Repository: voxel51/fiftyone

Issue:
[BUG] COCO export format should be 1-indexed
There is a bug in the export of dates in COCO format. Currently, the `category_id` keyword in exported labels.json is "0" indexed. However, COCO requires that this be "1" indexed. You can reproduce this, by exporting the quickstart dataset as COCODetectionDataset and searching the labels.json file for "category_id": 0. I haven't checked if this breaks training of models, but it definitely breaks ingest by other computer vision micro services like CVAT if you are not using the SDK to send the jobs directly.

```python
import fiftyone as fo
import fiftyone.zoo as foz
from pathlib import Path


dataset = foz.load_zoo_dataset("quickstart")
dataset.export(
	export_dir=str(Path(__file__).parent.joinpath("coco-example").absolute()),
	dataset_type=fo.types.COCODetectionDataset,
	label_field="ground_truth",
)
```

![image](https://github.com/user-attachments/assets/07e80090-f3e6-41e4-9db3-1c895d996449)

Discussion:
@benjaminpkane: Hi @miller-kevin944, we will get this fixed. Thank you for the report 🙇 

Duplicates #4570 @mythrandire: I was able to replicate this too @miller-kevin944. I encountered a similar issue on the data import side of things (#4795), while yours has to do with export formats. Any additional info you can share will be helpful.@brimoor: Fixed by https://github.com/voxel51/fiftyone/pull/4884 and will be available in the next `fiftyone>1.0` release. 

Solution patch:
diff --git a/app/packages/looker/src/elements/common/bubbles.test.ts b/app/packages/looker/src/elements/common/bubbles.test.ts
index 40b5dd654..edac34b19 100644
--- a/app/packages/looker/src/elements/common/bubbles.test.ts
+++ b/app/packages/looker/src/elements/common/bubbles.test.ts
@@ -1,9 +1,11 @@
 import type { Schema } from "@fiftyone/utilities";
 import {
+  CLASSIFICATIONS,
   DYNAMIC_EMBEDDED_DOCUMENT_PATH,
   EMBEDDED_DOCUMENT_FIELD,
   LIST_FIELD,
   STRING_FIELD,
+  TEMPORAL_DETECTIONS,
 } from "@fiftyone/utilities";
 import { describe, expect, it } from "vitest";
 import { getBubbles, getField, unwind } from "./bubbles";
@@ -195,6 +197,38 @@ describe("text bubble tests", () => {
         }
       )
     ).toStrictEqual([field.fields.value, ["value"]]);
+
+    const classifications = {
+      ...FIELD_DATA,
+      dbField: "classes",
+      ftype: EMBEDDED_DOCUMENT_FIELD,
+      embeddedDocType: CLASSIFICATIONS,
+      fields: {},
+    };
+
+    expect(
+      getBubbles(
+        "classes",
+        { classes: { classifications: [{ label: "label" }] } },
+        { classes: classifications }
+      )
+    );
+
+    const temporalDetections = {
+      ...FIELD_DATA,
+      dbField: "temporal",
+      ftype: EMBEDDED_DOCUMENT_FIELD,
+      embeddedDocType: TEMPORAL_DETECTIONS,
+      fields: {},
+    };
+
+    expect(
+      getBubbles(
+        "temporal",
+        { temporal: { detections: [{ label: "label" }] } },
+        { temporal: temporalDetections }
+      )
+    );
   });
 
   it("getField gets field from a path keys", () => {
diff --git a/app/packages/looker/src/elements/common/bubbles.ts b/app/packages/looker/src/elements/common/bubbles.ts
index 9b649e8ad..feb9195c9 100644
--- a/app/packages/looker/src/elements/common/bubbles.ts
+++ b/app/packages/looker/src/elements/common/bubbles.ts
@@ -54,14 +54,14 @@ export const getBubbles = (
     }
 
     if (field.embeddedDocType === withPath(LABELS_PATH, CLASSIFICATIONS)) {
-      out.values = out.values.flatMap(
+      out.values = unwind(field.dbField, out.values).flatMap(
         (value) => value.classifications || []
       ) as Sample[];
       break;
     }
 
     if (field.embeddedDocType === withPath(LABELS_PATH, TEMPORAL_DETECTIONS)) {
-      out.values = out.values.flatMap(
+      out.values = unwind(field.dbField, out.values).flatMap(
         (value) => value.detections || []
       ) as Sample[];
       break;
diff --git a/docs/source/integrations/coco.rst b/docs/source/integrations/coco.rst
index 7ca3b0e89..2b910952e 100644
--- a/docs/source/integrations/coco.rst
+++ b/docs/source/integrations/coco.rst
@@ -192,18 +192,14 @@ file containing COCO-formatted labels to work with:
 
     dataset = foz.load_zoo_dataset("quickstart")
 
-    # Classes list
-    classes = dataset.distinct("ground_truth.detections.label")
-
     # The directory in which the dataset's images are stored
     IMAGES_DIR = os.path.dirname(dataset.first().filepath)
 
     # Export some labels in COCO format
-    dataset.take(5).export(
+    dataset.take(5, seed=51).export(
         dataset_type=fo.types.COCODetectionDataset,
         label_field="ground_truth",
         labels_path="/tmp/coco.json",
-        classes=classes,
     )
 
 Now we have a ``/tmp/coco.json`` file on disk containing COCO labels
@@ -220,7 +216,7 @@ corresponding to the images in ``IMAGES_DIR``:
         "licenses": [],
         "categories": [
             {
-                "id": 0,
+                "id": 1,
                 "name": "airplane",
                 "supercategory": null
             },
@@ -229,9 +225,9 @@ corresponding to the images in ``IMAGES_DIR``:
         "images": [
             {
                 "id": 1,
-                "file_name": "001631.jpg",
-                "height": 612,
-                "width": 612,
+                "file_name": "003486.jpg",
+                "height": 427,
+                "width": 640,
                 "license": null,
                 "coco_url": null
             },
@@ -241,14 +237,14 @@ corresponding to the images in ``IMAGES_DIR``:
             {
                 "id": 1,
                 "image_id": 1,
-                "category_id": 9,
+                "category_id": 1,
                 "bbox": [
-                    92.14,
-                    220.04,
-                    519.86,
-                    61.89000000000001
+                    34.34,
+                    147.46,
+                    492.69,
+                    192.36
                 ],
-                "area": 32174.135400000006,
+                "area": 94773.8484,
                 "iscrowd": 0
             },
             ...
@@ -271,8 +267,9 @@ dataset:
         include_id=True,
     )
 
-    # Verify that the class list for our dataset was imported
-    print(coco_dataset.default_classes)  # ['airplane', 'apple', ...]
+    # COCO categories are also imported
+    print(coco_dataset.info["categories"])
+    # [{'id': 1, 'name': 'airplane', 'supercategory': None}, ...]
 
     print(coco_dataset)
 
@@ -319,16 +316,16 @@ to add them to your dataset as follows:
     #
     # Mock COCO predictions, where:
     # - `image_id` corresponds to the `coco_id` field of `coco_dataset`
-    # - `category_id` corresponds to classes in `coco_dataset.default_classes`
+    # - `category_id` corresponds to `coco_dataset.info["categories"]`
     #
     predictions = [
-        {"image_id": 1, "category_id": 18, "bbox": [258, 41, 348, 243], "score": 0.87},
-        {"image_id": 2, "category_id": 11, "bbox": [61, 22, 504, 609], "score": 0.95},
+        {"image_id": 1, "category_id": 2, "bbox": [258, 41, 348, 243], "score": 0.87},
+        {"image_id": 2, "category_id": 4, "bbox": [61, 22, 504, 609], "score": 0.95},
     ]
+    categories = coco_dataset.info["categories"]
 
     # Add COCO predictions to `predictions` field of dataset
-    classes = coco_dataset.default_classes
-    fouc.add_coco_labels(coco_dataset, "predictions", predictions, classes)
+    fouc.add_coco_labels(coco_dataset, "predictions", predictions, categories)
 
     # Verify that predictions were added to two images
     print(coco_dataset.count("predictions"))  # 2
diff --git a/docs/source/user_guide/dataset_creation/datasets.rst b/docs/source/user_guide/dataset_creation/datasets.rst
index c25550a19..ac4085bb3 100644
--- a/docs/source/user_guide/dataset_creation/datasets.rst
+++ b/docs/source/user_guide/dataset_creation/datasets.rst
@@ -1499,9 +1499,8 @@ where `labels.json` is a JSON file in the following format:
             ...
         ],
         "categories": [
-            ...
             {
-                "id": 2,
+                "id": 1,
                 "name": "cat",
                 "supercategory": "animal",
                 "keypoints": ["nose", "head", ...],
@@ -1524,7 +1523,7 @@ where `labels.json` is a JSON file in the following format:
             {
                 "id": 1,
                 "image_id": 1,
-                "category_id": 2,
+                "category_id": 1,
                 "bbox": [260, 177, 231, 199],
                 "segmentation": [...],
                 "keypoints": [224, 226, 2, ...],
diff --git a/docs/source/user_guide/export_datasets.rst b/docs/source/user_guide/export_datasets.rst
index 293672544..810601036 100644
--- a/docs/source/user_guide/export_datasets.rst
+++ b/docs/source/user_guide/export_datasets.rst
@@ -1646,9 +1646,8 @@ where `labels.json` is a JSON file in the following format:
         },
         "licenses": [],
         "categories": [
-            ...
             {
-                "id": 2,
+                "id": 1,
                 "name": "cat",
                 "supercategory": "animal"
             },
@@ -1669,7 +1668,7 @@ where `labels.json` is a JSON file in the following format:
             {
                 "id": 1,
                 "image_id": 1,
-                "category_id": 2,
+                "category_id": 1,
                 "bbox": [260, 177, 231, 199],
                 "segmentation": [...],
                 "score": 0.95,
diff --git a/fiftyone/constants.py b/fiftyone/constants.py
index aed1b250c..4783ee722 100644
--- a/fiftyone/constants.py
+++ b/fiftyone/constants.py
@@ -42,7 +42,7 @@ RESOURCES_DIR = os.path.join(FIFTYONE_DIR, "resources")
 # This setting may be ``None`` if this client has no compatibility with other
 # versions
 #
-COMPATIBLE_VERSIONS = ">=0.19,<0.25"
+COMPATIBLE_VERSIONS = ">=0.19,<1.1"
 
 # Package metadata
 _META = metadata("fiftyone")
@@ -106,11 +106,3 @@ DATABASE_APPNAME = "fiftyone"
 
 # Server setup
 SERVER_DIR = os.path.join(FIFTYONE_DIR, "server")
-
-# App setup
-try:
-    from fiftyone.desktop import FIFTYONE_DESKTOP_APP_DIR
-except ImportError:
-    FIFTYONE_DESKTOP_APP_DIR = os.path.normpath(
-        os.path.join(FIFTYONE_DIR, "../app")
-    )
diff --git a/fiftyone/core/session/session.py b/fiftyone/core/session/session.py
index 1f05184e4..4b01c389b 100644
--- a/fiftyone/core/session/session.py
+++ b/fiftyone/core/session/session.py
@@ -1125,7 +1125,7 @@ class Session(object):
             if wait < 0:
                 while True:
                     time.sleep(10)
-            elif self.remote:
+            else:
                 self._wait_closed = False
                 while not self._wait_closed:
                     time.sleep(wait)
diff --git a/fiftyone/utils/coco.py b/fiftyone/utils/coco.py
index 76a4fd494..b2c5a730d 100644
--- a/fiftyone/utils/coco.py
+++ b/fiftyone/utils/coco.py
@@ -45,7 +45,7 @@ def add_coco_labels(
     sample_collection,
     label_field,
     labels_or_path,
-    classes,
+    categories,
     label_type="detections",
     coco_id_field=None,
     include_annotation_id=False,
@@ -68,7 +68,7 @@ def add_coco_labels(
             {
                 "id": 1,
                 "image_id": 1,
-                "category_id": 2,
+                "category_id": 1,
                 "bbox": [260, 177, 231, 199],
 
                 # optional
@@ -88,7 +88,7 @@ def add_coco_labels(
             {
                 "id": 1,
                 "image_id": 1,
-                "category_id": 2,
+                "category_id": 1,
                 "bbox": [260, 177, 231, 199],
                 "segmentation": [...],
 
@@ -109,7 +109,7 @@ def add_coco_labels(
             {
                 "id": 1,
                 "image_id": 1,
-                "category_id": 2,
+                "category_id": 1,
                 "keypoints": [224, 226, 2, ...],
                 "num_keypoints": 10,
 
@@ -129,8 +129,14 @@ def add_coco_labels(
             will be created if necessary
         labels_or_path: a list of COCO annotations or the path to a JSON file
             containing such data on disk
-        classes: the list of class label strings or a dict mapping class IDs to
-            class labels
+        categories: can be any of the following:
+
+            -   a list of category dicts in the format of
+                :meth:`parse_coco_categories` specifying the classes and their
+                category IDs
+            -   a dict mapping class IDs to class labels
+            -   a list of class labels whose 1-based ordering is assumed to
+                correspond to the category IDs in the provided COCO labels
         label_type ("detections"): the type of labels to load. Supported values
             are ``("detections", "segmentations", "keypoints")``
         coco_id_field (None): this parameter determines how to map the
@@ -195,10 +201,14 @@ def add_coco_labels(
     view.compute_metadata()
     widths, heights = view.values(["metadata.width", "metadata.height"])
 
-    if isinstance(classes, dict):
-        classes_map = classes
+    if isinstance(categories, dict):
+        classes_map = categories
+    elif not categories:
+        classes_map = {}
+    elif isinstance(categories[0], dict):
+        classes_map = {c["id"]: c["name"] for c in categories}
     else:
-        classes_map = {i: label for i, label in enumerate(classes)}
+        classes_map = {i: label for i, label in enumerate(categories, 1)}
 
     labels = []
     for _coco_objects, width, height in zip(coco_objects, widths, heights):
@@ -563,15 +573,11 @@ class COCODetectionDatasetImporter(
                 self.labels_path, extra_attrs=self.extra_attrs
             )
 
-            classes = None
             if classes_map is not None:
-                classes = _to_classes(classes_map)
-
-            if classes is not None:
-                info["classes"] = classes
+                info["classes"] = _to_classes(classes_map)
 
             image_ids = _get_matching_image_ids(
-                classes,
+                classes_map,
                 images,
                 annotations,
                 image_ids=self.image_ids,
@@ -907,12 +913,11 @@ class COCODetectionDatasetExporter(
 
     def close(self, *args):
         if self._dynamic_classes:
-            classes = sorted(self._classes)
-            labels_map_rev = _to_labels_map_rev(classes)
+            labels_map_rev = _to_labels_map_rev(sorted(self._classes))
             for anno in self._annotations:
                 anno["category_id"] = labels_map_rev[anno["category_id"]]
-        else:
-            classes = self.classes
+        elif self.categories is None:
+            labels_map_rev = _to_labels_map_rev(self.classes)
 
         _info = self.info or {}
         _date_created = datetime.now().replace(microsecond=0).isoformat()
@@ -933,10 +938,10 @@ class COCODetectionDatasetExporter(
             categories = [
                 {
                     "id": i,
-                    "name": l,
+                    "name": c,
                     "supercategory": None,
                 }
-                for i, l in enumerate(classes)
+                for c, i in sorted(labels_map_rev.items(), key=lambda t: t[1])
             ]
 
         labels = {
@@ -1681,7 +1686,7 @@ def download_coco_dataset_split(
         if classes is not None:
             # Filter by specified classes
             all_ids, any_ids = _get_images_with_classes(
-                image_ids, annotations, classes, all_classes
+                image_ids, annotations, classes, all_classes_map
             )
         else:
             all_ids = image_ids
@@ -1846,7 +1851,7 @@ def _parse_include_license(include_license):
 
 
 def _get_matching_image_ids(
-    all_classes,
+    classes_map,
     images,
     annotations,
     image_ids=None,
@@ -1862,7 +1867,7 @@ def _get_matching_image_ids(
 
     if classes is not None:
         all_ids, any_ids = _get_images_with_classes(
-            image_ids, annotations, classes, all_classes
+            image_ids, annotations, classes, classes_map
         )
     else:
         all_ids = image_ids
@@ -1930,7 +1935,7 @@ def _do_download(args):
 
 
 def _get_images_with_classes(
-    image_ids, annotations, target_classes, all_classes
+    image_ids, annotations, target_classes, classes_map
 ):
     if annotations is None:
         logger.warning("Dataset is unlabeled; ignoring classes requirement")
@@ -1939,11 +1944,12 @@ def _get_images_with_classes(
     if etau.is_str(target_classes):
         target_classes = [target_classes]
 
-    bad_classes = [c for c in target_classes if c not in all_classes]
+    labels_map_rev = {c: i for i, c in classes_map.items()}
+
+    bad_classes = [c for c in target_classes if c not in labels_map_rev]
     if bad_classes:
         raise ValueError("Unsupported classes: %s" % bad_classes)
 
-    labels_map_rev = _to_labels_map_rev(all_classes)
     class_ids = {labels_map_rev[c] for c in target_classes}
 
     all_ids = []
@@ -2029,7 +2035,7 @@ def _load_image_ids_json(json_path):
 
 
 def _to_labels_map_rev(classes):
-    return {c: i for i, c in enumerate(classes)}
+    return {c: i for i, c in enumerate(classes, 1)}
 
 
 def _to_classes(classes_map):
diff --git a/fiftyone/utils/quickstart.py b/fiftyone/utils/quickstart.py
index 574ab9e2a..9085f2a31 100644
--- a/fiftyone/utils/quickstart.py
+++ b/fiftyone/utils/quickstart.py
@@ -11,9 +11,7 @@ import fiftyone.core.session as fos
 import fiftyone.zoo.datasets as fozd
 
 
-def quickstart(
-    video=False, port=None, address=None, remote=False, desktop=None
-):
+def quickstart(video=False, port=None, address=None, remote=False):
     """Runs the FiftyOne quickstart.
 
     This method loads an interesting dataset from the Dataset Zoo, launches the
@@ -27,9 +25,6 @@ def quickstart(
             ``fiftyone.config.default_app_address`` is used
         remote (False): whether this is a remote session, and opening the App
             should not be attempted
-        desktop (None): whether to launch the App in the browser (False) or as
-            a desktop App (True). If None, ``fiftyone.config.desktop_app`` is
-            used. Not applicable to notebook contexts
 
     Returns:
         a tuple containing
@@ -39,30 +34,29 @@ def quickstart(
             the App that was launched
     """
     if video:
-        return _video_quickstart(port, address, remote, desktop)
+        return _video_quickstart(port, address, remote)
 
-    return _quickstart(port, address, remote, desktop)
+    return _quickstart(port, address, remote)
 
 
-def _quickstart(port, address, remote, desktop):
+def _quickstart(port, address, remote):
     print(_QUICKSTART_GUIDE)
     dataset = fozd.load_zoo_dataset("quickstart")
-    return _launch_app(dataset, port, address, remote, desktop)
+    return _launch_app(dataset, port, address, remote)
 
 
-def _video_quickstart(port, address, remote, desktop):
+def _video_quickstart(port, address, remote):
     print(_VIDEO_QUICKSTART_GUIDE)
     dataset = fozd.load_zoo_dataset("quickstart-video")
-    return _launch_app(dataset, port, address, remote, desktop)
+    return _launch_app(dataset, port, address, remote)
 
 
-def _launch_app(dataset, port, address, remote, desktop):
+def _launch_app(dataset, port, address, remote):
     session = fos.launch_app(
         dataset=dataset,
         port=port,
         address=address,
         remote=remote,
-        desktop=desktop,
     )
 
     return dataset, session


Test patch:
diff --git a/tests/unittests/import_export_tests.py b/tests/unittests/import_export_tests.py
index 54798733f..896429d8a 100644
--- a/tests/unittests/import_export_tests.py
+++ b/tests/unittests/import_export_tests.py
@@ -1317,6 +1317,65 @@ class ImageDetectionDatasetTests(ImageDatasetTests):
             {c["id"] for c in categories2},
         )
 
+        # Alphabetized 1-based categories by default
+
+        export_dir = self._new_dir()
+
+        dataset.export(
+            export_dir=export_dir,
+            dataset_type=fo.types.COCODetectionDataset,
+        )
+
+        dataset2 = fo.Dataset.from_dir(
+            dataset_dir=export_dir,
+            dataset_type=fo.types.COCODetectionDataset,
+            label_types="detections",
+            label_field="predictions",
+        )
+        categories2 = dataset2.info["categories"]
+
+        self.assertListEqual([c["id"] for c in categories2], [1, 2])
+        self.assertListEqual([c["name"] for c in categories2], ["cat", "dog"])
+
+        # Only load matching classes
+
+        export_dir = self._new_dir()
+
+        dataset.export(
+            export_dir=export_dir,
+            dataset_type=fo.types.COCODetectionDataset,
+        )
+
+        dataset2 = fo.Dataset.from_dir(
+            dataset_dir=export_dir,
+            dataset_type=fo.types.COCODetectionDataset,
+            label_types="detections",
+            label_field="predictions",
+            classes="cat",
+            only_matching=False,
+        )
+
+        self.assertEqual(len(dataset2), 2)
+        self.assertListEqual(
+            dataset2.distinct("predictions.detections.label"),
+            ["cat", "dog"],
+        )
+
+        dataset3 = fo.Dataset.from_dir(
+            dataset_dir=export_dir,
+            dataset_type=fo.types.COCODetectionDataset,
+            label_types="detections",
+            label_field="predictions",
+            classes="cat",
+            only_matching=True,
+        )
+
+        self.assertEqual(len(dataset3), 2)
+        self.assertListEqual(
+            dataset3.distinct("predictions.detections.label"),
+            ["cat"],
+        )
+
     @drop_datasets
     def test_voc_detection_dataset(self):
         dataset = self._make_dataset()
@@ -1758,16 +1817,19 @@ class ImageDetectionDatasetTests(ImageDatasetTests):
     @drop_datasets
     def test_add_coco_labels(self):
         dataset = self._make_dataset()
+
         classes = dataset.distinct("predictions.detections.label")
+        categories = [{"id": i, "name": l} for i, l in enumerate(classes, 1)]
 
         export_dir = self._new_dir()
         dataset.export(
             export_dir=export_dir,
             dataset_type=fo.types.COCODetectionDataset,
+            categories=categories,
         )
         coco_labels_path = os.path.join(export_dir, "labels.json")
 
-        fouc.add_coco_labels(dataset, "coco", coco_labels_path, classes)
+        fouc.add_coco_labels(dataset, "coco", coco_labels_path, categories)
         self.assertEqual(
             dataset.count_values("predictions.detections.label"),
             dataset.count_values("coco.detections.label"),