Commit
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Parent(s):
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doodle-dash-vit2
Browse files- .DS_Store +0 -0
- README.md +207 -9
- all_results.json +10 -10
- config.json +35 -16
- model.safetensors +2 -2
- preprocessor_config.json +11 -18
- test_results.json +6 -5
- train_results.json +5 -5
- trainer_state.json +0 -0
- training_args.bin +2 -2
.DS_Store
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Binary files a/.DS_Store and b/.DS_Store differ
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README.md
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@@ -1,8 +1,9 @@
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---
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-
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base_model: google/vit-base-patch16-224
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tags:
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- generated_from_trainer
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model-index:
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- name: results
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results: []
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@@ -13,14 +14,10 @@ should probably proofread and complete it, then remove this comment. -->
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# results
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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-
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- eval_runtime: 38.6479
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- eval_samples_per_second: 6523.813
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- eval_steps_per_second: 25.486
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- step: 0
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## Model description
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@@ -49,6 +46,207 @@ The following hyperparameters were used during training:
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- num_epochs: 5
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.40.0
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---
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+
base_model: laszlokiss27/doodle-dash2
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tags:
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- generated_from_trainer
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+
metrics:
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+
- accuracy
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model-index:
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- name: results
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results: []
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# results
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+
This model is a fine-tuned version of [laszlokiss27/doodle-dash2](https://huggingface.co/laszlokiss27/doodle-dash2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7177
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- Accuracy: 0.8121
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## Model description
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- num_epochs: 5
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+
|:-------------:|:------:|:------:|:---------------:|:--------:|
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| 0.9709 | 0.0256 | 5000 | 0.9170 | 0.7612 |
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| 0.9635 | 0.0513 | 10000 | 0.9147 | 0.7623 |
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| 0.9518 | 0.0769 | 15000 | 0.9081 | 0.7646 |
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| 0.9472 | 0.1026 | 20000 | 0.9044 | 0.7656 |
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| 0.9443 | 0.1282 | 25000 | 0.9061 | 0.7660 |
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| 0.93 | 0.1538 | 30000 | 0.9071 | 0.7651 |
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| 0.9206 | 0.1795 | 35000 | 0.8963 | 0.7680 |
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| 0.9214 | 0.2051 | 40000 | 0.8910 | 0.7693 |
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| 0.912 | 0.2308 | 45000 | 0.8914 | 0.7687 |
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| 0.9113 | 0.2564 | 50000 | 0.8801 | 0.7719 |
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| 0.9035 | 0.2820 | 55000 | 0.8803 | 0.7723 |
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| 0.9035 | 0.3077 | 60000 | 0.8798 | 0.7717 |
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| 0.8898 | 0.3333 | 65000 | 0.8822 | 0.7719 |
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| 0.8874 | 0.3590 | 70000 | 0.8703 | 0.7748 |
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| 0.8848 | 0.3846 | 75000 | 0.8623 | 0.7764 |
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| 0.8817 | 0.4102 | 80000 | 0.8609 | 0.7766 |
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| 0.8765 | 0.4359 | 85000 | 0.8599 | 0.7769 |
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| 0.8763 | 0.4615 | 90000 | 0.8532 | 0.7787 |
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| 0.8714 | 0.4872 | 95000 | 0.8572 | 0.7774 |
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| 0.869 | 0.5128 | 100000 | 0.8479 | 0.7796 |
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| 0.8672 | 0.5384 | 105000 | 0.8480 | 0.7798 |
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| 0.8632 | 0.5641 | 110000 | 0.8520 | 0.7792 |
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| 0.8592 | 0.5897 | 115000 | 0.8433 | 0.7811 |
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| 0.8607 | 0.6154 | 120000 | 0.8428 | 0.7811 |
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| 0.853 | 0.6410 | 125000 | 0.8375 | 0.7827 |
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| 0.8541 | 0.6666 | 130000 | 0.8455 | 0.7805 |
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| 0.8473 | 0.6923 | 135000 | 0.8330 | 0.7838 |
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| 0.8449 | 0.7179 | 140000 | 0.8305 | 0.7838 |
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| 0.8465 | 0.7436 | 145000 | 0.8274 | 0.7850 |
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| 0.8423 | 0.7692 | 150000 | 0.8325 | 0.7836 |
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| 0.8454 | 0.7948 | 155000 | 0.8270 | 0.7849 |
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| 0.8358 | 0.8205 | 160000 | 0.8328 | 0.7838 |
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| 0.8389 | 0.8461 | 165000 | 0.8209 | 0.7868 |
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| 0.8332 | 0.8718 | 170000 | 0.8340 | 0.7834 |
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| 0.8357 | 0.8974 | 175000 | 0.8200 | 0.7864 |
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| 0.8356 | 0.9230 | 180000 | 0.8162 | 0.7877 |
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| 0.835 | 0.9487 | 185000 | 0.8181 | 0.7874 |
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| 0.8298 | 0.9743 | 190000 | 0.8180 | 0.7874 |
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| 0.8285 | 1.0000 | 195000 | 0.8154 | 0.7878 |
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| 0.8138 | 1.0256 | 200000 | 0.8119 | 0.7889 |
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| 0.8104 | 1.0512 | 205000 | 0.8087 | 0.7887 |
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| 0.8162 | 1.0769 | 210000 | 0.8073 | 0.7895 |
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| 0.8122 | 1.1025 | 215000 | 0.8053 | 0.7902 |
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| 0.807 | 1.1282 | 220000 | 0.8064 | 0.7900 |
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| 0.8114 | 1.1538 | 225000 | 0.8043 | 0.7907 |
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| 0.8165 | 1.1794 | 230000 | 0.8042 | 0.7911 |
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| 0.8124 | 1.2051 | 235000 | 0.8009 | 0.7910 |
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| 0.8092 | 1.2307 | 240000 | 0.8019 | 0.7914 |
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| 0.8023 | 1.2564 | 245000 | 0.7979 | 0.7921 |
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| 0.8058 | 1.2820 | 250000 | 0.7988 | 0.7922 |
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| 0.8057 | 1.3076 | 255000 | 0.7976 | 0.7923 |
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| 0.8076 | 1.3333 | 260000 | 0.7976 | 0.7921 |
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| 0.805 | 1.3589 | 265000 | 0.7953 | 0.7930 |
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| 0.797 | 1.3846 | 270000 | 0.7990 | 0.7926 |
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| 0.7997 | 1.4102 | 275000 | 0.7929 | 0.7935 |
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| 0.8028 | 1.4358 | 280000 | 0.7933 | 0.7933 |
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| 0.7981 | 1.4615 | 285000 | 0.7905 | 0.7934 |
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| 0.8002 | 1.4871 | 290000 | 0.7965 | 0.7924 |
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| 0.7984 | 1.5128 | 295000 | 0.7915 | 0.7933 |
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| 0.7973 | 1.5384 | 300000 | 0.7950 | 0.7932 |
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| 0.7933 | 1.5640 | 305000 | 0.7865 | 0.7950 |
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| 0.7927 | 1.5897 | 310000 | 0.7886 | 0.7946 |
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| 0.799 | 1.6153 | 315000 | 0.7840 | 0.7954 |
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| 0.7961 | 1.6410 | 320000 | 0.8132 | 0.7901 |
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| 0.7866 | 1.6666 | 325000 | 0.7829 | 0.7958 |
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| 0.7898 | 1.6922 | 330000 | 0.7813 | 0.7959 |
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| 0.7885 | 1.7179 | 335000 | 0.7796 | 0.7969 |
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| 0.7901 | 1.7435 | 340000 | 0.7817 | 0.7958 |
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| 0.7916 | 1.7692 | 345000 | 0.7823 | 0.7962 |
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| 0.787 | 1.7948 | 350000 | 0.7789 | 0.7969 |
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| 0.7822 | 1.8204 | 355000 | 0.7787 | 0.7968 |
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| 0.7844 | 1.8461 | 360000 | 0.7754 | 0.7981 |
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| 0.7849 | 1.8717 | 365000 | 0.7775 | 0.7972 |
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| 0.7845 | 1.8974 | 370000 | 0.7761 | 0.7973 |
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| 0.7905 | 1.9230 | 375000 | 0.7736 | 0.7983 |
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| 0.788 | 1.9486 | 380000 | 0.7738 | 0.7978 |
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| 0.7832 | 1.9743 | 385000 | 0.7719 | 0.7980 |
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| 0.7787 | 1.9999 | 390000 | 0.7710 | 0.7986 |
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| 0.767 | 2.0256 | 395000 | 0.7717 | 0.7985 |
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| 0.7666 | 2.0512 | 400000 | 0.7698 | 0.7989 |
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| 0.7631 | 2.0768 | 405000 | 0.7719 | 0.7982 |
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| 0.7634 | 2.1025 | 410000 | 0.7684 | 0.7994 |
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| 0.7621 | 2.1281 | 415000 | 0.7707 | 0.7987 |
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| 0.7694 | 2.1538 | 420000 | 0.7700 | 0.7994 |
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| 0.7648 | 2.1794 | 425000 | 0.7678 | 0.7995 |
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| 0.7612 | 2.2050 | 430000 | 0.7673 | 0.7995 |
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| 0.7627 | 2.2307 | 435000 | 0.7671 | 0.7997 |
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| 0.766 | 2.2563 | 440000 | 0.7649 | 0.8003 |
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| 0.7635 | 2.2820 | 445000 | 0.7653 | 0.8000 |
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| 0.761 | 2.3076 | 450000 | 0.7647 | 0.8000 |
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| 0.7649 | 2.3332 | 455000 | 0.7661 | 0.8001 |
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| 0.7589 | 2.3589 | 460000 | 0.7630 | 0.8005 |
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| 0.7586 | 2.3845 | 465000 | 0.7703 | 0.7988 |
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| 0.7595 | 2.4102 | 470000 | 0.7640 | 0.8003 |
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| 0.7622 | 2.4358 | 475000 | 0.7627 | 0.8005 |
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| 0.7593 | 2.4614 | 480000 | 0.7605 | 0.8013 |
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| 0.7558 | 2.4871 | 485000 | 0.7609 | 0.8012 |
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| 0.7599 | 2.5127 | 490000 | 0.7651 | 0.8002 |
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| 0.7587 | 2.5384 | 495000 | 0.7589 | 0.8016 |
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| 0.7588 | 2.5640 | 500000 | 0.7570 | 0.8024 |
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| 0.762 | 2.5896 | 505000 | 0.7566 | 0.8020 |
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| 154 |
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| 0.7526 | 2.6153 | 510000 | 0.7602 | 0.8013 |
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| 0.7587 | 2.6409 | 515000 | 0.7560 | 0.8021 |
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| 156 |
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| 0.7522 | 2.6666 | 520000 | 0.7557 | 0.8026 |
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| 157 |
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| 0.7546 | 2.6922 | 525000 | 0.7542 | 0.8026 |
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| 158 |
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| 0.7542 | 2.7178 | 530000 | 0.7543 | 0.8029 |
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| 159 |
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| 0.7509 | 2.7435 | 535000 | 0.7542 | 0.8029 |
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| 0.7515 | 2.7691 | 540000 | 0.7585 | 0.8016 |
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| 161 |
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| 0.7508 | 2.7948 | 545000 | 0.7553 | 0.8024 |
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| 162 |
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| 0.7523 | 2.8204 | 550000 | 0.7531 | 0.8028 |
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| 163 |
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| 0.756 | 2.8460 | 555000 | 0.7511 | 0.8035 |
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| 164 |
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| 0.7559 | 2.8717 | 560000 | 0.7500 | 0.8038 |
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| 165 |
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| 0.75 | 2.8973 | 565000 | 0.7494 | 0.8038 |
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| 0.7492 | 2.9230 | 570000 | 0.7511 | 0.8035 |
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| 0.7481 | 2.9486 | 575000 | 0.7471 | 0.8044 |
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| 0.751 | 2.9742 | 580000 | 0.7478 | 0.8043 |
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| 0.7545 | 2.9999 | 585000 | 0.7595 | 0.8019 |
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| 0.7299 | 3.0255 | 590000 | 0.7478 | 0.8042 |
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| 171 |
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| 0.7305 | 3.0512 | 595000 | 0.7487 | 0.8047 |
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| 0.7343 | 3.0768 | 600000 | 0.7466 | 0.8047 |
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| 0.731 | 3.1024 | 605000 | 0.7472 | 0.8045 |
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| 0.733 | 3.1281 | 610000 | 0.7460 | 0.8046 |
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| 0.7351 | 3.1537 | 615000 | 0.7486 | 0.8043 |
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| 0.7372 | 3.1794 | 620000 | 0.7446 | 0.8052 |
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| 177 |
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| 0.7299 | 3.2050 | 625000 | 0.7478 | 0.8045 |
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| 0.7351 | 3.2306 | 630000 | 0.7458 | 0.8047 |
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| 0.7304 | 3.2563 | 635000 | 0.7460 | 0.8049 |
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| 0.7335 | 3.2819 | 640000 | 0.7451 | 0.8049 |
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| 0.7351 | 3.3076 | 645000 | 0.7416 | 0.8058 |
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| 182 |
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| 0.7324 | 3.3332 | 650000 | 0.7420 | 0.8058 |
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| 183 |
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| 0.732 | 3.3588 | 655000 | 0.7426 | 0.8057 |
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| 184 |
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| 0.7286 | 3.3845 | 660000 | 0.7418 | 0.8062 |
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| 185 |
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| 0.7331 | 3.4101 | 665000 | 0.7420 | 0.8059 |
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| 0.729 | 3.4358 | 670000 | 0.7402 | 0.8065 |
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| 187 |
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| 0.7336 | 3.4614 | 675000 | 0.7409 | 0.8063 |
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| 0.7275 | 3.4870 | 680000 | 0.7398 | 0.8064 |
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| 189 |
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| 0.7298 | 3.5127 | 685000 | 0.7388 | 0.8069 |
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| 0.724 | 3.5383 | 690000 | 0.7365 | 0.8070 |
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| 191 |
+
| 0.7266 | 3.5640 | 695000 | 0.7373 | 0.8072 |
|
| 192 |
+
| 0.7282 | 3.5896 | 700000 | 0.7371 | 0.8074 |
|
| 193 |
+
| 0.7272 | 3.6152 | 705000 | 0.7360 | 0.8073 |
|
| 194 |
+
| 0.7227 | 3.6409 | 710000 | 0.7360 | 0.8072 |
|
| 195 |
+
| 0.7275 | 3.6665 | 715000 | 0.7358 | 0.8073 |
|
| 196 |
+
| 0.7299 | 3.6922 | 720000 | 0.7422 | 0.8063 |
|
| 197 |
+
| 0.7363 | 3.7178 | 725000 | 0.7361 | 0.8072 |
|
| 198 |
+
| 0.7274 | 3.7434 | 730000 | 0.7334 | 0.8082 |
|
| 199 |
+
| 0.7282 | 3.7691 | 735000 | 0.7347 | 0.8081 |
|
| 200 |
+
| 0.7239 | 3.7947 | 740000 | 0.7326 | 0.8085 |
|
| 201 |
+
| 0.7225 | 3.8204 | 745000 | 0.7352 | 0.8076 |
|
| 202 |
+
| 0.7242 | 3.8460 | 750000 | 0.7320 | 0.8086 |
|
| 203 |
+
| 0.7291 | 3.8716 | 755000 | 0.7317 | 0.8089 |
|
| 204 |
+
| 0.7292 | 3.8973 | 760000 | 0.7310 | 0.8087 |
|
| 205 |
+
| 0.7247 | 3.9229 | 765000 | 0.7310 | 0.8083 |
|
| 206 |
+
| 0.7286 | 3.9486 | 770000 | 0.7326 | 0.8084 |
|
| 207 |
+
| 0.7237 | 3.9742 | 775000 | 0.7303 | 0.8088 |
|
| 208 |
+
| 0.7187 | 3.9998 | 780000 | 0.7298 | 0.8090 |
|
| 209 |
+
| 0.7077 | 4.0255 | 785000 | 0.7316 | 0.8084 |
|
| 210 |
+
| 0.7108 | 4.0511 | 790000 | 0.7316 | 0.8084 |
|
| 211 |
+
| 0.7025 | 4.0768 | 795000 | 0.7300 | 0.8093 |
|
| 212 |
+
| 0.708 | 4.1024 | 800000 | 0.7295 | 0.8093 |
|
| 213 |
+
| 0.7067 | 4.1280 | 805000 | 0.7288 | 0.8094 |
|
| 214 |
+
| 0.7123 | 4.1537 | 810000 | 0.7287 | 0.8094 |
|
| 215 |
+
| 0.707 | 4.1793 | 815000 | 0.7283 | 0.8095 |
|
| 216 |
+
| 0.7033 | 4.2050 | 820000 | 0.7282 | 0.8099 |
|
| 217 |
+
| 0.7128 | 4.2306 | 825000 | 0.7272 | 0.8099 |
|
| 218 |
+
| 0.7053 | 4.2562 | 830000 | 0.7284 | 0.8095 |
|
| 219 |
+
| 0.7097 | 4.2819 | 835000 | 0.7268 | 0.8098 |
|
| 220 |
+
| 0.7101 | 4.3075 | 840000 | 0.7267 | 0.8097 |
|
| 221 |
+
| 0.7074 | 4.3332 | 845000 | 0.7261 | 0.8102 |
|
| 222 |
+
| 0.7034 | 4.3588 | 850000 | 0.7257 | 0.8101 |
|
| 223 |
+
| 0.7059 | 4.3844 | 855000 | 0.7262 | 0.8098 |
|
| 224 |
+
| 0.7008 | 4.4101 | 860000 | 0.7247 | 0.8100 |
|
| 225 |
+
| 0.7021 | 4.4357 | 865000 | 0.7241 | 0.8103 |
|
| 226 |
+
| 0.707 | 4.4614 | 870000 | 0.7243 | 0.8105 |
|
| 227 |
+
| 0.7034 | 4.4870 | 875000 | 0.7238 | 0.8106 |
|
| 228 |
+
| 0.7055 | 4.5126 | 880000 | 0.7233 | 0.8106 |
|
| 229 |
+
| 0.7056 | 4.5383 | 885000 | 0.7231 | 0.8107 |
|
| 230 |
+
| 0.7029 | 4.5639 | 890000 | 0.7226 | 0.8108 |
|
| 231 |
+
| 0.7048 | 4.5896 | 895000 | 0.7224 | 0.8111 |
|
| 232 |
+
| 0.7031 | 4.6152 | 900000 | 0.7221 | 0.8110 |
|
| 233 |
+
| 0.7034 | 4.6408 | 905000 | 0.7216 | 0.8112 |
|
| 234 |
+
| 0.7012 | 4.6665 | 910000 | 0.7218 | 0.8113 |
|
| 235 |
+
| 0.702 | 4.6921 | 915000 | 0.7209 | 0.8114 |
|
| 236 |
+
| 0.7018 | 4.7178 | 920000 | 0.7207 | 0.8115 |
|
| 237 |
+
| 0.7056 | 4.7434 | 925000 | 0.7201 | 0.8116 |
|
| 238 |
+
| 0.7005 | 4.7690 | 930000 | 0.7199 | 0.8118 |
|
| 239 |
+
| 0.7005 | 4.7947 | 935000 | 0.7197 | 0.8117 |
|
| 240 |
+
| 0.708 | 4.8203 | 940000 | 0.7189 | 0.8117 |
|
| 241 |
+
| 0.6956 | 4.8460 | 945000 | 0.7190 | 0.8118 |
|
| 242 |
+
| 0.7074 | 4.8716 | 950000 | 0.7185 | 0.8120 |
|
| 243 |
+
| 0.6964 | 4.8972 | 955000 | 0.7184 | 0.8121 |
|
| 244 |
+
| 0.7048 | 4.9229 | 960000 | 0.7188 | 0.8120 |
|
| 245 |
+
| 0.7018 | 4.9485 | 965000 | 0.7178 | 0.8122 |
|
| 246 |
+
| 0.7006 | 4.9742 | 970000 | 0.7177 | 0.8121 |
|
| 247 |
+
| 0.7005 | 4.9998 | 975000 | 0.7177 | 0.8121 |
|
| 248 |
+
|
| 249 |
+
|
| 250 |
### Framework versions
|
| 251 |
|
| 252 |
- Transformers 4.40.0
|
all_results.json
CHANGED
|
@@ -1,13 +1,13 @@
|
|
| 1 |
{
|
| 2 |
"epoch": 5.0,
|
| 3 |
-
"eval_accuracy": 0.
|
| 4 |
-
"eval_loss":
|
| 5 |
-
"eval_runtime":
|
| 6 |
-
"eval_samples_per_second":
|
| 7 |
-
"eval_steps_per_second":
|
| 8 |
-
"total_flos":
|
| 9 |
-
"train_loss": 0.
|
| 10 |
-
"train_runtime":
|
| 11 |
-
"train_samples_per_second":
|
| 12 |
-
"train_steps_per_second":
|
| 13 |
}
|
|
|
|
| 1 |
{
|
| 2 |
"epoch": 5.0,
|
| 3 |
+
"eval_accuracy": 0.812062356349762,
|
| 4 |
+
"eval_loss": 0.7176774740219116,
|
| 5 |
+
"eval_runtime": 70.253,
|
| 6 |
+
"eval_samples_per_second": 7177.819,
|
| 7 |
+
"eval_steps_per_second": 28.042,
|
| 8 |
+
"total_flos": 2.144143543604609e+19,
|
| 9 |
+
"train_loss": 0.7742696757605321,
|
| 10 |
+
"train_runtime": 91371.0354,
|
| 11 |
+
"train_samples_per_second": 2731.829,
|
| 12 |
+
"train_steps_per_second": 10.671
|
| 13 |
}
|
config.json
CHANGED
|
@@ -1,13 +1,27 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "
|
| 3 |
"architectures": [
|
| 4 |
-
"
|
| 5 |
],
|
| 6 |
-
"
|
| 7 |
-
"
|
| 8 |
-
"
|
| 9 |
-
|
| 10 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 11 |
"id2label": {
|
| 12 |
"0": "aircraft carrier",
|
| 13 |
"1": "airplane",
|
|
@@ -357,7 +371,6 @@
|
|
| 357 |
},
|
| 358 |
"image_size": 64,
|
| 359 |
"initializer_range": 0.02,
|
| 360 |
-
"intermediate_size": 3072,
|
| 361 |
"label2id": {
|
| 362 |
"The Eiffel Tower": "309",
|
| 363 |
"The Great Wall of China": "310",
|
|
@@ -705,14 +718,20 @@
|
|
| 705 |
"zebra": "343",
|
| 706 |
"zigzag": "344"
|
| 707 |
},
|
| 708 |
-
"layer_norm_eps": 1e-
|
| 709 |
-
"
|
| 710 |
-
"
|
| 711 |
-
"
|
| 712 |
-
|
| 713 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 714 |
"problem_type": "single_label_classification",
|
| 715 |
-
"
|
| 716 |
"torch_dtype": "float32",
|
| 717 |
-
"transformers_version": "4.40.0"
|
|
|
|
| 718 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "laszlokiss27/doodle-dash2",
|
| 3 |
"architectures": [
|
| 4 |
+
"MobileViTV2ForImageClassification"
|
| 5 |
],
|
| 6 |
+
"aspp_dropout_prob": 0.1,
|
| 7 |
+
"aspp_out_channels": 512,
|
| 8 |
+
"atrous_rates": [
|
| 9 |
+
6,
|
| 10 |
+
12,
|
| 11 |
+
18
|
| 12 |
+
],
|
| 13 |
+
"attn_dropout": 0.0,
|
| 14 |
+
"base_attn_unit_dims": [
|
| 15 |
+
128,
|
| 16 |
+
192,
|
| 17 |
+
256
|
| 18 |
+
],
|
| 19 |
+
"classifier_dropout_prob": 0.1,
|
| 20 |
+
"conv_kernel_size": 3,
|
| 21 |
+
"expand_ratio": 2.0,
|
| 22 |
+
"ffn_dropout": 0.0,
|
| 23 |
+
"ffn_multiplier": 2,
|
| 24 |
+
"hidden_act": "swish",
|
| 25 |
"id2label": {
|
| 26 |
"0": "aircraft carrier",
|
| 27 |
"1": "airplane",
|
|
|
|
| 371 |
},
|
| 372 |
"image_size": 64,
|
| 373 |
"initializer_range": 0.02,
|
|
|
|
| 374 |
"label2id": {
|
| 375 |
"The Eiffel Tower": "309",
|
| 376 |
"The Great Wall of China": "310",
|
|
|
|
| 718 |
"zebra": "343",
|
| 719 |
"zigzag": "344"
|
| 720 |
},
|
| 721 |
+
"layer_norm_eps": 1e-05,
|
| 722 |
+
"mlp_ratio": 2.0,
|
| 723 |
+
"model_type": "mobilevitv2",
|
| 724 |
+
"n_attn_blocks": [
|
| 725 |
+
2,
|
| 726 |
+
4,
|
| 727 |
+
3
|
| 728 |
+
],
|
| 729 |
+
"num_channels": 1,
|
| 730 |
+
"output_stride": 32,
|
| 731 |
+
"patch_size": 2,
|
| 732 |
"problem_type": "single_label_classification",
|
| 733 |
+
"semantic_loss_ignore_index": 255,
|
| 734 |
"torch_dtype": "float32",
|
| 735 |
+
"transformers_version": "4.40.0",
|
| 736 |
+
"width_multiplier": 1.0
|
| 737 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e8cd6e374cfbf07883a1fcdf158c89f8afb511411ce78e49515298767c5f1eb1
|
| 3 |
+
size 18360744
|
preprocessor_config.json
CHANGED
|
@@ -1,39 +1,32 @@
|
|
| 1 |
{
|
| 2 |
"_valid_processor_keys": [
|
| 3 |
"images",
|
|
|
|
| 4 |
"do_resize",
|
| 5 |
"size",
|
| 6 |
"resample",
|
| 7 |
"do_rescale",
|
| 8 |
"rescale_factor",
|
| 9 |
-
"
|
| 10 |
-
"
|
| 11 |
-
"
|
| 12 |
"return_tensors",
|
| 13 |
"data_format",
|
| 14 |
"input_data_format"
|
| 15 |
],
|
| 16 |
-
"crop_size":
|
|
|
|
|
|
|
|
|
|
|
|
|
| 17 |
"do_convert_rgb": false,
|
| 18 |
"do_flip_channel_order": false,
|
| 19 |
-
"do_normalize": true,
|
| 20 |
"do_rescale": true,
|
| 21 |
"do_resize": true,
|
| 22 |
-
"
|
| 23 |
-
0.5,
|
| 24 |
-
0.5,
|
| 25 |
-
0.5
|
| 26 |
-
],
|
| 27 |
-
"image_processor_type": "ViTImageProcessor",
|
| 28 |
-
"image_std": [
|
| 29 |
-
0.5,
|
| 30 |
-
0.5,
|
| 31 |
-
0.5
|
| 32 |
-
],
|
| 33 |
"resample": 2,
|
| 34 |
"rescale_factor": 0.00392156862745098,
|
| 35 |
"size": {
|
| 36 |
-
"
|
| 37 |
-
"width": 64
|
| 38 |
}
|
| 39 |
}
|
|
|
|
| 1 |
{
|
| 2 |
"_valid_processor_keys": [
|
| 3 |
"images",
|
| 4 |
+
"segmentation_maps",
|
| 5 |
"do_resize",
|
| 6 |
"size",
|
| 7 |
"resample",
|
| 8 |
"do_rescale",
|
| 9 |
"rescale_factor",
|
| 10 |
+
"do_center_crop",
|
| 11 |
+
"crop_size",
|
| 12 |
+
"do_flip_channel_order",
|
| 13 |
"return_tensors",
|
| 14 |
"data_format",
|
| 15 |
"input_data_format"
|
| 16 |
],
|
| 17 |
+
"crop_size": {
|
| 18 |
+
"height": 56,
|
| 19 |
+
"width": 56
|
| 20 |
+
},
|
| 21 |
+
"do_center_crop": true,
|
| 22 |
"do_convert_rgb": false,
|
| 23 |
"do_flip_channel_order": false,
|
|
|
|
| 24 |
"do_rescale": true,
|
| 25 |
"do_resize": true,
|
| 26 |
+
"image_processor_type": "MobileViTImageProcessor",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 27 |
"resample": 2,
|
| 28 |
"rescale_factor": 0.00392156862745098,
|
| 29 |
"size": {
|
| 30 |
+
"shortest_edge": 56
|
|
|
|
| 31 |
}
|
| 32 |
}
|
test_results.json
CHANGED
|
@@ -1,7 +1,8 @@
|
|
| 1 |
{
|
| 2 |
-
"
|
| 3 |
-
"
|
| 4 |
-
"
|
| 5 |
-
"
|
| 6 |
-
"
|
|
|
|
| 7 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"epoch": 5.0,
|
| 3 |
+
"eval_accuracy": 0.812062356349762,
|
| 4 |
+
"eval_loss": 0.7176774740219116,
|
| 5 |
+
"eval_runtime": 70.253,
|
| 6 |
+
"eval_samples_per_second": 7177.819,
|
| 7 |
+
"eval_steps_per_second": 28.042
|
| 8 |
}
|
train_results.json
CHANGED
|
@@ -1,8 +1,8 @@
|
|
| 1 |
{
|
| 2 |
"epoch": 5.0,
|
| 3 |
-
"total_flos":
|
| 4 |
-
"train_loss": 0.
|
| 5 |
-
"train_runtime":
|
| 6 |
-
"train_samples_per_second":
|
| 7 |
-
"train_steps_per_second":
|
| 8 |
}
|
|
|
|
| 1 |
{
|
| 2 |
"epoch": 5.0,
|
| 3 |
+
"total_flos": 2.144143543604609e+19,
|
| 4 |
+
"train_loss": 0.7742696757605321,
|
| 5 |
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"train_runtime": 91371.0354,
|
| 6 |
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"train_samples_per_second": 2731.829,
|
| 7 |
+
"train_steps_per_second": 10.671
|
| 8 |
}
|
trainer_state.json
CHANGED
|
The diff for this file is too large to render.
See raw diff
|
|
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:
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size
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| 1 |
version https://git-lfs.github.com/spec/v1
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size 4984
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