docs/docs/configuration/face_recognition.md

@@ -107,17 +107,17 @@ When choosing images to include in the face training set it is recommended to al
 
 ### Step 1 - Building a Strong Foundation
 
-When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-5 photos containing just this person's face. It is important that the person's face in the photo is front-facing and not turned, this will ensure a good starting point.
+When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-5 "portrait" photos for each person. It is important that the person's face in the photo is straight-on and not turned which will ensure a good starting point.
 
-Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are front-facing. Ignore images from cameras that recognize faces from an angle.
+Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle.
 
 Aim to strike a balance between the quality of images while also having a range of conditions (day / night, different weather conditions, different times of day, etc.) in order to have diversity in the images used for each person and not have over-fitting.
 
-Once a person starts to be consistently recognized correctly on images that are front-facing, it is time to move on to the next step.
+Once a person starts to be consistently recognized correctly on images that are straight-on, it is time to move on to the next step.
 
 ### Step 2 - Expanding The Dataset
 
-Once front-facing images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.
+Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.
 
 ## FAQ
 
@@ -156,7 +156,3 @@ Face recognition does not run on the recording stream, this would be suboptimal
 ### I get an unknown error when taking a photo directly with my iPhone
 
 By default iOS devices will use HEIC (High Efficiency Image Container) for images, but this format is not supported for uploads. Choosing `large` as the format instead of `original` will use JPG which will work correctly.
-
-## How can I delete the face database and start over?
-
-Frigate does not store anything in its database related to face recognition. You can simply delete all of your faces through the Frigate UI or remove the contents of the `/media/frigate/clips/faces` directory.

frigate/detectors/detector_config.py

@@ -127,7 +127,8 @@ class ModelConfig(BaseModel):
             return
 
         # ensure that model cache dir exists
-        os.makedirs(MODEL_CACHE_DIR, exist_ok=True)
+        if not os.path.exists(MODEL_CACHE_DIR):
+            os.makedirs(MODEL_CACHE_DIR)
 
         model_id = self.path[7:]
         self.path = os.path.join(MODEL_CACHE_DIR, model_id)

web/public/locales/en/views/faceLibrary.json

@@ -27,10 +27,7 @@
   "steps": {
     "faceName": "Enter Face Name",
     "uploadFace": "Upload Face Image",
-    "nextSteps": "Next Steps",
-    "description": {
-      "uploadFace": "Upload an image of {{name}} that shows their face from a front-facing angle. The image does not need to be cropped to just their face."
-    }
+    "nextSteps": "Next Steps"
   },
   "train": {
     "title": "Train",

web/src/components/overlay/detail/FaceCreateWizardDialog.tsx

@@ -128,18 +128,13 @@ export default function CreateFaceWizardDialog({
           </TextEntry>
         )}
         {step == 1 && (
-          <>
-            <div className="px-8 py-2 text-center text-sm text-secondary-foreground">
-              {t("steps.description.uploadFace", { name })}
+          <ImageEntry onSave={onUploadImage}>
+            <div className="flex justify-end py-2">
+              <Button variant="select" type="submit">
+                {t("button.next", { ns: "common" })}
+              </Button>
             </div>
-            <ImageEntry onSave={onUploadImage}>
-              <div className="flex justify-end py-2">
-                <Button variant="select" type="submit">
-                  {t("button.next", { ns: "common" })}
-                </Button>
-              </div>
-            </ImageEntry>
-          </>
+          </ImageEntry>
         )}
         {step == 2 && (
           <div className="mt-2">