DeepLabCut · n-poulsen · Feb 24, 2025 · Feb 24, 2025
diff --git a/deeplabcut/modelzoo/video_inference.py b/deeplabcut/modelzoo/video_inference.py
@@ -231,7 +231,7 @@ def video_inference_superanimal(
     )
 
     Tips:
-    * max_individuals: make sure you correclty give the number of individuals. Our
+    * max_individuals: make sure you correctly give the number of individuals. Our
         inference api will only give up to max_individuals number of predictions.
     * pseudo_threshold: the higher you set, the more aggressive you filter low
         confidence predictions during video adaptation.

diff --git a/deeplabcut/pose_estimation_pytorch/README.md b/deeplabcut/pose_estimation_pytorch/README.md
@@ -462,7 +462,7 @@ When `deeplabcut.pose_estimation_pytorch.apis.analyze_videos.video_inference` is
 with a top-down model, it is assumed that a detector snapshot is given as well to obtain
 bounding boxes with which to run pose estimation. It's possible that you've already 
 obtained bounding boxes for your video (with another object detector or through some 
-other means), and you want to re-use those bounding boxes instead of running an object
+other means), and you want to reuse those bounding boxes instead of running an object
 detector again.
 
 You can easily do so by writing a bit of custom code, as shown in the example below:

diff --git a/deeplabcut/pose_estimation_pytorch/models/backbones/cspnext.py b/deeplabcut/pose_estimation_pytorch/models/backbones/cspnext.py
@@ -60,7 +60,7 @@ class CSPNeXt(HuggingFaceWeightsMixin, BaseBackbone):
         out_indices: The branch indices to output. If a tuple of integers, the outputs
             are returned as a list of tensors. If a single integer, a tensor is returned
             containing the configured index.
-        channel_attention: Add chanel attention to all stages
+        channel_attention: Add channel attention to all stages
         norm_layer: The type of normalization layer to use.
         activation_fn: The type of activation function to use.
         **kwargs: BaseBackbone kwargs.

diff --git a/deeplabcut/pose_estimation_pytorch/models/target_generators/sim_cc.py b/deeplabcut/pose_estimation_pytorch/models/target_generators/sim_cc.py
@@ -147,7 +147,7 @@ def _generate_standard(
         target_y = np.zeros((N, K, H), dtype=np.float32)
 
         for n, k in product(range(N), range(K)):
-            # skip unlabled keypoints
+            # skip unlabeled keypoints
             if keypoints_visible[n, k] < 0.5:
                 continue
 
@@ -204,7 +204,7 @@ def _generate_gaussian(
         y = np.arange(0, H, 1, dtype=np.float32)
 
         for n, k in product(range(N), range(K)):
-            # skip unlabled keypoints
+            # skip unlabeled keypoints
             if keypoints_visible[n, k] < 0.5:
                 continue
 

diff --git a/deeplabcut/utils/pseudo_label.py b/deeplabcut/utils/pseudo_label.py
@@ -402,7 +402,7 @@ def dlc3predictions_2_annotation_from_video(
     # skipping every 4 frames should speed up and not impact the performance
     predictions, image_paths = predictions[::10], image_paths[::10]
 
-    # because inference api does not return image path. I am assuming the predictions come in an oder from the video
+    # Since the inference API does not return the image path, I assume the predictions are provided in the same order as the frames in the video.
     assert len(image_paths) == len(
         predictions
     ), f"number of images must be equal to number of predictions. image_paths: {len(image_paths)} , predictions: {len(predictions)}"

diff --git a/examples/COLAB/COLAB_YOURDATA_SuperAnimal.ipynb b/examples/COLAB/COLAB_YOURDATA_SuperAnimal.ipynb
@@ -497,7 +497,7 @@
     "\n",
     "**SuperAnimal transfer-learning** The encoder was pre-trained first from ImageNet, then in pose datasets we colleceted. Then decoder is trained from scratch in downstream tasks.\n",
     "\n",
-    "**SuperAnimal naive fine-tuning** Both the encoder and the decoder were pre-trained in pose datasets we collected. In downstream datsets, we only finetune convolutional channels that correspond to the annotated keypoints in the downstream datasets. This introduces catastrophic forgetting in keypoints that are not annotated in the downstream datasets.\n",
+    "**SuperAnimal naive fine-tuning** Both the encoder and the decoder were pre-trained in pose datasets we collected. In downstream datasets, we only finetune convolutional channels that correspond to the annotated keypoints in the downstream datasets. This introduces catastrophic forgetting in keypoints that are not annotated in the downstream datasets.\n",
     "\n",
     "**SuperAnimal memory-replay fine-tuning** If we apply fine-tuning with SuperAnimal without further cares, the models will forget about keypoints that are not annotated in the downstream datasets. To mitigate this, we mix the annotations and zero-shot predictions of SuperAnimal models to create a dataset that 'replays' the memory of the SuperAnimal keypoints.\n",
     "\n",
@@ -785,7 +785,7 @@
    "source": [
     "#### Setup the weight init and dataset\n",
     "\n",
-    "First we do keypoint matching. This steps make it possible to understand the correspondance between the existing annotations and SuperAnimal annotations. This step produces 3 outputs\n",
+    "First we do keypoint matching. This steps make it possible to understand the correspondence between the existing annotations and SuperAnimal annotations. This step produces 3 outputs\n",
     "- The confusion matrix\n",
     "- The conversion table\n",
     "- Pseudo predictions over the whole dataset"
@@ -799,7 +799,7 @@
    "source": [
     "#### What is keypoint matching?\n",
     "\n",
-    "Because SuperAnimal models have their pre-defined keypoints that are potentially different from your annotations, we porposed this algorithm to minimize the gap between the model and the dataset. We use our model to perform zero-shot inference on the whole dataset. This gives pairs of predictions and ground truth for every image. Then, we cast the matching between models’ predictions (2D coordinates)\n",
+    "Because SuperAnimal models have their pre-defined keypoints that are potentially different from your annotations, we proposed this algorithm to minimize the gap between the model and the dataset. We use our model to perform zero-shot inference on the whole dataset. This gives pairs of predictions and ground truth for every image. Then, we cast the matching between models’ predictions (2D coordinates)\n",
     "and ground truth as bipartitematching using the Euclidean distance as the cost between paired of keypoints. We then solve the matching using the Hungarian algorithm. Thus for every image, we end up getting a matching matrix where 1 counts formatch and 0 counts for non-matching. Because the models’ predictions can be noisy from image to image, we average the aforementioned matching matrix across all the images and perform another bipartite matching, resulting in the final keypoint conversion table between the model and the dataset. Note that the quality of thematching will impact the performance\n",
     "of the model, especially for zero-shot. In the case where, e.g., the annotation nose is mistakenly converted to keypoint tail and vice versa, the model will have to unlearn the channel that corresponds to nose and tail (see also case study in Mathis et al.)."
    ]
@@ -843,7 +843,7 @@
    "source": [
     "#### Display the confusion matrix\n",
     "\n",
-    "The x axis lists the keypoints in the existing annotations. The y axis lists the keypoints in SuperAnimal keypoint space. Darker color encodes stronger correspondance between the human annotation and SuperAnimal annotations."
+    "The x axis lists the keypoints in the existing annotations. The y axis lists the keypoints in SuperAnimal keypoint space. Darker color encodes stronger correspondence between the human annotation and SuperAnimal annotations."
    ]
   },
   {
@@ -871,7 +871,7 @@
    },
    "source": [
     "#### Display the conversion table\n",
-    "The gt columns represents the keypoint names in the existing dataset. The MasterName represents the correspoinding keypoints in SuperAnimal keypoint space."
+    "The gt columns represents the keypoint names in the existing dataset. The MasterName represents the corresponding keypoints in SuperAnimal keypoint space."
    ]
   },
   {