diff --git a/tcn_hpl/callbacks/plot_metrics.py b/tcn_hpl/callbacks/plot_metrics.py
index 850702bf8..1a4206e31 100644
--- a/tcn_hpl/callbacks/plot_metrics.py
+++ b/tcn_hpl/callbacks/plot_metrics.py
@@ -10,6 +10,7 @@
 from pytorch_lightning.utilities.types import STEP_OUTPUT
 from sklearn.metrics import confusion_matrix
 import torch
+import kwcoco
 
 try:
     from aim import Image
@@ -345,6 +346,7 @@ def on_test_batch_end(
         self._val_all_targets.append(outputs["targets"].cpu())
         self._val_all_source_vids.append(outputs["source_vid"].cpu())
         self._val_all_source_frames.append(outputs["source_frame"].cpu())
+        self._preds_dset_output_fpath = self.output_dir / "tcn_activity_predictions.kwcoco.json"
 
     def on_test_epoch_end(
         self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"
@@ -362,6 +364,36 @@ def on_test_epoch_end(
         test_acc = pl_module.test_metrics.acc.compute()
         test_f1 = pl_module.test_metrics.f1.compute()
 
+        # Create activity predictions KWCOCO JSON
+        truth_dset_fpath = trainer.datamodule.hparams["coco_test_activities"]
+        truth_dset = kwcoco.CocoDataset(truth_dset_fpath)
+        acts_dset = kwcoco.CocoDataset()
+        acts_dset.fpath = self._preds_dset_output_fpath
+        acts_dset.dataset['videos'] = truth_dset.dataset['videos']
+        acts_dset.dataset['images'] = truth_dset.dataset['images']
+        acts_dset.dataset['categories'] = truth_dset.dataset['categories']
+        acts_dset.index.build(acts_dset)
+        # Create numpy lookup tables
+        for i in range(len(all_preds)):
+            frame_index = all_source_frames[i].item()
+            video_id = all_source_vids[i].item()
+            # Now get the image_id that matches the frame_index and video_id.
+            sorted_img_ids_for_one_video = acts_dset.index.vidid_to_gids[int(video_id)]
+            image_id = sorted_img_ids_for_one_video[frame_index]
+            # Sanity check: this image_id corresponds to the frame_index and video_id
+            assert acts_dset.index.imgs[image_id]['frame_index'] == frame_index
+            assert acts_dset.index.imgs[image_id]['video_id'] == video_id
+
+            acts_dset.add_annotation(
+                image_id=image_id,
+                category_id=all_preds[i].item(),
+                score=all_probs[i][all_preds[i]].item(),
+                prob=all_probs[i].numpy().tolist(),
+            )  
+        print(f"Dumping activities file to {acts_dset.fpath}")
+        acts_dset.dump(acts_dset.fpath, newlines=True)  
+
+
         #
         # Plot per-video class predictions vs. GT across progressive frames in
         # that video.