diff --git a/answer_classifier_cached_features/select_best_model.py b/answer_classifier_cached_features/select_best_model.py
index 8f77e06be9b886f8e9c01d9a8ea76e6cafe805f6..ee6b2706151d02216b663c8368504dcebd641c72 100644
--- a/answer_classifier_cached_features/select_best_model.py
+++ b/answer_classifier_cached_features/select_best_model.py
@@ -245,6 +245,7 @@ def eval_model(model_to_eval, results_json):
         0,
         0,
         0,
+        constants.answer_obj_atr_loss_wt,
         resnet_feat_dim=constants.resnet_feat_dim,
         training=False)
 
diff --git a/constants_crunchy.py b/constants_crunchy.py
index 70b07fc70b8bdbb7a5fc3aa1677970b7c8c8192f..9402eb2b6f3c846ecfa06ac9359d986c99189564 100644
--- a/constants_crunchy.py
+++ b/constants_crunchy.py
@@ -159,13 +159,13 @@ num_test_questions = 0
 
 # Answer classifier training params
 answer_batch_size = 50
-answer_num_epochs = 4
+answer_num_epochs = 6
 answer_offset = 0
 answer_obj_atr_loss_wt = 0.1
 answer_regularization_coeff = 1e-5
 answer_queue_size = 500
 answer_embedding_dim = 600
-answer_lr = 1e-3
+answer_lr = 1e-4
 answer_log_every_n_iter = 500
 answer_output_dir = os.path.join(
     global_experiment_dir,
@@ -185,12 +185,12 @@ answer_model = os.path.join(
 num_regions_with_labels = 100
 
 # Answer fine tune params
-answer_fine_tune_from_iter = 17000
+answer_fine_tune_from_iter = 19500
 answer_fine_tune_from = answer_model + '-' + str(answer_fine_tune_from_iter)
 
 # Answer eval params
 answer_eval_on = 'val'
-answer_model_to_eval = answer_model + '-13000'
+answer_model_to_eval = answer_model + '-49500'
 
 answer_eval_data_json = os.path.join(
     answer_output_dir,