Initial methods to skip already-processed images.

ami/base/models.py

@@ -13,7 +13,7 @@ def __str__(self) -> str:
         """All django models should have this method."""
         if hasattr(self, "name"):
             name = getattr(self, "name") or "Untitled"
-            return name
+            return f"#{self.pk} {name}"
         else:
             return f"{self.__class__.__name__} #{self.pk}"
 

ami/jobs/models.py

@@ -401,6 +401,7 @@ def run(self):
                     deployment=self.deployment,
                     source_images=[self.source_image_single] if self.source_image_single else None,
                     job_id=self.pk,
+                    skip_processed=True,
                     # shuffle=self.shuffle,
                 )
             )
@@ -447,12 +448,12 @@ def run(self):
                     continue
 
                 total_detections += len(results.detections)
-                total_classifications += len(results.classifications)
+                total_classifications += len([c for d in results.detections for c in d.classifications])
                 self.progress.update_stage(
                     "process",
                     status=JobState.STARTED,
                     progress=(i + 1) / len(chunks),
-                    proccessed=(i + 1) * CHUNK_SIZE,
+                    processed=(i + 1) * CHUNK_SIZE,
                     remaining=image_count - (i + 1) * CHUNK_SIZE,
                     detections=total_detections,
                     classifications=total_classifications,

ami/main/api/views.py

@@ -232,7 +232,7 @@ def get_queryset(self) -> QuerySet:
                 .filter(has_detections=has_detections)
                 .order_by(
                     "?"
-                )  # Random order is here for our demo ML backend to limit the same images from being proccessed
+                )  # Random order is here for our demo ML backend to limit the same images from being processed
                 # @TODO remove this when we have a real queue for the ML backend
             )
         return queryset

ami/main/models.py

@@ -1393,6 +1393,9 @@ class Classification(BaseModel):
     class Meta:
         ordering = ["-created_at", "-score"]
 
+    def __str__(self) -> str:
+        return f"#{self.pk} to Taxon #{self.taxon_id} ({self.score:.2f}) by Algorithm #{self.algorithm_id}"
+
 
 @final
 class Detection(BaseModel):
@@ -1538,6 +1541,9 @@ def save(self, update_calculated_fields=True, *args, **kwargs):
         # if not self.occurrence:
         #     self.associate_new_occurrence()
 
+    def __str__(self) -> str:
+        return f"#{self.pk} from SourceImage #{self.source_image_id} with Algorithm #{self.detection_algorithm_id}"
+
 
 @final
 class OccurrenceManager(models.Manager):

ami/ml/models/algorithm.py

@@ -4,6 +4,7 @@
 
 if TYPE_CHECKING:
     from ami.main.models import Classification
+    from ami.ml.models import Pipeline
 
 import typing
 
@@ -22,11 +23,12 @@ class Algorithm(BaseModel):
     description = models.TextField(blank=True)
     version = models.IntegerField(default=1)
     version_name = models.CharField(max_length=255, blank=True)
-    url = models.URLField(blank=True)
+    url = models.URLField(blank=True)  # URL to the model homepage, origin or docs (huggingface, wandb, etc.)
 
     # api_base_url = models.URLField(blank=True)
     # api = models.CharField(max_length=255, blank=True)
 
+    pipelines: models.QuerySet[Pipeline]
     classifications: models.QuerySet[Classification]
 
     class Meta: