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Switch to biber and add classifier eval

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zenon
2023-03-08 17:05:27 +01:00
parent 0e37585e57
commit 088d6647c1
12 changed files with 1395 additions and 420 deletions
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classification/classifier/train-metrics.csv
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epoch,train_loss,train_acc,val_loss,val_acc
0,0.6290,0.6920,0.4647,0.7843
1,0.7473,0.6970,0.5752,0.7941
2,0.5318,0.7531,0.4026,0.8137
3,0.5383,0.7693,0.4723,0.7745
4,0.8356,0.7282,0.9150,0.6569
5,0.5942,0.7693,0.4992,0.7745
6,0.4339,0.8092,0.4625,0.7451
7,0.3620,0.8454,0.3219,0.8431
8,0.3350,0.8579,0.2812,0.8725
9,0.3237,0.8653,0.3355,0.8333
10,0.3264,0.8516,0.2956,0.8627
11,0.3434,0.8541,0.3215,0.8235
12,0.3129,0.8641,0.2990,0.8333
13,0.3072,0.8616,0.2975,0.8725
14,0.2892,0.8815,0.2844,0.9020
15,0.3042,0.8616,0.3124,0.8235
16,0.3007,0.8703,0.3064,0.8431
17,0.2580,0.8928,0.3006,0.8431
18,0.2844,0.8778,0.3441,0.8137
19,0.2994,0.8815,0.2690,0.8824
20,0.3063,0.8653,0.3406,0.8137
21,0.2839,0.8766,0.2964,0.8431
22,0.2871,0.8815,0.3140,0.8333
23,0.2874,0.8766,0.3014,0.8725
24,0.2694,0.8890,0.3236,0.8333
25,0.3031,0.8778,0.3001,0.8431
26,0.2996,0.8641,0.3217,0.8235
27,0.3109,0.8766,0.2569,0.9118
28,0.2949,0.8703,0.3114,0.8333
29,0.3067,0.8728,0.3476,0.8235
30,0.3084,0.8828,0.2851,0.8627
31,0.3158,0.8653,0.2967,0.8431
32,0.3153,0.8603,0.2872,0.8529
33,0.3017,0.8716,0.2775,0.8431
34,0.3171,0.8791,0.3527,0.8235
35,0.3027,0.8666,0.2903,0.8333
36,0.2997,0.8840,0.2729,0.8627
37,0.2867,0.8840,0.2564,0.8922
38,0.3044,0.8628,0.3305,0.8333
39,0.2978,0.8840,0.3722,0.8235
40,0.2906,0.8678,0.2804,0.8627
41,0.2822,0.8778,0.2952,0.8725
42,0.3403,0.8466,0.3014,0.8529
43,0.3040,0.8803,0.3133,0.8333
44,0.3107,0.8815,0.2954,0.8431
45,0.2785,0.8828,0.3147,0.8431
46,0.3109,0.8778,0.2670,0.8725
47,0.2848,0.8766,0.3000,0.8529
48,0.2832,0.8791,0.2768,0.8627
49,0.2606,0.8890,0.2756,0.8824
1 epoch train_loss train_acc val_loss val_acc
2 0 0.6290 0.6920 0.4647 0.7843
3 1 0.7473 0.6970 0.5752 0.7941
4 2 0.5318 0.7531 0.4026 0.8137
5 3 0.5383 0.7693 0.4723 0.7745
6 4 0.8356 0.7282 0.9150 0.6569
7 5 0.5942 0.7693 0.4992 0.7745
8 6 0.4339 0.8092 0.4625 0.7451
9 7 0.3620 0.8454 0.3219 0.8431
10 8 0.3350 0.8579 0.2812 0.8725
11 9 0.3237 0.8653 0.3355 0.8333
12 10 0.3264 0.8516 0.2956 0.8627
13 11 0.3434 0.8541 0.3215 0.8235
14 12 0.3129 0.8641 0.2990 0.8333
15 13 0.3072 0.8616 0.2975 0.8725
16 14 0.2892 0.8815 0.2844 0.9020
17 15 0.3042 0.8616 0.3124 0.8235
18 16 0.3007 0.8703 0.3064 0.8431
19 17 0.2580 0.8928 0.3006 0.8431
20 18 0.2844 0.8778 0.3441 0.8137
21 19 0.2994 0.8815 0.2690 0.8824
22 20 0.3063 0.8653 0.3406 0.8137
23 21 0.2839 0.8766 0.2964 0.8431
24 22 0.2871 0.8815 0.3140 0.8333
25 23 0.2874 0.8766 0.3014 0.8725
26 24 0.2694 0.8890 0.3236 0.8333
27 25 0.3031 0.8778 0.3001 0.8431
28 26 0.2996 0.8641 0.3217 0.8235
29 27 0.3109 0.8766 0.2569 0.9118
30 28 0.2949 0.8703 0.3114 0.8333
31 29 0.3067 0.8728 0.3476 0.8235
32 30 0.3084 0.8828 0.2851 0.8627
33 31 0.3158 0.8653 0.2967 0.8431
34 32 0.3153 0.8603 0.2872 0.8529
35 33 0.3017 0.8716 0.2775 0.8431
36 34 0.3171 0.8791 0.3527 0.8235
37 35 0.3027 0.8666 0.2903 0.8333
38 36 0.2997 0.8840 0.2729 0.8627
39 37 0.2867 0.8840 0.2564 0.8922
40 38 0.3044 0.8628 0.3305 0.8333
41 39 0.2978 0.8840 0.3722 0.8235
42 40 0.2906 0.8678 0.2804 0.8627
43 41 0.2822 0.8778 0.2952 0.8725
44 42 0.3403 0.8466 0.3014 0.8529
45 43 0.3040 0.8803 0.3133 0.8333
46 44 0.3107 0.8815 0.2954 0.8431
47 45 0.2785 0.8828 0.3147 0.8431
48 46 0.3109 0.8778 0.2670 0.8725
49 47 0.2848 0.8766 0.3000 0.8529
50 48 0.2832 0.8791 0.2768 0.8627
51 49 0.2606 0.8890 0.2756 0.8824
classification/classifier/train.ipynb
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classification/evaluation/eval-test-model.ipynb
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classification/evaluation/eval-train-yolo.ipynb
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],
"source": [
"fig, ax = plt.subplots(1, 1, figsize=set_size(width, subplots=(1,1)))\n",
"sns.lineplot(x='epoch', y='value', style='metric', dashes=[\"\", (2,1)], data=df_aranged, color='black', linewidth=1)\n",
"sns.lineplot(x='epoch', y='value', style='metric', dashes=[\"\", (2,1)], data=df_aranged,\n",
" color='black', linewidth=1)\n",
"ax.set_ylim([0, 1])\n",
"ax.set_xticks(np.arange(0, 350, 50))\n",
"ax.set_ylabel('')\n",