smaller batch

tasks/image.py

@@ -248,8 +248,9 @@ async def evaluate_image(request: ImageEvaluationRequest):
     # Second pass - batch predictions
     for i, batch_start in enumerate(range(0, n_examples, BATCH_SIZE)):
         batch_end = min(batch_start + BATCH_SIZE, n_examples)
-        print(f"Processing batch {batch_start//BATCH_SIZE + 1} of {(n_examples + BATCH_SIZE - 1)//BATCH_SIZE}")
-        print(f"Batch start: {batch_start}, Batch end: {batch_end}")
+        if i % 100 == 0:
+            print(f"Processing batch {batch_start//BATCH_SIZE + 1} of {(n_examples + BATCH_SIZE - 1)//BATCH_SIZE}")
+            print(f"Batch start: {batch_start}, Batch end: {batch_end}")
         # Get batch of images
         batch_images = [test_dataset[i]['image'] for i in range(batch_start, batch_end)]
         
@@ -283,12 +284,10 @@ async def evaluate_image(request: ImageEvaluationRequest):
             # Process predicted boxes
             try:
                 if len(all_preds[idx]) < 1:
-                    print("No boxes found")
                     model_preds = [0, 0, 0, 0]
                 else:
                     model_preds = all_preds[idx]
             except:
-                print("No boxes found")
                 model_preds = [0, 0, 0, 0]
             pred_boxes.append(model_preds)