Hugging Face's logo

heromiya
/
my-fine-tuned-model

Image Segmentation
Transformers
TensorBoard
Safetensors
segformer
vision
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community

my-fine-tuned-model

This model is a fine-tuned version of nvidia/segformer-b1-finetuned-ade-512-512 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8640
  • Mean Iou: 0.1813
  • Mean Accuracy: 0.2233
  • Overall Accuracy: 0.7956
  • Accuracy Unlabeled: nan
  • Accuracy Flat-road: 0.9372
  • Accuracy Flat-sidewalk: 0.9555
  • Accuracy Flat-crosswalk: 0.0
  • Accuracy Flat-cyclinglane: 0.6635
  • Accuracy Flat-parkingdriveway: 0.0799
  • Accuracy Flat-railtrack: nan
  • Accuracy Flat-curb: 0.0001
  • Accuracy Human-person: 0.0
  • Accuracy Human-rider: 0.0
  • Accuracy Vehicle-car: 0.8975
  • Accuracy Vehicle-truck: 0.0
  • Accuracy Vehicle-bus: 0.0
  • Accuracy Vehicle-tramtrain: 0.0
  • Accuracy Vehicle-motorcycle: 0.0
  • Accuracy Vehicle-bicycle: 0.0
  • Accuracy Vehicle-caravan: 0.0
  • Accuracy Vehicle-cartrailer: 0.0
  • Accuracy Construction-building: 0.9214
  • Accuracy Construction-door: 0.0
  • Accuracy Construction-wall: 0.0000
  • Accuracy Construction-fenceguardrail: 0.0
  • Accuracy Construction-bridge: 0.0
  • Accuracy Construction-tunnel: nan
  • Accuracy Construction-stairs: 0.0
  • Accuracy Object-pole: 0.0
  • Accuracy Object-trafficsign: 0.0
  • Accuracy Object-trafficlight: 0.0
  • Accuracy Nature-vegetation: 0.9128
  • Accuracy Nature-terrain: 0.8493
  • Accuracy Sky: 0.9299
  • Accuracy Void-ground: 0.0
  • Accuracy Void-dynamic: 0.0
  • Accuracy Void-static: 0.0
  • Accuracy Void-unclear: 0.0
  • Iou Unlabeled: nan
  • Iou Flat-road: 0.6819
  • Iou Flat-sidewalk: 0.8378
  • Iou Flat-crosswalk: 0.0
  • Iou Flat-cyclinglane: 0.5970
  • Iou Flat-parkingdriveway: 0.0754
  • Iou Flat-railtrack: nan
  • Iou Flat-curb: 0.0001
  • Iou Human-person: 0.0
  • Iou Human-rider: 0.0
  • Iou Vehicle-car: 0.7287
  • Iou Vehicle-truck: 0.0
  • Iou Vehicle-bus: 0.0
  • Iou Vehicle-tramtrain: 0.0
  • Iou Vehicle-motorcycle: 0.0
  • Iou Vehicle-bicycle: 0.0
  • Iou Vehicle-caravan: 0.0
  • Iou Vehicle-cartrailer: 0.0
  • Iou Construction-building: 0.5594
  • Iou Construction-door: 0.0
  • Iou Construction-wall: 0.0000
  • Iou Construction-fenceguardrail: 0.0
  • Iou Construction-bridge: 0.0
  • Iou Construction-tunnel: nan
  • Iou Construction-stairs: 0.0
  • Iou Object-pole: 0.0
  • Iou Object-trafficsign: 0.0
  • Iou Object-trafficlight: 0.0
  • Iou Nature-vegetation: 0.7763
  • Iou Nature-terrain: 0.6951
  • Iou Sky: 0.8496
  • Iou Void-ground: 0.0
  • Iou Void-dynamic: 0.0
  • Iou Void-static: 0.0
  • Iou Void-unclear: 0.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Flat-road Accuracy Flat-sidewalk Accuracy Flat-crosswalk Accuracy Flat-cyclinglane Accuracy Flat-parkingdriveway Accuracy Flat-railtrack Accuracy Flat-curb Accuracy Human-person Accuracy Human-rider Accuracy Vehicle-car Accuracy Vehicle-truck Accuracy Vehicle-bus Accuracy Vehicle-tramtrain Accuracy Vehicle-motorcycle Accuracy Vehicle-bicycle Accuracy Vehicle-caravan Accuracy Vehicle-cartrailer Accuracy Construction-building Accuracy Construction-door Accuracy Construction-wall Accuracy Construction-fenceguardrail Accuracy Construction-bridge Accuracy Construction-tunnel Accuracy Construction-stairs Accuracy Object-pole Accuracy Object-trafficsign Accuracy Object-trafficlight Accuracy Nature-vegetation Accuracy Nature-terrain Accuracy Sky Accuracy Void-ground Accuracy Void-dynamic Accuracy Void-static Accuracy Void-unclear Iou Unlabeled Iou Flat-road Iou Flat-sidewalk Iou Flat-crosswalk Iou Flat-cyclinglane Iou Flat-parkingdriveway Iou Flat-railtrack Iou Flat-curb Iou Human-person Iou Human-rider Iou Vehicle-car Iou Vehicle-truck Iou Vehicle-bus Iou Vehicle-tramtrain Iou Vehicle-motorcycle Iou Vehicle-bicycle Iou Vehicle-caravan Iou Vehicle-cartrailer Iou Construction-building Iou Construction-door Iou Construction-wall Iou Construction-fenceguardrail Iou Construction-bridge Iou Construction-tunnel Iou Construction-stairs Iou Object-pole Iou Object-trafficsign Iou Object-trafficlight Iou Nature-vegetation Iou Nature-terrain Iou Sky Iou Void-ground Iou Void-dynamic Iou Void-static Iou Void-unclear
2.9097 0.05 20 2.4129 0.0976 0.1470 0.6553 nan 0.6590 0.9442 0.0 0.0820 0.0057 nan 0.0033 0.0000 0.0 0.6208 0.0 0.0 0.0 0.0 0.0176 0.0 0.0 0.5921 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9758 0.0306 0.7320 0.0002 0.0 0.0402 0.0 0.0 0.4482 0.7368 0.0 0.0774 0.0057 0.0 0.0030 0.0000 0.0 0.4563 0.0 0.0 0.0 0.0 0.0140 0.0 0.0 0.4050 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5114 0.0299 0.6961 0.0002 0.0 0.0302 0.0
2.2468 0.1 40 2.2262 0.1037 0.1504 0.6653 nan 0.8040 0.8979 0.0 0.0072 0.0016 nan 0.0004 0.0 0.0 0.4835 0.0 0.0 0.0 0.0 0.0006 0.0 0.0 0.8209 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9612 0.0184 0.8068 0.0 0.0 0.0106 0.0 nan 0.4453 0.7623 0.0 0.0070 0.0016 0.0 0.0004 0.0 0.0 0.4186 0.0 0.0 0.0 0.0 0.0006 0.0 0.0 0.4512 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5601 0.0182 0.7458 0.0 0.0 0.0104 0.0
1.925 0.15 60 2.0682 0.1164 0.1590 0.6842 nan 0.8002 0.9212 0.0 0.0815 0.0005 nan 0.0 0.0 0.0 0.4581 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8328 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9535 0.1864 0.8544 0.0 0.0 0.0000 0.0 nan 0.4820 0.7689 0.0 0.0802 0.0005 nan 0.0 0.0 0.0 0.4091 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4382 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5993 0.1795 0.7682 0.0 0.0 0.0000 0.0
2.3829 0.2 80 1.8790 0.1358 0.1780 0.7131 nan 0.8259 0.9218 0.0 0.2819 0.0002 nan 0.0 0.0 0.0 0.5001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8609 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9421 0.4796 0.8829 0.0 0.0 0.0000 0.0 nan 0.5192 0.7818 0.0 0.2719 0.0002 nan 0.0 0.0 0.0 0.4260 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4509 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6537 0.4571 0.7836 0.0 0.0 0.0000 0.0
1.6895 0.25 100 1.7516 0.1275 0.1719 0.7009 nan 0.8793 0.8949 0.0 0.2101 0.0001 nan 0.0 0.0 0.0 0.5551 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8658 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9496 0.2505 0.8940 0.0 0.0 0.0 0.0 nan 0.4903 0.7883 0.0 0.2059 0.0001 nan 0.0 0.0 0.0 0.4758 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4736 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6125 0.2442 0.7891 0.0 0.0 0.0 0.0
2.0938 0.3 120 1.6307 0.1428 0.1812 0.7269 nan 0.7659 0.9636 0.0 0.2570 0.0002 nan 0.0 0.0 0.0 0.6130 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8365 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9509 0.5413 0.8706 0.0 0.0 0.0001 0.0 nan 0.5601 0.7606 0.0 0.2490 0.0002 nan 0.0 0.0 0.0 0.5441 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4929 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6511 0.5149 0.7966 0.0 0.0 0.0001 0.0
1.6363 0.35 140 1.4988 0.1522 0.1931 0.7447 nan 0.8234 0.9534 0.0 0.4649 0.0000 nan 0.0 0.0 0.0 0.6198 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8628 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9422 0.6012 0.9106 0.0 0.0 0.0 0.0 nan 0.6114 0.7902 0.0 0.4050 0.0000 nan 0.0 0.0 0.0 0.5380 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4899 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6747 0.5672 0.7937 0.0 0.0 0.0 0.0
1.7375 0.4 160 1.4685 0.1521 0.1922 0.7444 nan 0.8202 0.9583 0.0 0.4191 0.0000 nan 0.0 0.0 0.0 0.5643 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9003 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9231 0.6577 0.9072 0.0 0.0 0.0 0.0 nan 0.6212 0.7871 0.0 0.3807 0.0000 nan 0.0 0.0 0.0 0.4978 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4735 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7028 0.6064 0.7976 0.0 0.0 0.0 0.0
1.5994 0.45 180 1.3911 0.1559 0.1979 0.7545 nan 0.8985 0.9464 0.0 0.3306 0.0002 nan 0.0 0.0 0.0 0.7378 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8685 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9345 0.7326 0.8831 0.0 0.0 0.0 0.0 nan 0.5703 0.8161 0.0 0.3218 0.0002 nan 0.0 0.0 0.0 0.5994 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5232 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7016 0.6663 0.7904 0.0 0.0 0.0 0.0
2.0048 0.5 200 1.3081 0.1578 0.1978 0.7541 nan 0.7970 0.9670 0.0 0.4200 0.0003 nan 0.0 0.0 0.0 0.7116 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8340 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9554 0.7548 0.8900 0.0 0.0 0.0 0.0 nan 0.6425 0.7818 0.0 0.3644 0.0003 nan 0.0 0.0 0.0 0.6015 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5267 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6703 0.6692 0.7928 0.0 0.0 0.0 0.0
1.4191 0.55 220 1.2710 0.1621 0.2014 0.7633 nan 0.8450 0.9635 0.0 0.3623 0.0013 nan 0.0 0.0 0.0 0.7991 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9186 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9269 0.7738 0.8549 0.0 0.0 0.0 0.0 nan 0.6466 0.7850 0.0 0.3533 0.0013 nan 0.0 0.0 0.0 0.6419 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5394 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7239 0.6741 0.8204 0.0 0.0 0.0 0.0
1.2046 0.6 240 1.2596 0.1623 0.2054 0.7580 nan 0.9143 0.9102 0.0 0.6029 0.0011 nan 0.0 0.0 0.0 0.7193 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9179 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9449 0.6649 0.8957 0.0 0.0 0.0 0.0 nan 0.5819 0.8199 0.0 0.5105 0.0011 nan 0.0 0.0 0.0 0.6122 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5208 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6951 0.6131 0.8401 0.0 0.0 0.0 0.0
2.3716 0.65 260 1.2121 0.1652 0.2072 0.7702 nan 0.9240 0.9416 0.0 0.5131 0.0005 nan 0.0 0.0 0.0 0.8518 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8971 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9395 0.6577 0.9059 0.0 0.0 0.0 0.0 nan 0.6001 0.8232 0.0 0.4777 0.0005 nan 0.0 0.0 0.0 0.6470 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5700 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7144 0.6094 0.8436 0.0 0.0 0.0 0.0
1.1683 0.7 280 1.1438 0.1706 0.2117 0.7786 nan 0.8707 0.9609 0.0 0.6311 0.0007 nan 0.0 0.0 0.0 0.8191 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9206 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9257 0.7316 0.9131 0.0 0.0 0.0 0.0 nan 0.6810 0.8121 0.0 0.5472 0.0007 nan 0.0 0.0 0.0 0.6511 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5441 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7346 0.6461 0.8426 0.0 0.0 0.0 0.0
1.2216 0.75 300 1.1543 0.1685 0.2111 0.7765 nan 0.9293 0.9435 0.0 0.5694 0.0006 nan 0.0 0.0 0.0 0.8627 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8790 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9470 0.7174 0.9055 0.0 0.0 0.0 0.0 nan 0.6241 0.8300 0.0 0.5381 0.0006 nan 0.0 0.0 0.0 0.6406 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5680 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7144 0.6421 0.8326 0.0 0.0 0.0 0.0
1.0353 0.8 320 1.1280 0.1609 0.2030 0.7583 nan 0.9576 0.9160 0.0 0.4185 0.0001 nan 0.0 0.0 0.0 0.7980 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9393 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9169 0.6555 0.8954 0.0 0.0 0.0 0.0 nan 0.5528 0.8267 0.0 0.4079 0.0001 nan 0.0 0.0 0.0 0.6672 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5294 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7287 0.5931 0.8417 0.0 0.0 0.0 0.0
1.2914 0.85 340 1.0709 0.1669 0.2087 0.7767 nan 0.8841 0.9690 0.0 0.4128 0.0002 nan 0.0 0.0 0.0 0.8989 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8778 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9331 0.7791 0.9226 0.0 0.0 0.0 0.0 nan 0.6624 0.8036 0.0 0.4016 0.0002 nan 0.0 0.0 0.0 0.6614 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5705 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7446 0.6539 0.8414 0.0 0.0 0.0 0.0
1.04 0.9 360 1.0501 0.1711 0.2155 0.7789 nan 0.9411 0.9289 0.0 0.5881 0.0009 nan 0.0 0.0 0.0 0.8622 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9081 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9310 0.8356 0.9012 0.0 0.0 0.0 0.0 nan 0.6170 0.8329 0.0 0.5329 0.0009 nan 0.0 0.0 0.0 0.6817 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5572 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7424 0.6656 0.8460 0.0 0.0 0.0 0.0
1.3936 0.95 380 1.0156 0.1704 0.2137 0.7769 nan 0.9314 0.9340 0.0 0.5883 0.0041 nan 0.0 0.0 0.0 0.8612 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8653 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9497 0.7963 0.9091 0.0 0.0 0.0 0.0 nan 0.6222 0.8216 0.0 0.5566 0.0041 nan 0.0 0.0 0.0 0.6814 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5725 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7117 0.6494 0.8339 0.0 0.0 0.0 0.0
1.0755 1.0 400 1.0207 0.1699 0.2136 0.7785 nan 0.9235 0.9450 0.0 0.5502 0.0009 nan 0.0 0.0 0.0 0.9010 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8852 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9251 0.7857 0.9188 0.0 0.0 0.0 0.0 nan 0.6156 0.8246 0.0 0.5239 0.0009 nan 0.0 0.0 0.0 0.6712 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5672 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7448 0.6645 0.8251 0.0 0.0 0.0 0.0
0.9754 1.05 420 0.9999 0.1744 0.2183 0.7868 nan 0.9275 0.9491 0.0 0.6556 0.0032 nan 0.0 0.0 0.0 0.8851 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8986 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9194 0.8269 0.9203 0.0 0.0 0.0 0.0 nan 0.6496 0.8305 0.0 0.5666 0.0032 nan 0.0 0.0 0.0 0.6863 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5681 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7582 0.6836 0.8350 0.0 0.0 0.0 0.0
1.2134 1.1 440 0.9553 0.1736 0.2182 0.7841 nan 0.9338 0.9361 0.0 0.6495 0.0132 nan 0.0 0.0 0.0 0.8674 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9159 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9247 0.8396 0.9013 0.0 0.0 0.0 0.0 nan 0.6456 0.8346 0.0 0.5679 0.0131 nan 0.0 0.0 0.0 0.6805 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5559 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7527 0.6596 0.8452 0.0 0.0 0.0 0.0
1.0486 1.15 460 0.9654 0.1713 0.2152 0.7795 nan 0.9483 0.9307 0.0 0.5519 0.0128 nan 0.0 0.0 0.0 0.8840 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8885 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9438 0.8156 0.9099 0.0 0.0 0.0 0.0 nan 0.6150 0.8302 0.0 0.5254 0.0127 nan 0.0 0.0 0.0 0.6839 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5836 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7245 0.6576 0.8476 0.0 0.0 0.0 0.0
1.4786 1.2 480 0.9317 0.1756 0.2183 0.7891 nan 0.9250 0.9547 0.0 0.6636 0.0136 nan 0.0 0.0 0.0 0.8882 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9153 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9228 0.7705 0.9308 0.0 0.0 0.0 0.0 nan 0.6901 0.8278 0.0 0.5496 0.0135 nan 0.0 0.0 0.0 0.7016 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5615 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7577 0.6675 0.8495 0.0 0.0 0.0 0.0
0.861 1.25 500 0.9472 0.1729 0.2145 0.7849 nan 0.9310 0.9541 0.0 0.5750 0.0191 nan 0.0 0.0 0.0 0.8967 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9062 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9373 0.7201 0.9248 0.0 0.0 0.0 0.0 nan 0.6869 0.8236 0.0 0.5153 0.0188 nan 0.0 0.0 0.0 0.6985 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5649 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7351 0.6375 0.8513 0.0 0.0 0.0 0.0
0.8521 1.3 520 0.9387 0.1754 0.2175 0.7852 nan 0.9459 0.9409 0.0 0.5862 0.0391 nan 0.0 0.0 0.0 0.8778 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9179 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9274 0.8059 0.9185 0.0 0.0 0.0 0.0 nan 0.6392 0.8315 0.0 0.5525 0.0383 nan 0.0 0.0 0.0 0.7149 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5561 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7556 0.6739 0.8523 0.0 0.0 0.0 0.0
1.8729 1.35 540 0.9124 0.1767 0.2181 0.7869 nan 0.9405 0.9449 0.0 0.6155 0.0486 nan 0.0000 0.0 0.0 0.8884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9140 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9311 0.7752 0.9202 0.0 0.0 0.0 0.0 nan 0.6594 0.8249 0.0 0.5603 0.0471 nan 0.0000 0.0 0.0 0.7207 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5630 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7527 0.6722 0.8533 0.0 0.0 0.0 0.0
0.9849 1.4 560 0.9113 0.1781 0.2223 0.7883 nan 0.9474 0.9323 0.0 0.7026 0.0366 nan 0.0000 0.0 0.0 0.9012 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9031 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9198 0.8473 0.9240 0.0 0.0 0.0 0.0 nan 0.6448 0.8255 0.0 0.5907 0.0357 nan 0.0000 0.0 0.0 0.7192 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5728 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7640 0.6903 0.8553 0.0 0.0 0.0 0.0
0.7368 1.45 580 0.9020 0.1797 0.2219 0.7919 nan 0.9410 0.9434 0.0 0.7123 0.0395 nan 0.0000 0.0 0.0 0.8789 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9184 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9260 0.8265 0.9163 0.0 0.0 0.0 0.0 nan 0.6703 0.8327 0.0 0.6143 0.0386 nan 0.0000 0.0 0.0 0.7178 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5594 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7605 0.7026 0.8538 0.0 0.0 0.0 0.0
0.8884 1.5 600 0.9055 0.1789 0.2201 0.7927 nan 0.9278 0.9592 0.0 0.6248 0.0399 nan 0.0 0.0 0.0 0.9106 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9110 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9228 0.8225 0.9229 0.0 0.0 0.0 0.0 nan 0.6812 0.8265 0.0 0.5748 0.0387 nan 0.0 0.0 0.0 0.7103 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5692 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7681 0.7012 0.8540 0.0 0.0 0.0 0.0
0.7291 1.55 620 0.9008 0.1787 0.2228 0.7900 nan 0.9461 0.9404 0.0 0.6101 0.1005 nan 0.0000 0.0 0.0 0.9062 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9094 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9155 0.8713 0.9290 0.0 0.0 0.0 0.0 nan 0.6276 0.8469 0.0 0.5788 0.0962 nan 0.0000 0.0 0.0 0.7110 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5622 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7648 0.6880 0.8430 0.0 0.0 0.0 0.0
0.7366 1.6 640 0.8804 0.1799 0.2250 0.7942 nan 0.9457 0.9424 0.0 0.7139 0.0687 nan 0.0 0.0 0.0 0.9156 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9051 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9154 0.8601 0.9338 0.0 0.0 0.0 0.0 nan 0.6548 0.8488 0.0 0.6189 0.0666 nan 0.0 0.0 0.0 0.7035 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5631 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7690 0.6915 0.8419 0.0 0.0 0.0 0.0
0.8606 1.65 660 0.8687 0.1812 0.2249 0.7965 nan 0.9329 0.9527 0.0 0.7145 0.0836 nan 0.0000 0.0 0.0 0.9109 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9073 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9181 0.8460 0.9319 0.0 0.0 0.0 0.0 nan 0.6843 0.8430 0.0 0.6109 0.0795 nan 0.0000 0.0 0.0 0.7107 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5643 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7685 0.6944 0.8437 0.0 0.0 0.0 0.0
0.8797 1.7 680 0.8755 0.1797 0.2211 0.7946 nan 0.9228 0.9600 0.0 0.6712 0.0447 nan 0.0000 0.0 0.0 0.8878 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9058 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9313 0.8406 0.9120 0.0 0.0 0.0 0.0 nan 0.7002 0.8299 0.0 0.5802 0.0434 nan 0.0000 0.0 0.0 0.7303 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5710 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7558 0.6902 0.8480 0.0 0.0 0.0 0.0
0.8329 1.75 700 0.8782 0.1802 0.2234 0.7910 nan 0.9567 0.9326 0.0 0.7030 0.0780 nan 0.0 0.0 0.0 0.8918 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9066 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9329 0.8216 0.9253 0.0 0.0 0.0 0.0 nan 0.6213 0.8476 0.0 0.6263 0.0752 nan 0.0 0.0 0.0 0.7226 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5718 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7541 0.6980 0.8500 0.0 0.0 0.0 0.0
0.8551 1.8 720 0.8607 0.1806 0.2231 0.7947 nan 0.9434 0.9503 0.0 0.6813 0.0720 nan 0.0000 0.0 0.0 0.8949 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9108 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9230 0.8410 0.9235 0.0 0.0 0.0 0.0 nan 0.6667 0.8402 0.0 0.6013 0.0691 nan 0.0000 0.0 0.0 0.7247 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5676 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7667 0.6951 0.8476 0.0 0.0 0.0 0.0
1.0606 1.85 740 0.8495 0.1805 0.2241 0.7945 nan 0.9486 0.9476 0.0 0.6721 0.0820 nan 0.0 0.0 0.0 0.9137 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9072 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9155 0.8497 0.9342 0.0 0.0 0.0 0.0 nan 0.6515 0.8446 0.0 0.6023 0.0786 nan 0.0 0.0 0.0 0.7120 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5703 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7725 0.6978 0.8463 0.0 0.0 0.0 0.0
0.875 1.9 760 0.8552 0.1819 0.2251 0.7966 nan 0.9384 0.9511 0.0 0.6797 0.1089 nan 0.0001 0.0 0.0 0.9089 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9078 0.0 0.0001 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9227 0.8567 0.9292 0.0 0.0 0.0 0.0 nan 0.6766 0.8437 0.0 0.5987 0.1023 nan 0.0001 0.0 0.0 0.7202 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5687 0.0 0.0001 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7672 0.6915 0.8501 0.0 0.0 0.0 0.0
1.1214 1.95 780 0.8541 0.1823 0.2254 0.7970 nan 0.9338 0.9521 0.0 0.6748 0.1255 nan 0.0002 0.0 0.0 0.9104 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9056 0.0 0.0001 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9270 0.8590 0.9235 0.0 0.0 0.0 0.0 nan 0.6846 0.8435 0.0 0.5999 0.1174 nan 0.0002 0.0 0.0 0.7159 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5677 0.0 0.0001 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7634 0.6887 0.8506 0.0 0.0 0.0 0.0
0.8599 2.0 800 0.8640 0.1813 0.2233 0.7956 nan 0.9372 0.9555 0.0 0.6635 0.0799 nan 0.0001 0.0 0.0 0.8975 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9214 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9128 0.8493 0.9299 0.0 0.0 0.0 0.0 nan 0.6819 0.8378 0.0 0.5970 0.0754 nan 0.0001 0.0 0.0 0.7287 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5594 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7763 0.6951 0.8496 0.0 0.0 0.0 0.0

Framework versions

  • Transformers 4.48.0
  • Pytorch 2.1.1+cu118
  • Datasets 3.2.0
  • Tokenizers 0.21.0
Downloads last month
13
Safetensors
Model size
13.7M params
Tensor type
F32
·
Inference Examples
Image Segmentation
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for heromiya/my-fine-tuned-model

Base model

nvidia/segformer-b1-finetuned-ade-512-512
Finetuned
(26)
this model

Evaluation results

Metadata error: specify a dataset to view leaderboard