# # 128D-cifar100
# python may16/hyp.py --emb_path embs/cifar100_hierarchy_128d.pth --epochs 200 --dataset cifar100
# Acc: 0.7419, mAP: 0.7360, SmAP: 0.8570
# python may16/hyp.py --emb_path embs/iclr24_cifar_128d.pth --epochs 200 --dataset cifar100
# Acc: 0.7344, mAP: 0.7278, SmAP: 0.8550
# python may16/hyp.py --emb_path embs/iclr24_cifar_64d.pth --epochs 300 --dataset cifar100
# Acc: 0.7359, mAP: 0.7361, SmAP: 0.8530
# python may16/euc2.py --emb_path embs/iclr24_cifar_128d.pth --epochs 200 --dataset cifar100
# Acc: 0.7274, mAP: 0.7129, SmAP: 0.8275


# python may16/hyp.py --emb_path embs/iclr24_cifar_128d.pth --epochs 300 --dataset cifar100
# Acc: 0.7482, mAP: 0.7416, SmAP: 0.8598
# python may16/euc2.py --emb_path embs/iclr24_cifar_256d.pth --epochs 300 --dataset cifar100
# Acc: 0.7409, mAP: 0.7298, SmAP: 0.8327

# 16D-cifar100 HYP
# python may16/hyp.py --emb_path embs/iclr24_cifar_16d.pth --epochs 300 --dataset cifar100
# Acc: 0.7096, mAP: 0.7089, SmAP: 0.8328

# python may16/hyp.py --emb_path embs/cifar100_hierarchysph_16d.pth --epochs 300 --dataset cifar100
# Acc: 0.7162, mAP: 0.7219, SmAP: 0.8274

# 32D-cifar100 HYP
# python may16/hyp.py --emb_path embs/iclr24_cifar_32d.pth --epochs 300 --dataset cifar100
# Acc: 0.7341, mAP: 0.7305, SmAP: 0.8497
# python may16/hyp.py --emb_path embs/cifar100_hierarchysph_32d.pth --epochs 300 --dataset cifar100
# Acc: 0.7343, mAP: 0.7385, SmAP: 0.8426

# 32D-cifar100 EUC
# python may16/euc2.py --emb_path embs/cifar100_hierarchysph_32d.pth --epochs 300 --dataset cifar100
# Acc: 0.7440, mAP: 0.7446, SmAP: 0.8430
# 64D-cifar100 EUC
# python may16/euc2.py --emb_path embs/cifar100_hierarchysph_64d.pth --epochs 300 --dataset cifar100
# Acc: 0.7511, mAP: 0.7561, SmAP: 0.8522
# 128D-cifar100 EUC
# python may16/euc2.py --emb_path embs/cifar100_hierarchysph_128d.pth --epochs 300 --dataset cifar100
# Acc: 0.7557, mAP: 0.7607, SmAP: 0.8589
# 256D-cifar100 EUC
# python may16/euc2.py --emb_path embs/cifar100_hierarchysph_256d.pth --epochs 300 --dataset cifar100
# Acc: 0.7640, mAP: 0.7642, SmAP: 0.8631



# # 128D-mim
python may16/hyp.py --emb_path embs/moments_depth_v5_128d.pth --epochs 200 --dataset mim

# # 128D-imagenet 
# python may16/hyp.py --emb_path embs/imagenet_hierarchy_128d.pth --epochs 200 --dataset imagenet


# # 256D
# python may16/hyp.py --emb_path embs/cifar100_hierarchy_256d.pth --epochs 200 --dataset cifar100
# Acc: 0.7453, mAP: 0.7411, SmAP: 0.8611

# # shp 128D
# python may16/hyp.py --emb_path embs/cifar100_hierarchysph_128d.pth --epochs 200 --dataset cifar100
# Acc: 0.7589, mAP: 0.7601, SmAP: 0.8561
# python may16/euc2.py --emb_path embs/cifar100_hierarchysph_128d.pth --epochs 300 --dataset cifar100
# Acc: 0.7597, mAP: 0.7607, SmAP: 0.8608
# Acc: 0.7577, mAP: 0.7591, SmAP: 0.8573

# # shp 256D
# python may16/hyp.py --emb_path embs/cifar100_hierarchysph_256d.pth --epochs 200 --dataset cifar100
# Acc: 0.7587, mAP: 0.7603, SmAP: 0.8601
# python may16/euc2.py --emb_path embs/cifar100_hierarchysph_256d.pth --epochs 200 --dataset cifar100
# Acc: 0.7623, mAP: 0.7589, SmAP: 0.8609

# # shp 512D
# python may16/hyp.py --emb_path embs/cifar100_hierarchysph_512d.pth --epochs 200 --dataset cifar100
# Acc: 0.7623, mAP: 0.7611, SmAP: 0.8571
# python may16/euc2.py --emb_path embs/cifar100_hierarchysph_512d.pth --epochs 200 --dataset cifar100
# Acc: 0.7562, mAP: 0.7498, SmAP: 0.8537