Fix: correct all SlimSAM mentions

datadreamer/dataset_annotation/slimsam_annotator.py

@@ -77,7 +77,7 @@ def annotate_batch(
         boxes_batch: List[np.ndarray],
         iou_threshold: float = 0.2,
     ) -> List[List[List[float]]]:
-        """Annotates images for the task of instance segmentation using the FastSAM
+        """Annotates images for the task of instance segmentation using the SlimSAM
         model.
 
         Args:

examples/generate_dataset_and_train_yolo.ipynb

@@ -84,7 +84,7 @@
     "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n",
     "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n",
     "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n",
-    "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-fastsam` for instance segmentation. Default is `owlv2`.\n",
+    "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-slimsam` for instance segmentation. Default is `owlv2`.\n",
     "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n",
     "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n",
     "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n",

examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb

@@ -99,7 +99,7 @@
         "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n",
         "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n",
         "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n",
-        "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-fastsam` for instance segmentation. Default is `owlv2`.\n",
+        "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-slimsam` for instance segmentation. Default is `owlv2`.\n",
         "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n",
         "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n",
         "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n",

tests/core_tests/unittests/test_annotators.py

@@ -127,29 +127,29 @@ def _check_slimsam_annotator(device: str, size: str = "base"):
     not torch.cuda.is_available() or total_disk_space < 16,
     reason="Test requires GPU and 16GB of HDD",
 )
-def test_cuda_fastsam_base_annotator():
+def test_cuda_slimsam_base_annotator():
     _check_slimsam_annotator("cuda")
 
 
 @pytest.mark.skipif(
     total_disk_space < 16,
     reason="Test requires at least 16GB of HDD",
 )
-def test_cpu_fastsam_base_annotator():
+def test_cpu_slimsam_base_annotator():
     _check_slimsam_annotator("cpu")
 
 
 @pytest.mark.skipif(
     not torch.cuda.is_available() or total_disk_space < 16,
     reason="Test requires GPU and 16GB of HDD",
 )
-def test_cuda_fastsam_large_annotator():
+def test_cuda_slimsam_large_annotator():
     _check_slimsam_annotator("cuda", size="large")
 
 
 @pytest.mark.skipif(
     total_disk_space < 16,
     reason="Test requires at least 16GB of HDD",
 )
-def test_cpu_fastsam_large_annotator():
+def test_cpu_slimsam_large_annotator():
     _check_slimsam_annotator("cpu", size="large")