I think this should all work

make_dataset.py → 1_make_dataset.py

@@ -79,5 +79,5 @@ def start_fiftyone():
 if __name__ == "__main__":
     print("reading in data")
     import_and_create_with_fields()
-    start_fiftyone()
+    #start_fiftyone()
     print("done")

2_data_splitting.py

@@ -0,0 +1,18 @@
+import fiftyone as fo
+import fiftyone.zoo as foz
+import fiftyone.brain as fob
+from sympy.core.random import shuffle
+
+dataset_name = "photo_album"
+
+if __name__ == "__main__":
+    print("starting")
+    dataset = fo.load_dataset(dataset_name)
+    # A DatasetView is a shallow copy of the original dataset.This means,
+    # if your dataset is persisted, any data or schema changes to the view will be persisted to the original dataset.
+    # If you don't want to affect the original dataset you must make the view a clone of the original data.
+    shuffle_view = dataset.shuffle()
+    training_view = shuffle_view[0:300].clone()
+    training_view.save()
+
+    print("finished")

3_make_embeddings.py

@@ -0,0 +1,25 @@
+import fiftyone as fo
+import fiftyone.zoo as foz
+import fiftyone.brain as fob
+
+from imageio.plugins.ffmpeg import download
+
+dataset_name = "training_data"
+# model = foz.load_zoo_model("faster-rcnn-resnet50-fpn-coco-torch")
+# samples.compute_embeddings(model, embeddings_field="embeddings")
+
+if __name__ == "__main__":
+
+    training_view = fo.load_dataset("training_data")
+
+    model = foz.load_zoo_model('resnet101-imagenet-torch')
+    fob.compute_visualization(training_view, model=model, embeddings="sresnet101_imagenet",
+                              brain_key="resnet101_imagenet_embed")
+
+    model2 = foz.load_zoo_model("vgg11-bn-imagenet-torch")
+    fob.compute_visualization(training_view, model=model2, embeddings="fvgg11_bn", brain_key="vgg11_bn_embed")
+
+    session = fo.launch_app(dataset)
+    session.wait()
+
+    print("done")