handwriting done

Author: d31003

bash_scripts/handwriting_script.sh

@@ -1,10 +1,7 @@
 # classification
-# CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=10
-# CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=25
-CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=30
+CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=10
+CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=25
 CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=50
-CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=60
-CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=70
-# CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=100
-# CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=200
-# CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=500
+CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=100
+CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=200
+CUDA_VISIBLE_DEVICES=1 poetry run python mmda/bimodal_classification.py dataset=handwriting handwriting.sim_dim=500

config/main.yaml

@@ -104,10 +104,10 @@ imagenet:
     label_embeddings: ${imagenet.paths.dataset_path}_${text_encoder}_label_embeddings.npy
 
 handwriting:
-  sim_dim: 700 # dimension of the similarity score and the CCA transformation
-  equal_weights: False
+  sim_dim: 50 # dimension of the similarity score and the CCA transformation
+  equal_weights: True
   img_encoder: "tsfresh"
-  text_encoder: "clip"
+  text_encoder: "gtr"
   train_test_ratios: [0.85]
   shuffle: False
   paths: