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Published 20 hours ago • verified publisher icon ivonajdenkoska

Release trained checkpoint

Open wenzhezhai opened this issue 1 year ago • 5 comments

Thanks for the author's impressive work! And I have a few questions.

  1. Do you have any plans to release the trained checkpoint for your model?
  2. I tried to reproduce your results on the COCO dataset, but the accuracy was very low. Here is the log of my training process:
Total num of params: 1777664
shuffle DB: train, b:10000, 2-way, 1-shot, 5-query, 0-repeats, resize:224
shuffle DB: val, b:100, 2-way, 1-shot, 1-query, 0-repeats, resize:224
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 0         Losses: [27.3474, 26.4712, 25.9338, 25.349, 25.0009, 24.364]
Step: 0         Training acc: [0.005  0.0025 0.0025 0.005  0.01   0.015 ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 100       Losses: [16.2629, 16.4309, 16.4842, 16.2964, 16.4591, 16.5697]
Step: 100       Training acc: [0.1175 0.12   0.1075 0.13   0.1325 0.115 ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 200       Losses: [14.6574, 14.5848, 14.5446, 14.5029, 14.5325, 14.4986]
Step: 200       Training acc: [0.11   0.11   0.1075 0.115  0.1125 0.1   ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 300       Losses: [13.8562, 13.9223, 13.823, 13.672, 13.8453, 13.8192]
Step: 300       Training acc: [0.1    0.09   0.105  0.105  0.0925 0.095 ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 400       Losses: [13.3061, 13.2029, 13.3253, 13.2227, 13.2487, 13.1655]
Step: 400       Training acc: [0.1125 0.1075 0.1    0.09   0.11   0.1175]

Model saved on path /multimodal-few-shot/models/
------ Meta-test 2-way, 1-shot (5-query) ------
Step: 400       Test acc: [0.118  0.122  0.1195 0.121  0.116  0.1135]

------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 500       Losses: [13.6204, 13.3988, 13.5718, 13.4468, 13.5434, 13.55]
Step: 500       Training acc: [0.12   0.1125 0.1325 0.11   0.115  0.135 ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 600       Losses: [13.0284, 13.0951, 13.1156, 13.1911, 13.0417, 13.1139]                                                                                                                 
Step: 600       Training acc: [0.1425 0.14   0.125  0.1375 0.1275 0.135 ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 700       Losses: [13.0562, 13.078, 13.227, 13.125, 12.9114, 13.0547]
Step: 700       Training acc: [0.16   0.1625 0.1625 0.145  0.1875 0.155 ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 800       Losses: [13.4406, 13.5702, 13.4736, 13.4705, 13.4559, 13.3458]
Step: 800       Training acc: [0.1325 0.135  0.135  0.13   0.13   0.13  ]

Model saved on path /multimodal-few-shot/models/
------ Meta-test 2-way, 1-shot (5-query) ------
Step: 800       Test acc: [0.1495 0.141  0.1445 0.143  0.1475 0.14  ]

------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 900       Losses: [12.3796, 12.4293, 12.3599, 12.3973, 12.3356, 12.4367]
Step: 900       Training acc: [0.125  0.155  0.1275 0.14   0.1525 0.14  ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 1000      Losses: [11.1597, 11.1914, 11.4077, 11.0616, 11.1694, 11.2945]
Step: 1000      Training acc: [0.1675 0.155  0.1475 0.17   0.16   0.1575]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 1100      Losses: [12.062, 12.0198, 12.0565, 12.0145, 12.168, 12.0419]
Step: 1100      Training acc: [0.1575 0.1725 0.155  0.1325 0.1675 0.1375]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 1200      Losses: [12.0617, 11.8894, 12.03, 12.0184, 12.064, 12.1158]
Step: 1200      Training acc: [0.1225 0.125  0.1325 0.13   0.1375 0.13  ]

Model saved on path /multimodal-few-shot/models/
------ Meta-test 2-way, 1-shot (5-query) ------
Step: 1200      Test acc: [0.1395 0.1505 0.154  0.134  0.1545 0.1575]

------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 1300      Losses: [12.2494, 12.2317, 12.2031, 12.0318, 12.146, 12.1562]
Step: 1300      Training acc: [0.115  0.115  0.1375 0.1375 0.14   0.135 ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 1400      Losses: [11.9626, 11.9661, 11.8697, 11.9094, 11.8787, 11.8339]
Step: 1400      Training acc: [0.115  0.13   0.12   0.1225 0.135  0.13  ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 1500      Losses: [13.2522, 12.9408, 12.9902, 12.9345, 12.8652, 12.9913]
Step: 1500      Training acc: [0.13   0.1525 0.125  0.1375 0.1275 0.1225]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 1600      Losses: [12.1111, 12.2421, 12.2779, 12.2388, 12.1456, 12.2824]
Step: 1600      Training acc: [0.1325 0.14   0.16   0.1375 0.15   0.1425]

Model saved on path /multimodal-few-shot/models/
------ Meta-test 2-way, 1-shot (5-query) ------
Step: 1600      Test acc: [0.148  0.1615 0.1495 0.1495 0.141  0.1436]

------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 1700      Losses: [11.8007, 11.9436, 11.9044, 11.8383, 11.9157, 11.9359]
Step: 1700      Training acc: [0.155  0.1625 0.17   0.165  0.1525 0.1525]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 1800      Losses: [11.7353, 11.7092, 11.663, 11.8356, 11.6716, 11.6708]
Step: 1800      Training acc: [0.125  0.12   0.1275 0.12   0.115  0.1075]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 1900      Losses: [12.8347, 12.818, 12.811, 12.9872, 12.7438, 12.7335]
Step: 1900      Training acc: [0.1525 0.145  0.1725 0.1525 0.15   0.1525]

Model saved on path /multimodal-few-shot/models/
Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 2000      Losses: [11.3431, 11.4844, 11.6313, 11.4486, 11.308, 11.3376]
Step: 2000      Training acc: [0.1925 0.1525 0.1725 0.155  0.1725 0.165 ]

Model saved on path /multimodal-few-shot/models/
------ Meta-test 2-way, 1-shot (5-query) ------
Step: 2000      Test acc: [0.156  0.143  0.1455 0.153  0.1515 0.1436]

------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 2100      Losses: [12.6283, 12.5109, 12.5109, 12.4421, 12.537, 12.7002]
Step: 2100      Training acc: [0.1475 0.13   0.1425 0.16   0.1225 0.125 ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 2200      Losses: [11.5735, 11.8118, 11.6213, 11.793, 11.7382, 11.7658]
Step: 2200      Training acc: [0.1525 0.16   0.16   0.135  0.1175 0.1375]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 2300      Losses: [12.602, 12.7457, 12.5895, 12.7572, 12.605, 12.6782]
Step: 2300      Training acc: [0.16   0.155  0.16   0.1575 0.165  0.15  ]

Model saved on path /multimodal-few-shot/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 2400      Losses: [11.4712, 11.6158, 11.4204, 11.4886, 11.5966, 11.5541]
Step: 2400      Training acc: [0.1525 0.14   0.1475 0.1575 0.1525 0.13  ]

Model saved on path /multimodal-few-shot/models/
------ Meta-test 2-way, 1-shot (5-query) ------
Step: 2400      Test acc: [0.163  0.1555 0.1545 0.155  0.168  0.1495]

Is this normal or did I miss something?

wenzhezhai avatar Jun 14 '23 01:06 wenzhezhai

I have the same problem. Here is the last log of my training process:

Model saved on path /home/huminyang/Code/multimodal-meta-learn/models/
------ Meta-training 2-way, 1-shot (5-query) ATT-mapper 4-prefix tokens------
Step: 2400      Losses: [11.39, 11.3287, 11.3388, 11.2218, 11.3529, 11.3879]
Step: 2400      Training acc: [0.1375 0.1525 0.155  0.1925 0.1575 0.1675]

Model saved on path /home/huminyang/Code/multimodal-meta-learn/models/
------ Meta-test 2-way, 1-shot (5-query) ------
Step: 2400      Test acc: [0.16   0.1555 0.1595 0.1575 0.166  0.156 ]

Did I miss something during training?

hu-my avatar Jun 16 '23 02:06 hu-my

Img: ['real_name_mi_shots_1_ways_2_id_0874_question.jpg'], GT answer: electric guitar, Pred. answer: a man wearing a skateboard is talking to a man wearing a skate Step: 2497 Test acc: [0. 0. 0. 0. 0. 0.] Img: ['real_name_mi_shots_1_ways_2_id_1883_question.jpg'], GT answer: vase, Pred. answer: a cup of water is held up to a woman's hand. The Step: 2498 Test acc: [0. 0. 0. 0. 0. 0.] Img: ['real_name_mi_shots_1_ways_2_id_0050_question.jpg'], GT answer: golden retriever, Pred. answer: a dog sitting on a shelf with a bottle of wine in front of Step: 2499 Test acc: [0. 0. 0. 0. 0. 0.] FINAL: Test acc: [0.002485 0.00237 0.002256 0.00217 0.002342 0.002 ]

YvoGao avatar Feb 28 '24 08:02 YvoGao

预训练的结果 Step: 2100 Training acc: [0.1325 0.115 0.115 0.1175 0.11 0.13 ] Step: 2200 Training acc: [0.16 0.1575 0.1625 0.155 0.1625 0.1525] Step: 2300 Training acc: [0.2 0.1775 0.185 0.205 0.1725 0.1825] Step: 2400 Training acc: [0.1575 0.1375 0.1575 0.16 0.175 0.18 ] Step: 2400 Test acc: [0.373 0.387 0.378 0.369 0.356 0.3926]

real_name_mi的结果 Img: ['real_name_mi_shots_1_ways_2_id_0874_question.jpg'], GT answer: electric guitar, Pred. answer: a man wearing a skateboard is talking to a man wearing a skate Step: 2497 Test acc: [0. 0. 0. 0. 0. 0.] Img: ['real_name_mi_shots_1_ways_2_id_1883_question.jpg'], GT answer: vase, Pred. answer: a cup of water is held up to a woman's hand. The Step: 2498 Test acc: [0. 0. 0. 0. 0. 0.] Img: ['real_name_mi_shots_1_ways_2_id_0050_question.jpg'], GT answer: golden retriever, Pred. answer: a dog sitting on a shelf with a bottle of wine in front of Step: 2499 Test acc: [0. 0. 0. 0. 0. 0.] FINAL: Test acc: [0.002485 0.00237 0.002256 0.00217 0.002342 0.002 ] 不知道是哪里出了问题

YvoGao avatar Feb 28 '24 08:02 YvoGao

预训练的结果 Step: 2100 Training acc: [0.1325 0.115 0.115 0.1175 0.11 0.13 ] Step: 2200 Training acc: [0.16 0.1575 0.1625 0.155 0.1625 0.1525] Step: 2300 Training acc: [0.2 0.1775 0.185 0.205 0.1725 0.1825] Step: 2400 Training acc: [0.1575 0.1375 0.1575 0.16 0.175 0.18 ] Step: 2400 Test acc: [0.373 0.387 0.378 0.369 0.356 0.3926]

real_name_mi的结果 Img: ['real_name_mi_shots_1_ways_2_id_0874_question.jpg'], GT answer: electric guitar, Pred. answer: a man wearing a skateboard is talking to a man wearing a skate Step: 2497 Test acc: [0. 0. 0. 0. 0. 0.] Img: ['real_name_mi_shots_1_ways_2_id_1883_question.jpg'], GT answer: vase, Pred. answer: a cup of water is held up to a woman's hand. The Step: 2498 Test acc: [0. 0. 0. 0. 0. 0.] Img: ['real_name_mi_shots_1_ways_2_id_0050_question.jpg'], GT answer: golden retriever, Pred. answer: a dog sitting on a shelf with a bottle of wine in front of Step: 2499 Test acc: [0. 0. 0. 0. 0. 0.] FINAL: Test acc: [0.002485 0.00237 0.002256 0.00217 0.002342 0.002 ] 不知道是哪里出了问题

我也和你有类似的结果，所以你最后解决了吗

Zzzzzzzzzzj avatar Sep 14 '24 07:09 Zzzzzzzzzzj

没有，还没有解决这个事情，也许把包换成低版本的可能会解决这个问题，这个做法在其他项目上成功了

---- Replied Message ---- | From | Zhang @.> | | Date | 09/14/2024 15:48 | | To | @.> | | Cc | @.> , @.> | | Subject | Re: [ivonajdenkoska/multimodal-meta-learn] Release trained checkpoint (Issue #3) |

预训练的结果 Step: 2100 Training acc: [0.1325 0.115 0.115 0.1175 0.11 0.13 ] Step: 2200 Training acc: [0.16 0.1575 0.1625 0.155 0.1625 0.1525] Step: 2300 Training acc: [0.2 0.1775 0.185 0.205 0.1725 0.1825] Step: 2400 Training acc: [0.1575 0.1375 0.1575 0.16 0.175 0.18 ] Step: 2400 Test acc: [0.373 0.387 0.378 0.369 0.356 0.3926]

real_name_mi的结果 Img: ['real_name_mi_shots_1_ways_2_id_0874_question.jpg'], GT answer: electric guitar, Pred. answer: a man wearing a skateboard is talking to a man wearing a skate Step: 2497 Test acc: [0. 0. 0. 0. 0. 0.] Img: ['real_name_mi_shots_1_ways_2_id_1883_question.jpg'], GT answer: vase, Pred. answer: a cup of water is held up to a woman's hand. The Step: 2498 Test acc: [0. 0. 0. 0. 0. 0.] Img: ['real_name_mi_shots_1_ways_2_id_0050_question.jpg'], GT answer: golden retriever, Pred. answer: a dog sitting on a shelf with a bottle of wine in front of Step: 2499 Test acc: [0. 0. 0. 0. 0. 0.] FINAL: Test acc: [0.002485 0.00237 0.002256 0.00217 0.002342 0.002 ] 不知道是哪里出了问题

我也和你有类似的结果，所以你最后解决了吗

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YvoGao avatar Sep 22 '24 06:09 YvoGao