Hello!
Thank you for making the code for this project public.

I would have one question: are the embeddings in Codes_Wiki_W2V_X_Y.txt ordered according to the classes in classes.txt?

When I tried to train GoogLeNet (fine-tuning) on CUB dataset with attributes, it did not converge. I just followed hyperparameters in SCoRe paper and this github code. The hyperparameters are as follows.
python score_train.py G_Attr CUB Attributes GoogLeNet -g 0.01 -c 1.0 --iters 700 --gpu 0 --init_lr 0.0005 --snapshot ./G_Attr/inception_v1.caffemodel |& tee ./G_Attr/train_G_Attr.log

test_iter: 20
test_iter: 20
test_interval: 100
base_lr: 0.0005
display: 20
max_iter: 700
lr_policy: "fixed"
momentum: 0.9
weight_decay: 0.0005
snapshot: 5000

I trained on Ubuntu 16.04 with GTX 1080 Ti, CUDA 8.0, and cuDNN v7.
Could you give me any hints for convergence?

Results:

I1222 15:54:06.236789 26844 solver.cpp:397] Test net output #0: SCoRe/eval/obj/accuracy = 0.0210938
I1222 15:54:06.388346 26844 solver.cpp:218] Iteration 700 (3.90624 iter/s, 5.12002s/20 iters), loss = 5.02677
I1222 15:54:06.394364 26844 solver.cpp:237] Train net output #0: SCoRe/cwReg = 0.0053981 (* 1 = 0.0053981 loss)
I1222 15:54:06.394374 26844 solver.cpp:237] Train net output 1: SCoRe/eval/obj/accuracy = 0
I1222 15:54:06.394381 26844 solver.cpp:237] Train net output 2: SCoRe/objLoss = 5.02252 (* 0.990099 = 4.9728 loss)
I1222 15:54:06.394387 26844 solver.cpp:237] Train net output #3: SCoRe/semLoss = 216.479 (* 0.000224393 = 0.0485763 loss)
I1222 15:54:06.394403 26844 sgd_solver.cpp:105] Iteration 700, lr = 0.0005

I carry out train_eval_CUB.sh use linux,inception_v1.caffemodel. When carry out to

Evaluate SCoRe model

python score_eval.py ${MODEL_DIR} CUB Attributes GoogLeNet

I meet this problem and i can't slove it.
Layer SCoRe/obj/fc_target (weights) initialized
Traceback (most recent call last):
File "score_eval.py", line 139, in
main(parse_cmd())
File "score_eval.py", line 134, in main
mca, acc, semAcc, semAUC = evaluate_model(classes, constrains, LMDBs, args)
File "score_eval.py", line 111, in evaluate_model
semAcc[mode], semAUC[mode] = eval_semantics(scores, semantics, args)
File "score_eval.py", line 50, in eval_semantics
acc[s] = (pred*(lbl-0.5) > 0).astype(float).mean()
ValueError: operands could not be broadcast together with shapes (10,) (2933,)

Can you give me some suggestion?