segformer-b0-finetuned-segments-sidewalk-oct-22
This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:
- Loss: 1.1574
- Mean Iou: 0.1657
- Mean Accuracy: 0.2143
- Overall Accuracy: 0.7508
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.8145
- Accuracy Flat-sidewalk: 0.9504
- Accuracy Flat-crosswalk: 0.0
- Accuracy Flat-cyclinglane: 0.5828
- Accuracy Flat-parkingdriveway: 0.0118
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.0001
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.8895
- 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: 0.8937
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0389
- 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: 0.9244
- Accuracy Nature-terrain: 0.8287
- Accuracy Sky: 0.9224
- 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: 0.5381
- Iou Flat-sidewalk: 0.7939
- Iou Flat-crosswalk: 0.0
- Iou Flat-cyclinglane: 0.5124
- Iou Flat-parkingdriveway: 0.0117
- Iou Flat-railtrack: nan
- Iou Flat-curb: 0.0001
- Iou Human-person: 0.0
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.6117
- 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: 0.5570
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0365
- 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: 0.7504
- Iou Nature-terrain: 0.6347
- Iou Sky: 0.8566
- 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2.6835 | 0.25 | 20 | 2.9300 | 0.0679 | 0.1189 | 0.5663 | nan | 0.0864 | 0.9582 | 0.0005 | 0.0171 | 0.0000 | nan | 0.0049 | 0.0042 | 0.0 | 0.8284 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7913 | 0.0 | 0.0535 | 0.0 | 0.0 | nan | 0.0029 | 0.0 | 0.0 | 0.0 | 0.9260 | 0.0016 | 0.1138 | 0.0168 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0824 | 0.5815 | 0.0005 | 0.0168 | 0.0000 | 0.0 | 0.0045 | 0.0040 | 0.0 | 0.5349 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4154 | 0.0 | 0.0414 | 0.0 | 0.0 | 0.0 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.5765 | 0.0016 | 0.1137 | 0.0025 | 0.0 | 0.0 | 0.0 |
2.3707 | 0.5 | 40 | 2.1968 | 0.0875 | 0.1387 | 0.6231 | nan | 0.5613 | 0.9361 | 0.0 | 0.0210 | 0.0003 | nan | 0.0019 | 0.0 | 0.0 | 0.7965 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8356 | 0.0 | 0.0281 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9513 | 0.0032 | 0.3006 | 0.0011 | 0.0 | 0.0 | 0.0 | nan | 0.3878 | 0.6646 | 0.0 | 0.0208 | 0.0003 | 0.0 | 0.0019 | 0.0 | 0.0 | 0.5587 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4439 | 0.0 | 0.0249 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5675 | 0.0032 | 0.3003 | 0.0009 | 0.0 | 0.0 | 0.0 |
2.0797 | 0.75 | 60 | 1.9742 | 0.1106 | 0.1579 | 0.6543 | nan | 0.7248 | 0.9240 | 0.0 | 0.0036 | 0.0008 | nan | 0.0000 | 0.0 | 0.0 | 0.8368 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8428 | 0.0 | 0.0254 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9498 | 0.0444 | 0.7003 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4154 | 0.7036 | 0.0 | 0.0036 | 0.0008 | nan | 0.0000 | 0.0 | 0.0 | 0.5498 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4939 | 0.0 | 0.0240 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6151 | 0.0426 | 0.6908 | 0.0 | 0.0 | 0.0 | 0.0 |
1.9067 | 1.0 | 80 | 1.7288 | 0.1234 | 0.1699 | 0.6749 | nan | 0.7502 | 0.9269 | 0.0 | 0.0766 | 0.0014 | nan | 0.0000 | 0.0 | 0.0 | 0.8214 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8719 | 0.0 | 0.0111 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9451 | 0.2194 | 0.8139 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4498 | 0.7152 | 0.0 | 0.0761 | 0.0013 | nan | 0.0000 | 0.0 | 0.0 | 0.5696 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5007 | 0.0 | 0.0108 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6444 | 0.1954 | 0.7863 | 0.0 | 0.0 | 0.0 | 0.0 |
1.6673 | 1.25 | 100 | 1.6680 | 0.1258 | 0.1752 | 0.6789 | nan | 0.8230 | 0.9033 | 0.0 | 0.0493 | 0.0022 | nan | 0.0000 | 0.0 | 0.0 | 0.8676 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8389 | 0.0 | 0.0075 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9609 | 0.2915 | 0.8618 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4254 | 0.7481 | 0.0 | 0.0488 | 0.0022 | nan | 0.0000 | 0.0 | 0.0 | 0.5539 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5266 | 0.0 | 0.0074 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6400 | 0.2581 | 0.8161 | 0.0 | 0.0 | 0.0 | 0.0 |
1.7134 | 1.5 | 120 | 1.5470 | 0.1376 | 0.1870 | 0.6997 | nan | 0.7864 | 0.9287 | 0.0 | 0.1234 | 0.0027 | nan | 0.0000 | 0.0 | 0.0 | 0.8766 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8540 | 0.0 | 0.0060 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9433 | 0.5618 | 0.9008 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4735 | 0.7320 | 0.0 | 0.1216 | 0.0027 | nan | 0.0000 | 0.0 | 0.0 | 0.5599 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5319 | 0.0 | 0.0059 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6897 | 0.4601 | 0.8255 | 0.0 | 0.0 | 0.0 | 0.0 |
1.4384 | 1.75 | 140 | 1.4997 | 0.1458 | 0.1931 | 0.7143 | nan | 0.7973 | 0.9446 | 0.0 | 0.2050 | 0.0025 | nan | 0.0 | 0.0 | 0.0 | 0.8691 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8695 | 0.0 | 0.0025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9304 | 0.6705 | 0.8889 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4842 | 0.7479 | 0.0 | 0.1960 | 0.0025 | nan | 0.0 | 0.0 | 0.0 | 0.5860 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5294 | 0.0 | 0.0025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7244 | 0.5600 | 0.8314 | 0.0 | 0.0 | 0.0 | 0.0 |
1.4101 | 2.0 | 160 | 1.4325 | 0.1485 | 0.1990 | 0.7167 | nan | 0.8247 | 0.9212 | 0.0 | 0.2410 | 0.0032 | nan | 0.0 | 0.0 | 0.0 | 0.8787 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8896 | 0.0 | 0.0042 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9131 | 0.7902 | 0.9017 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4675 | 0.7648 | 0.0 | 0.2283 | 0.0032 | nan | 0.0 | 0.0 | 0.0 | 0.5777 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5259 | 0.0 | 0.0041 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7388 | 0.6051 | 0.8352 | 0.0 | 0.0 | 0.0 | 0.0 |
1.4613 | 2.25 | 180 | 1.3689 | 0.1522 | 0.2012 | 0.7248 | nan | 0.7783 | 0.9430 | 0.0 | 0.3362 | 0.0037 | nan | 0.0 | 0.0 | 0.0 | 0.8668 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8788 | 0.0 | 0.0033 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9201 | 0.7933 | 0.9142 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4951 | 0.7564 | 0.0 | 0.3089 | 0.0037 | nan | 0.0 | 0.0 | 0.0 | 0.5926 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5366 | 0.0 | 0.0033 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7330 | 0.6059 | 0.8348 | 0.0 | 0.0 | 0.0 | 0.0 |
1.1652 | 2.5 | 200 | 1.3458 | 0.1566 | 0.2036 | 0.7323 | nan | 0.7605 | 0.9551 | 0.0 | 0.4259 | 0.0038 | nan | 0.0 | 0.0 | 0.0 | 0.8580 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8902 | 0.0 | 0.0066 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9193 | 0.7943 | 0.9027 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5290 | 0.7548 | 0.0 | 0.3710 | 0.0038 | nan | 0.0 | 0.0 | 0.0 | 0.6064 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5270 | 0.0 | 0.0065 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7467 | 0.6256 | 0.8409 | 0.0 | 0.0 | 0.0 | 0.0 |
1.3201 | 2.75 | 220 | 1.2652 | 0.1572 | 0.2057 | 0.7353 | nan | 0.7825 | 0.9559 | 0.0 | 0.4231 | 0.0056 | nan | 0.0000 | 0.0 | 0.0 | 0.8904 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8834 | 0.0 | 0.0122 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9104 | 0.8184 | 0.9014 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5340 | 0.7600 | 0.0 | 0.3872 | 0.0056 | nan | 0.0000 | 0.0 | 0.0 | 0.5909 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5480 | 0.0 | 0.0120 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7383 | 0.6066 | 0.8463 | 0.0 | 0.0 | 0.0 | 0.0 |
1.2234 | 3.0 | 240 | 1.2746 | 0.1594 | 0.2088 | 0.7376 | nan | 0.8409 | 0.9292 | 0.0 | 0.4723 | 0.0054 | nan | 0.0 | 0.0 | 0.0 | 0.8857 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8767 | 0.0 | 0.0206 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9318 | 0.7948 | 0.9251 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4920 | 0.7916 | 0.0 | 0.4224 | 0.0053 | nan | 0.0 | 0.0 | 0.0 | 0.6083 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5504 | 0.0 | 0.0199 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7396 | 0.6269 | 0.8444 | 0.0 | 0.0 | 0.0 | 0.0 |
1.4557 | 3.25 | 260 | 1.2698 | 0.1584 | 0.2075 | 0.7313 | nan | 0.8659 | 0.9053 | 0.0 | 0.4557 | 0.0059 | nan | 0.0 | 0.0 | 0.0 | 0.8611 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8783 | 0.0 | 0.0141 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9394 | 0.7982 | 0.9172 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4660 | 0.7939 | 0.0 | 0.4017 | 0.0059 | nan | 0.0 | 0.0 | 0.0 | 0.6271 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5439 | 0.0 | 0.0137 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7346 | 0.6333 | 0.8478 | 0.0 | 0.0 | 0.0 | 0.0 |
1.2238 | 3.5 | 280 | 1.2213 | 0.1610 | 0.2090 | 0.7427 | nan | 0.8092 | 0.9490 | 0.0 | 0.4971 | 0.0087 | nan | 0.0 | 0.0 | 0.0 | 0.8902 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8860 | 0.0 | 0.0207 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9339 | 0.7840 | 0.9103 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5269 | 0.7830 | 0.0 | 0.4531 | 0.0086 | nan | 0.0 | 0.0 | 0.0 | 0.5897 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5528 | 0.0 | 0.0200 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7379 | 0.6292 | 0.8511 | 0.0 | 0.0 | 0.0 | 0.0 |
1.066 | 3.75 | 300 | 1.1935 | 0.1624 | 0.2109 | 0.7442 | nan | 0.8253 | 0.9479 | 0.0 | 0.5012 | 0.0083 | nan | 0.0 | 0.0 | 0.0 | 0.8755 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8923 | 0.0 | 0.0307 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9137 | 0.8313 | 0.9234 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5267 | 0.7849 | 0.0 | 0.4499 | 0.0082 | nan | 0.0 | 0.0 | 0.0 | 0.6217 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5554 | 0.0 | 0.0294 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7463 | 0.6202 | 0.8525 | 0.0 | 0.0 | 0.0 | 0.0 |
1.1549 | 4.0 | 320 | 1.1899 | 0.1632 | 0.2118 | 0.7464 | nan | 0.8334 | 0.9423 | 0.0 | 0.5290 | 0.0073 | nan | 0.0000 | 0.0 | 0.0 | 0.8857 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8956 | 0.0 | 0.0194 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9280 | 0.8115 | 0.9247 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5215 | 0.7940 | 0.0 | 0.4730 | 0.0073 | nan | 0.0000 | 0.0 | 0.0 | 0.6143 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5530 | 0.0 | 0.0188 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7488 | 0.6382 | 0.8541 | 0.0 | 0.0 | 0.0 | 0.0 |
1.0461 | 4.25 | 340 | 1.1746 | 0.1643 | 0.2127 | 0.7482 | nan | 0.8171 | 0.9549 | 0.0 | 0.5455 | 0.0078 | nan | 0.0 | 0.0 | 0.0 | 0.8974 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8812 | 0.0 | 0.0387 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9161 | 0.8302 | 0.9174 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5322 | 0.7872 | 0.0 | 0.4917 | 0.0077 | nan | 0.0 | 0.0 | 0.0 | 0.6076 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5651 | 0.0 | 0.0366 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7456 | 0.6282 | 0.8553 | 0.0 | 0.0 | 0.0 | 0.0 |
1.0398 | 4.5 | 360 | 1.1687 | 0.1652 | 0.2128 | 0.7494 | nan | 0.8254 | 0.9501 | 0.0 | 0.5507 | 0.0094 | nan | 0.0000 | 0.0 | 0.0 | 0.8755 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8972 | 0.0 | 0.0341 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9253 | 0.8186 | 0.9232 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5323 | 0.7930 | 0.0 | 0.4922 | 0.0093 | nan | 0.0000 | 0.0 | 0.0 | 0.6277 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5531 | 0.0 | 0.0323 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7518 | 0.6382 | 0.8553 | 0.0 | 0.0 | 0.0 | 0.0 |
1.0538 | 4.75 | 380 | 1.1675 | 0.1655 | 0.2135 | 0.7499 | nan | 0.8351 | 0.9458 | 0.0 | 0.5657 | 0.0087 | nan | 0.0000 | 0.0 | 0.0 | 0.8830 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8980 | 0.0 | 0.0322 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9244 | 0.8123 | 0.9262 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5260 | 0.7971 | 0.0 | 0.5002 | 0.0086 | nan | 0.0000 | 0.0 | 0.0 | 0.6214 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5537 | 0.0 | 0.0305 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7554 | 0.6472 | 0.8564 | 0.0 | 0.0 | 0.0 | 0.0 |
1.0232 | 5.0 | 400 | 1.1574 | 0.1657 | 0.2143 | 0.7508 | nan | 0.8145 | 0.9504 | 0.0 | 0.5828 | 0.0118 | nan | 0.0001 | 0.0 | 0.0 | 0.8895 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8937 | 0.0 | 0.0389 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9244 | 0.8287 | 0.9224 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5381 | 0.7939 | 0.0 | 0.5124 | 0.0117 | nan | 0.0001 | 0.0 | 0.0 | 0.6117 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5570 | 0.0 | 0.0365 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7504 | 0.6347 | 0.8566 | 0.0 | 0.0 | 0.0 | 0.0 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 8
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 nguyenb2240/segformer-b0-finetuned-segments-sidewalk-oct-22
Base model
nvidia/mit-b0