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NourFakih
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image-captioning-Vit-GPT2-Flickr8k

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vision-encoder-decoder
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  1. README.md +52 -52
  2. model.safetensors +1 -1
  3. runs/May01_11-25-38_b47eb30dbf80/events.out.tfevents.1714562769.b47eb30dbf80.34.0 +2 -2
README.md CHANGED
@@ -18,10 +18,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nlpconnect/vit-gpt2-image-captioning](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.4624
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- - Rouge1: 38.4598
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- - Rouge2: 14.1356
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- - Rougel: 35.4001
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- - Rougelsum: 35.4044
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  - Gen Len: 12.1355
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  ## Model description
@@ -53,54 +53,54 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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- | 0.5495 | 0.06 | 500 | 0.4942 | 35.0813 | 11.7169 | 32.4184 | 32.4273 | 11.5738 |
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- | 0.4945 | 0.12 | 1000 | 0.4903 | 35.4868 | 12.037 | 32.835 | 32.8388 | 11.8682 |
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- | 0.4984 | 0.19 | 1500 | 0.4862 | 35.3878 | 11.996 | 32.8196 | 32.8268 | 12.0544 |
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- | 0.4783 | 0.25 | 2000 | 0.4808 | 36.1063 | 12.3478 | 33.4632 | 33.4783 | 11.3468 |
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- | 0.4736 | 0.31 | 2500 | 0.4772 | 35.9266 | 12.3362 | 33.5046 | 33.5103 | 11.1066 |
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- | 0.4685 | 0.37 | 3000 | 0.4708 | 36.9089 | 13.0915 | 34.2896 | 34.2995 | 11.4739 |
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- | 0.4687 | 0.43 | 3500 | 0.4704 | 36.1844 | 12.5731 | 33.4609 | 33.4733 | 11.9201 |
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- | 0.4709 | 0.49 | 4000 | 0.4696 | 36.1774 | 12.8262 | 33.3824 | 33.3814 | 12.1733 |
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- | 0.4575 | 0.56 | 4500 | 0.4675 | 37.4417 | 13.7581 | 34.5386 | 34.5523 | 12.6302 |
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- | 0.4484 | 0.62 | 5000 | 0.4662 | 36.6864 | 13.0727 | 33.9056 | 33.9339 | 12.6007 |
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- | 0.4507 | 0.68 | 5500 | 0.4656 | 36.5144 | 12.7924 | 34.0484 | 34.0759 | 11.4316 |
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- | 0.4445 | 0.74 | 6000 | 0.4628 | 37.0553 | 13.3404 | 34.4096 | 34.4153 | 12.3211 |
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- | 0.4557 | 0.8 | 6500 | 0.4594 | 37.3241 | 13.1468 | 34.45 | 34.4658 | 12.2522 |
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- | 0.4451 | 0.87 | 7000 | 0.4600 | 37.33 | 13.5726 | 34.6534 | 34.6635 | 12.0494 |
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- | 0.4381 | 0.93 | 7500 | 0.4588 | 37.6255 | 13.8048 | 34.817 | 34.8252 | 12.1347 |
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- | 0.4357 | 0.99 | 8000 | 0.4571 | 37.2088 | 13.4177 | 34.3316 | 34.3372 | 12.2670 |
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- | 0.3869 | 1.05 | 8500 | 0.4612 | 37.7054 | 13.683 | 34.9637 | 34.9821 | 11.3216 |
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- | 0.377 | 1.11 | 9000 | 0.4616 | 37.2701 | 13.2182 | 34.3249 | 34.3396 | 12.3221 |
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- | 0.3736 | 1.17 | 9500 | 0.4607 | 37.2101 | 13.1285 | 34.3812 | 34.3767 | 11.8274 |
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- | 0.3801 | 1.24 | 10000 | 0.4617 | 37.9963 | 13.7537 | 35.2402 | 35.2374 | 11.6079 |
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- | 0.3816 | 1.3 | 10500 | 0.4599 | 37.3247 | 13.619 | 34.6494 | 34.6538 | 12.2101 |
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- | 0.377 | 1.36 | 11000 | 0.4619 | 37.2827 | 13.4471 | 34.3588 | 34.3861 | 12.3911 |
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- | 0.3745 | 1.42 | 11500 | 0.4604 | 37.5469 | 13.3948 | 34.5403 | 34.5613 | 12.2747 |
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- | 0.3785 | 1.48 | 12000 | 0.4568 | 38.085 | 14.0087 | 35.0549 | 35.0564 | 12.3179 |
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- | 0.3675 | 1.54 | 12500 | 0.4587 | 37.6241 | 13.8529 | 34.7614 | 34.7853 | 11.8732 |
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- | 0.3731 | 1.61 | 13000 | 0.4554 | 38.4418 | 14.1464 | 35.6658 | 35.6502 | 11.4294 |
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- | 0.3731 | 1.67 | 13500 | 0.4548 | 37.9045 | 13.7524 | 34.9001 | 34.9092 | 12.1241 |
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- | 0.371 | 1.73 | 14000 | 0.4542 | 38.412 | 14.212 | 35.473 | 35.4781 | 12.1014 |
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- | 0.3615 | 1.79 | 14500 | 0.4551 | 38.0734 | 14.1066 | 35.1289 | 35.1552 | 12.1135 |
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- | 0.3687 | 1.85 | 15000 | 0.4550 | 38.1762 | 14.1402 | 35.288 | 35.2936 | 12.2255 |
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- | 0.3711 | 1.92 | 15500 | 0.4532 | 37.6439 | 13.611 | 34.7558 | 34.7601 | 12.1632 |
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- | 0.3685 | 1.98 | 16000 | 0.4515 | 38.5682 | 14.5305 | 35.552 | 35.5703 | 11.9162 |
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- | 0.3333 | 2.04 | 16500 | 0.4626 | 38.4527 | 14.4649 | 35.6252 | 35.6307 | 11.9506 |
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- | 0.3129 | 2.1 | 17000 | 0.4660 | 38.203 | 14.0699 | 35.1626 | 35.1595 | 12.3313 |
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- | 0.3155 | 2.16 | 17500 | 0.4674 | 37.8903 | 13.9159 | 34.9097 | 34.9101 | 12.4853 |
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- | 0.3134 | 2.22 | 18000 | 0.4644 | 38.1489 | 13.9448 | 35.0351 | 35.0351 | 11.9748 |
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- | 0.3167 | 2.29 | 18500 | 0.4653 | 37.8449 | 13.9106 | 34.7773 | 34.7854 | 12.5273 |
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- | 0.322 | 2.35 | 19000 | 0.4673 | 37.9832 | 14.0115 | 34.8505 | 34.8597 | 12.4680 |
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- | 0.312 | 2.41 | 19500 | 0.4641 | 38.4627 | 14.2528 | 35.4297 | 35.4377 | 11.9315 |
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- | 0.3173 | 2.47 | 20000 | 0.4654 | 38.1591 | 13.9126 | 35.1114 | 35.1042 | 12.4845 |
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- | 0.3081 | 2.53 | 20500 | 0.4640 | 38.6969 | 14.3244 | 35.6933 | 35.692 | 11.8932 |
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- | 0.3093 | 2.6 | 21000 | 0.4633 | 38.2944 | 14.103 | 35.2407 | 35.2629 | 11.8932 |
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- | 0.3154 | 2.66 | 21500 | 0.4637 | 38.0668 | 13.7427 | 35.0547 | 35.0585 | 12.1310 |
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- | 0.3096 | 2.72 | 22000 | 0.4630 | 38.3647 | 14.0445 | 35.2568 | 35.2511 | 12.2591 |
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- | 0.3101 | 2.78 | 22500 | 0.4627 | 38.6366 | 14.3013 | 35.4955 | 35.4956 | 12.2836 |
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- | 0.309 | 2.84 | 23000 | 0.4620 | 38.3486 | 14.0403 | 35.3173 | 35.3265 | 12.3281 |
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- | 0.312 | 2.9 | 23500 | 0.4623 | 38.423 | 14.0759 | 35.3766 | 35.3853 | 12.2208 |
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- | 0.3135 | 2.97 | 24000 | 0.4624 | 38.4598 | 14.1356 | 35.4001 | 35.4044 | 12.1355 |
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  ### Framework versions
 
18
  This model is a fine-tuned version of [nlpconnect/vit-gpt2-image-captioning](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
  - Loss: 0.4624
21
+ - Rouge1: 38.4609
22
+ - Rouge2: 14.1268
23
+ - Rougel: 35.4304
24
+ - Rougelsum: 35.391
25
  - Gen Len: 12.1355
26
 
27
  ## Model description
 
53
 
54
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
55
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
56
+ | 0.5495 | 0.06 | 500 | 0.4942 | 35.0812 | 11.7357 | 32.4228 | 32.4251 | 11.5738 |
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+ | 0.4945 | 0.12 | 1000 | 0.4903 | 35.4943 | 12.0207 | 32.8571 | 32.8486 | 11.8682 |
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+ | 0.4984 | 0.19 | 1500 | 0.4862 | 35.3652 | 11.9707 | 32.8296 | 32.8126 | 12.0544 |
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+ | 0.4783 | 0.25 | 2000 | 0.4808 | 36.1048 | 12.3597 | 33.4635 | 33.4504 | 11.3468 |
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+ | 0.4736 | 0.31 | 2500 | 0.4772 | 35.9342 | 12.343 | 33.519 | 33.495 | 11.1066 |
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+ | 0.4685 | 0.37 | 3000 | 0.4708 | 36.8985 | 13.0743 | 34.3294 | 34.2978 | 11.4739 |
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+ | 0.4687 | 0.43 | 3500 | 0.4704 | 36.1934 | 12.5721 | 33.4731 | 33.4671 | 11.9201 |
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+ | 0.4709 | 0.49 | 4000 | 0.4696 | 36.1822 | 12.8306 | 33.4001 | 33.3673 | 12.1733 |
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+ | 0.4575 | 0.56 | 4500 | 0.4675 | 37.4471 | 13.7553 | 34.5655 | 34.5384 | 12.6302 |
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+ | 0.4484 | 0.62 | 5000 | 0.4662 | 36.6786 | 13.0601 | 33.9348 | 33.8999 | 12.6007 |
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+ | 0.4507 | 0.68 | 5500 | 0.4656 | 36.506 | 12.7992 | 34.0665 | 34.0409 | 11.4316 |
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+ | 0.4445 | 0.74 | 6000 | 0.4628 | 37.0737 | 13.3324 | 34.416 | 34.3902 | 12.3211 |
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+ | 0.4557 | 0.8 | 6500 | 0.4594 | 37.3349 | 13.1633 | 34.4709 | 34.4503 | 12.2522 |
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+ | 0.4451 | 0.87 | 7000 | 0.4600 | 37.3384 | 13.5699 | 34.6726 | 34.6555 | 12.0494 |
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+ | 0.4381 | 0.93 | 7500 | 0.4588 | 37.6164 | 13.7855 | 34.8467 | 34.8084 | 12.1347 |
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+ | 0.4357 | 0.99 | 8000 | 0.4571 | 37.2047 | 13.4341 | 34.3383 | 34.3121 | 12.2670 |
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+ | 0.3869 | 1.05 | 8500 | 0.4612 | 37.684 | 13.6922 | 34.9914 | 34.9721 | 11.3216 |
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+ | 0.377 | 1.11 | 9000 | 0.4616 | 37.2615 | 13.2059 | 34.3375 | 34.3327 | 12.3221 |
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+ | 0.3736 | 1.17 | 9500 | 0.4607 | 37.2109 | 13.1387 | 34.3923 | 34.3638 | 11.8274 |
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+ | 0.3801 | 1.24 | 10000 | 0.4617 | 38.0033 | 13.7561 | 35.2434 | 35.2414 | 11.6079 |
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+ | 0.3816 | 1.3 | 10500 | 0.4599 | 37.3453 | 13.622 | 34.6495 | 34.639 | 12.2101 |
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+ | 0.377 | 1.36 | 11000 | 0.4619 | 37.2996 | 13.4583 | 34.3777 | 34.3525 | 12.3911 |
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+ | 0.3745 | 1.42 | 11500 | 0.4604 | 37.5448 | 13.3841 | 34.5785 | 34.5532 | 12.2747 |
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+ | 0.3785 | 1.48 | 12000 | 0.4568 | 38.0769 | 14.0089 | 35.0744 | 35.0605 | 12.3179 |
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+ | 0.3675 | 1.54 | 12500 | 0.4587 | 37.6284 | 13.8277 | 34.7837 | 34.7618 | 11.8732 |
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+ | 0.3731 | 1.61 | 13000 | 0.4554 | 38.433 | 14.1461 | 35.6757 | 35.6683 | 11.4294 |
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+ | 0.3731 | 1.67 | 13500 | 0.4548 | 37.9065 | 13.7526 | 34.9091 | 34.8919 | 12.1241 |
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+ | 0.371 | 1.73 | 14000 | 0.4542 | 38.4064 | 14.2136 | 35.4845 | 35.4671 | 12.1014 |
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+ | 0.3615 | 1.79 | 14500 | 0.4551 | 38.0695 | 14.1042 | 35.162 | 35.1427 | 12.1135 |
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+ | 0.3687 | 1.85 | 15000 | 0.4550 | 38.1978 | 14.1243 | 35.3107 | 35.2821 | 12.2255 |
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+ | 0.3711 | 1.92 | 15500 | 0.4532 | 37.661 | 13.603 | 34.7601 | 34.7467 | 12.1632 |
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+ | 0.3685 | 1.98 | 16000 | 0.4515 | 38.5727 | 14.5345 | 35.5855 | 35.5585 | 11.9162 |
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+ | 0.3333 | 2.04 | 16500 | 0.4626 | 38.4657 | 14.4726 | 35.6431 | 35.6119 | 11.9506 |
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+ | 0.3129 | 2.1 | 17000 | 0.4660 | 38.2002 | 14.0689 | 35.1851 | 35.1748 | 12.3313 |
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+ | 0.3155 | 2.16 | 17500 | 0.4674 | 37.8919 | 13.91 | 34.9167 | 34.9154 | 12.4853 |
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+ | 0.3134 | 2.22 | 18000 | 0.4644 | 38.1576 | 13.9371 | 35.0486 | 35.0252 | 11.9748 |
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+ | 0.3167 | 2.29 | 18500 | 0.4653 | 37.8516 | 13.9029 | 34.7959 | 34.7847 | 12.5273 |
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+ | 0.322 | 2.35 | 19000 | 0.4673 | 37.9883 | 14.0127 | 34.8667 | 34.841 | 12.4680 |
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+ | 0.312 | 2.41 | 19500 | 0.4641 | 38.4611 | 14.238 | 35.4465 | 35.417 | 11.9315 |
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+ | 0.3173 | 2.47 | 20000 | 0.4654 | 38.1477 | 13.9164 | 35.1148 | 35.0905 | 12.4845 |
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+ | 0.3081 | 2.53 | 20500 | 0.4640 | 38.7153 | 14.3282 | 35.7048 | 35.6923 | 11.8932 |
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+ | 0.3093 | 2.6 | 21000 | 0.4633 | 38.2932 | 14.0961 | 35.2736 | 35.2308 | 11.8932 |
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+ | 0.3154 | 2.66 | 21500 | 0.4637 | 38.0708 | 13.7374 | 35.0722 | 35.055 | 12.1310 |
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+ | 0.3096 | 2.72 | 22000 | 0.4630 | 38.3722 | 14.041 | 35.2847 | 35.2425 | 12.2591 |
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+ | 0.3101 | 2.78 | 22500 | 0.4627 | 38.6372 | 14.2961 | 35.5118 | 35.4819 | 12.2836 |
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+ | 0.309 | 2.84 | 23000 | 0.4620 | 38.3596 | 14.0396 | 35.3285 | 35.3 | 12.3281 |
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+ | 0.312 | 2.9 | 23500 | 0.4623 | 38.4268 | 14.0768 | 35.4015 | 35.3656 | 12.2208 |
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+ | 0.3135 | 2.97 | 24000 | 0.4624 | 38.4609 | 14.1268 | 35.4304 | 35.391 | 12.1355 |
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  ### Framework versions
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