 |75.4 |78.7 |64.8 |63.0 |
|
||||
| ssd |87.5 |88.7 |87.0 |86.2 |83.3 |82.2 |84.5 |66.8 |63.8 |62.6 |
|
||||
| opencv |84.9 |87.6 |87.2 |84.6 |84.0 |85.0 |83.6 |66.2 |63.7 |60.1 |
|
||||
| skip |67.6 |91.4 |90.6 |54.8 |69.3 |78.4 |83.4 |57.4 |62.6 |61.1 |
|
||||
| retinaface |**98.4** |96.4 |95.8 |96.6 |89.1 |90.5 |92.4 |69.4 |67.7 |64.4 |
|
||||
| mtcnn |**97.6** |96.8 |95.9 |96.0 |90.0 |89.8 |90.5 |70.2 |66.3 |63.0 |
|
||||
| fastmtcnn |**98.1** |97.2 |95.8 |96.4 |91.0 |89.5 |90.0 |69.4 |67.4 |63.6 |
|
||||
| dlib |97.0 |92.6 |94.5 |95.1 |96.4 |63.3 |69.8 |75.8 |66.5 |58.7 |
|
||||
| yolov8 |97.3 |95.7 |95.0 |95.5 |88.8 |88.9 |91.9 |68.7 |67.5 |65.9 |
|
||||
| yunet |**97.9** |97.4 |96.0 |96.7 |91.6 |89.1 |91.0 |70.9 |66.5 |63.5 |
|
||||
| centerface |**97.7** |96.8 |95.7 |96.5 |90.9 |87.5 |89.3 |68.9 |67.8 |63.6 |
|
||||
| mediapipe |96.1 |90.6 |92.9 |90.3 |92.6 |64.3 |75.4 |78.7 |64.8 |63.0 |
|
||||
| ssd |88.7 |87.5 |87.0 |86.2 |83.3 |82.2 |84.5 |66.8 |63.8 |62.6 |
|
||||
| opencv |87.6 |84.9 |87.2 |84.6 |84.0 |85.0 |83.6 |66.2 |63.7 |60.1 |
|
||||
| skip |91.4 |67.6 |90.6 |54.8 |69.3 |78.4 |83.4 |57.4 |62.6 |61.1 |
|
||||
|
||||
### Performance Matrix for cosine while alignment is False
|
||||
## Performance Matrix for cosine while alignment is False
|
||||
|
||||
| | Facenet |Facenet512 |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
|
||||
| | Facenet512 |Facenet |VGG-Face |ArcFace |Dlib |GhostFaceNet |SFace |OpenFace |DeepFace |DeepID |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| retinaface |95.9 |**98.0** |95.7 |95.7 |88.4 |89.5 |90.6 |70.8 |67.7 |63.7 |
|
||||
| mtcnn |96.2 |**97.8** |95.5 |95.9 |89.2 |88.0 |91.1 |70.9 |67.0 |64.0 |
|
||||
| dlib |89.9 |96.5 |94.1 |93.8 |95.6 |63.0 |75.0 |75.9 |62.6 |61.7 |
|
||||
| yolov8 |95.8 |**97.7** |95.2 |95.0 |88.1 |88.7 |89.8 |68.9 |68.9 |65.3 |
|
||||
| yunet |96.8 |**98.3** |96.3 |96.1 |91.7 |88.0 |90.5 |71.7 |67.6 |63.2 |
|
||||
| mediapipe |90.0 |96.3 |93.1 |89.3 |91.8 |64.8 |74.6 |77.6 |64.9 |61.6 |
|
||||
| ssd |97.0 |**97.9** |96.7 |96.6 |89.4 |91.5 |93.0 |69.9 |68.7 |63.8 |
|
||||
| opencv |92.9 |96.2 |95.8 |93.2 |91.5 |93.3 |91.7 |71.1 |68.1 |61.1 |
|
||||
| skip |67.6 |91.4 |90.6 |54.8 |69.3 |78.4 |83.4 |57.4 |62.6 |61.1 |
|
||||
| retinaface |**98.0** |95.9 |95.7 |95.7 |88.4 |89.5 |90.6 |70.8 |67.7 |63.7 |
|
||||
| mtcnn |**97.8** |96.2 |95.5 |95.9 |89.2 |88.0 |91.1 |70.9 |67.0 |64.0 |
|
||||
| fastmtcnn |**97.7** |96.6 |96.0 |95.9 |89.6 |87.8 |89.7 |71.2 |67.8 |62.7 |
|
||||
| dlib |96.5 |89.9 |94.1 |93.8 |95.6 |63.0 |75.0 |75.9 |62.6 |61.7 |
|
||||
| yolov8 |**97.7** |95.8 |95.2 |95.0 |88.1 |88.7 |89.8 |68.9 |68.9 |65.3 |
|
||||
| yunet |**98.3** |96.8 |96.3 |96.1 |91.7 |88.0 |90.5 |71.7 |67.6 |63.2 |
|
||||
| centerface |97.4 |96.3 |95.8 |95.8 |90.2 |86.8 |89.3 |69.9 |68.4 |62.6 |
|
||||
| mediapipe |96.3 |90.0 |93.1 |89.3 |91.8 |64.8 |74.6 |77.6 |64.9 |61.6 |
|
||||
| ssd |**97.9** |97.0 |96.7 |96.6 |89.4 |91.5 |93.0 |69.9 |68.7 |63.8 |
|
||||
| opencv |96.2 |92.9 |95.8 |93.2 |91.5 |93.3 |91.7 |71.1 |68.1 |61.1 |
|
||||
| skip |91.4 |67.6 |90.6 |54.8 |69.3 |78.4 |83.4 |57.4 |62.6 |61.1 |
|
||||
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Reference in New Issue
Block a user