đ segformer-b3-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass
This model is a fine - tuned version of nvidia/mit-b5 for image segmentation tasks, achieving high accuracy on the JCAI2000/100By100BranchPNG dataset.
đ Quick Start
This model is a fine-tuned version of nvidia/mit-b5 on the JCAI2000/100By100BranchPNG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1497
- Mean Iou: 0.8933
- Mean Accuracy: 0.9531
- Overall Accuracy: 0.9662
- Accuracy Background: 0.9732
- Accuracy Branch: 0.9330
- Iou Background: 0.9597
- Iou Branch: 0.8270
đ Documentation
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
đ§ Technical Details
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Mean Iou |
Mean Accuracy |
Overall Accuracy |
Accuracy Background |
Accuracy Branch |
Iou Background |
Iou Branch |
0.2055 |
1.05 |
20 |
0.2925 |
0.8151 |
0.9469 |
0.9320 |
0.9242 |
0.9695 |
0.9183 |
0.7118 |
0.1549 |
2.11 |
40 |
0.1328 |
0.8802 |
0.9311 |
0.9628 |
0.9796 |
0.8825 |
0.9561 |
0.8043 |
0.0735 |
3.16 |
60 |
0.1178 |
0.8804 |
0.9512 |
0.9613 |
0.9666 |
0.9357 |
0.9538 |
0.8070 |
0.0636 |
4.21 |
80 |
0.0844 |
0.8966 |
0.9368 |
0.9686 |
0.9854 |
0.8881 |
0.9629 |
0.8303 |
0.0546 |
5.26 |
100 |
0.1099 |
0.8969 |
0.9526 |
0.9676 |
0.9756 |
0.9297 |
0.9614 |
0.8325 |
0.0567 |
6.32 |
120 |
0.1012 |
0.8996 |
0.9500 |
0.9688 |
0.9788 |
0.9213 |
0.9629 |
0.8364 |
0.0515 |
7.37 |
140 |
0.1137 |
0.8935 |
0.9462 |
0.9668 |
0.9777 |
0.9147 |
0.9605 |
0.8265 |
0.052 |
8.42 |
160 |
0.0987 |
0.8914 |
0.9317 |
0.9670 |
0.9858 |
0.8776 |
0.9611 |
0.8217 |
0.0358 |
9.47 |
180 |
0.1167 |
0.8978 |
0.9581 |
0.9676 |
0.9726 |
0.9435 |
0.9613 |
0.8344 |
0.0254 |
10.53 |
200 |
0.0767 |
0.9111 |
0.9519 |
0.9729 |
0.9840 |
0.9197 |
0.9678 |
0.8545 |
0.0483 |
11.58 |
220 |
0.0953 |
0.9037 |
0.9524 |
0.9701 |
0.9795 |
0.9253 |
0.9645 |
0.8429 |
0.0285 |
12.63 |
240 |
0.0904 |
0.9026 |
0.9490 |
0.9700 |
0.9811 |
0.9169 |
0.9643 |
0.8409 |
0.0389 |
13.68 |
260 |
0.0902 |
0.9025 |
0.9472 |
0.9701 |
0.9821 |
0.9123 |
0.9644 |
0.8406 |
0.0473 |
14.74 |
280 |
0.0852 |
0.9084 |
0.9522 |
0.9719 |
0.9823 |
0.9220 |
0.9665 |
0.8502 |
0.0266 |
15.79 |
300 |
0.0983 |
0.8985 |
0.9409 |
0.9690 |
0.9839 |
0.8979 |
0.9633 |
0.8337 |
0.0233 |
16.84 |
320 |
0.0965 |
0.9052 |
0.9601 |
0.9702 |
0.9756 |
0.9447 |
0.9644 |
0.8460 |
0.0257 |
17.89 |
340 |
0.0941 |
0.9039 |
0.9550 |
0.9701 |
0.9781 |
0.9319 |
0.9643 |
0.8434 |
0.0352 |
18.95 |
360 |
0.0855 |
0.9043 |
0.9483 |
0.9706 |
0.9824 |
0.9142 |
0.9651 |
0.8435 |
0.1941 |
20.0 |
380 |
0.0946 |
0.9045 |
0.9509 |
0.9706 |
0.9809 |
0.9210 |
0.9650 |
0.8441 |
0.0325 |
21.05 |
400 |
0.0972 |
0.8973 |
0.9449 |
0.9683 |
0.9807 |
0.9092 |
0.9624 |
0.8323 |
0.0159 |
22.11 |
420 |
0.0828 |
0.9081 |
0.9528 |
0.9717 |
0.9817 |
0.9239 |
0.9664 |
0.8498 |
0.0175 |
23.16 |
440 |
0.1061 |
0.8995 |
0.9491 |
0.9688 |
0.9793 |
0.9188 |
0.9629 |
0.8360 |
0.0281 |
24.21 |
460 |
0.1090 |
0.8969 |
0.9516 |
0.9677 |
0.9761 |
0.9271 |
0.9615 |
0.8323 |
0.0177 |
25.26 |
480 |
0.1122 |
0.8983 |
0.9547 |
0.9680 |
0.9750 |
0.9343 |
0.9618 |
0.8347 |
0.0228 |
26.32 |
500 |
0.1088 |
0.8957 |
0.9546 |
0.9670 |
0.9736 |
0.9357 |
0.9606 |
0.8307 |
0.0348 |
27.37 |
520 |
0.0933 |
0.9059 |
0.9524 |
0.9710 |
0.9808 |
0.9241 |
0.9654 |
0.8464 |
0.0177 |
28.42 |
540 |
0.1053 |
0.9025 |
0.9527 |
0.9697 |
0.9787 |
0.9268 |
0.9639 |
0.8411 |
0.0182 |
29.47 |
560 |
0.1039 |
0.8992 |
0.9473 |
0.9688 |
0.9802 |
0.9143 |
0.9630 |
0.8355 |
0.0171 |
30.53 |
580 |
0.1117 |
0.8991 |
0.9555 |
0.9682 |
0.9750 |
0.9360 |
0.9621 |
0.8361 |
0.0275 |
31.58 |
600 |
0.1142 |
0.8935 |
0.9497 |
0.9665 |
0.9754 |
0.9241 |
0.9601 |
0.8268 |
0.0186 |
32.63 |
620 |
0.1065 |
0.9024 |
0.9524 |
0.9697 |
0.9788 |
0.9261 |
0.9639 |
0.8408 |
0.0173 |
33.68 |
640 |
0.1081 |
0.8986 |
0.9529 |
0.9682 |
0.9764 |
0.9294 |
0.9621 |
0.8351 |
0.015 |
34.74 |
660 |
0.1243 |
0.8935 |
0.9530 |
0.9663 |
0.9733 |
0.9327 |
0.9598 |
0.8272 |
0.0183 |
35.79 |
680 |
0.1120 |
0.9005 |
0.9500 |
0.9691 |
0.9792 |
0.9209 |
0.9633 |
0.8377 |
0.0248 |
36.84 |
700 |
0.1185 |
0.8962 |
0.9517 |
0.9674 |
0.9757 |
0.9277 |
0.9611 |
0.8312 |
0.0104 |
37.89 |
720 |
0.1136 |
0.8975 |
0.9506 |
0.9680 |
0.9771 |
0.9241 |
0.9619 |
0.8332 |
0.0481 |
38.95 |
740 |
0.1127 |
0.9010 |
0.9528 |
0.9691 |
0.9778 |
0.9277 |
0.9632 |
0.8388 |
0.0153 |
40.0 |
760 |
0.1101 |
0.9019 |
0.9537 |
0.9694 |
0.9777 |
0.9297 |
0.9635 |
0.8402 |
0.0143 |
41.05 |
780 |
0.1105 |
0.9032 |
0.9558 |
0.9698 |
0.9771 |
0.9345 |
0.9639 |
0.8425 |
0.0104 |
42.11 |
800 |
0.1122 |
0.8986 |
0.9428 |
0.9689 |
0.9827 |
0.9028 |
0.9631 |
0.8340 |
0.0172 |
43.16 |
820 |
0.1097 |
0.9041 |
0.9540 |
0.9702 |
0.9788 |
0.9291 |
0.9645 |
0.8437 |
0.0371 |
44.21 |
840 |
0.1064 |
0.9011 |
0.9503 |
0.9693 |
0.9794 |
0.9212 |
0.9635 |
0.8387 |
0.0221 |
45.26 |
860 |
0.1150 |
0.9004 |
0.9515 |
0.9690 |
0.9783 |
0.9247 |
0.9631 |
0.8377 |
0.0186 |
46.32 |
880 |
0.1228 |
0.8958 |
0.9518 |
0.9672 |
0.9754 |
0.9282 |
0.9610 |
0.8306 |
0.0119 |
47.37 |
900 |
0.1205 |
0.8980 |
0.9525 |
0.9680 |
0.9762 |
0.9288 |
0.9619 |
0.8340 |
0.0113 |
48.42 |
920 |
0.1133 |
0.8998 |
0.9502 |
0.9688 |
0.9787 |
0.9216 |
0.9629 |
0.8366 |
0.0121 |
49.47 |
940 |
0.1145 |
0.8993 |
0.9490 |
0.9688 |
0.9792 |
0.9188 |
0.9629 |
0.8358 |
0.0263 |
50.53 |
960 |
0.1168 |
0.8977 |
0.9542 |
0.9678 |
0.9750 |
0.9334 |
0.9616 |
0.8338 |
0.0093 |
51.58 |
980 |
0.1213 |
0.8940 |
0.9534 |
0.9664 |
0.9733 |
0.9334 |
0.9600 |
0.8280 |
0.0193 |
52.63 |
1000 |
0.1241 |
0.8971 |
0.9507 |
0.9678 |
0.9769 |
0.9246 |
0.9617 |
0.8326 |
0.0139 |
53.68 |
1020 |
0.1263 |
0.8962 |
0.9546 |
0.9672 |
0.9739 |
0.9353 |
0.9609 |
0.8316 |
0.012 |
54.74 |
1040 |
0.1252 |
0.8952 |
0.9504 |
0.9671 |
0.9760 |
0.9247 |
0.9609 |
0.8296 |
0.008 |
55.79 |
1060 |
0.1219 |
0.8986 |
0.9516 |
0.9683 |
0.9772 |
0.9260 |
0.9623 |
0.8349 |
0.0092 |
56.84 |
1080 |
0.1290 |
0.8995 |
0.9552 |
0.9684 |
0.9754 |
0.9349 |
0.9623 |
0.8366 |
0.015 |
57.89 |
1100 |
0.1243 |
0.8989 |
0.9545 |
0.9682 |
0.9755 |
0.9335 |
0.9621 |
0.8358 |
0.0126 |
58.95 |
1120 |
0.1214 |
0.8977 |
0.9541 |
0.9678 |
0.9751 |
0.9331 |
0.9616 |
0.8337 |
0.0212 |
60.0 |
1140 |
0.1298 |
0.8953 |
0.9542 |
0.9669 |
0.9736 |
0.9347 |
0.9605 |
0.8301 |
0.0192 |
61.05 |
1160 |
0.1341 |
0.8930 |
0.9518 |
0.9661 |
0.9737 |
0.9299 |
0.9597 |
0.8262 |
0.0136 |
62.11 |
1180 |
0.1327 |
0.8970 |
0.9528 |
0.9676 |
0.9754 |
0.9302 |
0.9614 |
0.8325 |
0.0131 |
63.16 |
1200 |
0.1233 |
0.8997 |
0.9549 |
0.9685 |
0.9757 |
0.9340 |
0.9624 |
0.8369 |
0.0135 |
64.21 |
1220 |
0.1301 |
0.8957 |
0.9542 |
0.9670 |
0.9738 |
0.9345 |
0.9607 |
0.8307 |
0.0228 |
65.26 |
1240 |
0.1274 |
0.8979 |
0.9524 |
0.9680 |
0.9762 |
0.9285 |
0.9618 |
0.8339 |
0.0138 |
66.32 |
1260 |
0.1336 |
0.8965 |
0.9520 |
0.9675 |
0.9757 |
0.9283 |
0.9613 |
0.8318 |
0.0127 |
67.37 |
1280 |
0.1278 |
0.8980 |
0.9519 |
0.9681 |
0.9767 |
0.9271 |
0.9620 |
0.8341 |
0.0107 |
68.42 |
1300 |
0.1293 |
0.8970 |
0.9530 |
0.9676 |
0.9753 |
0.9308 |
0.9614 |
0.8327 |
0.0278 |
69.47 |
1320 |
0.1413 |
0.8926 |
0.9534 |
0.9659 |
0.9725 |
0.9343 |
0.9593 |
0.8258 |
0.0159 |
70.53 |
1340 |
0.1360 |
0.8953 |
0.9522 |
0.9670 |
0.9748 |
0.9296 |
0.9607 |
0.8298 |
0.0105 |
71.58 |
1360 |
0.1319 |
0.8972 |
0.9537 |
0.9676 |
0.9750 |
0.9324 |
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đ License
The license for this model is other.
Property |
Details |
Model Type |
Segformer B3 fine - tuned on 100by100PNG dataset |
Training Data |
JCAI2000/100By100BranchPNG dataset |