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segformer-b0-scene-parse-150

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PyTorch
segformer
generated_from_trainer
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segformer-b0-scene-parse-150

This model is a fine-tuned version of nvidia/mit-b0 on the scene_parse_150 dataset. It achieves the following results on the evaluation set:

  • Loss: 2.1758
  • Mean Iou: 0.0921
  • Mean Accuracy: 0.1570
  • Overall Accuracy: 0.4991
  • Per Category Iou: [0.41162018857503013, nan, nan, 0.6834138471273037, nan, 0.5660888434390043, nan, nan, 0.3905015281802778, nan, 0.20573930793046236, nan, 0.0, nan, 0.3206614178197826, nan, nan, 0.0, 0.02538924181085966, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.07803424888522502, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3195535744469181, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.06786386597868335, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.048377340151072555, nan, nan, nan, nan, nan, nan, 0.1889049414760762, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008826806067357957, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]
  • Per Category Accuracy: [0.5880858834057751, nan, nan, 0.8478921464967599, nan, 0.6622915037409686, nan, nan, 0.5198372544059364, nan, 0.28620150857795507, nan, 0.0, nan, 0.5618023496890118, nan, nan, 0.0, 0.05582151986985824, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.6001152682387974, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6449597442345556, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.07278563750980449, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.10115019566298403, nan, nan, nan, nan, nan, nan, 0.69300215594682, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.018489075802646426, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
4.6151 1.0 20 4.8677 0.0104 0.0604 0.1408 [0.17027168426258588, nan, 0.0, 0.27755221448471773, nan, 0.05448688197876, nan, 0.0, 0.023375761992241534, nan, 0.019909329254970506, 0.0, 0.0, 0.0, 0.0001365023724112325, nan, nan, 0.0, 0.015212968432106058, 0.00021881483402489627, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.12024177115497606, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.03168443628396414, 0.0, 0.0, 0.0, 0.0, 0.0003295892000210937, 0.0, nan, 0.0, nan, 0.0364623653535644, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.000771122781360101, nan, nan, nan, 0.0, 0.0, nan, 0.17151345683327507, 0.0, 0.0, 0.0, nan, 0.04636148137559709, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0053908948885515, 0.08277320444075222, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan] [0.2319163712632688, nan, nan, 0.2940682311295476, nan, 0.058238885062972, nan, nan, 0.03764370239199483, nan, 0.02312835603074263, nan, 0.0, nan, 0.00013821700069108501, nan, nan, 0.0, 0.02693469672321636, 0.0009846108963605864, nan, nan, nan, nan, 0.0, nan, nan, 0.9239229623937371, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04003158079419849, nan, nan, nan, 0.0, 0.0029161320424588824, 0.0, nan, nan, nan, 0.04788949249048601, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.001253972715283333, nan, nan, nan, nan, nan, nan, 0.220400646784046, 0.0, 0.0, nan, nan, 0.1583207907903836, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.018848086983280336, 0.08717092181659111, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]
4.284 2.0 40 4.3923 0.0254 0.0906 0.3149 [0.3184123550360134, nan, 0.0, 0.4307149320858523, nan, 0.1314056166763173, nan, nan, 0.037393746648473294, nan, 0.1285987741448984, nan, 0.0, 0.0, 0.033212403904292996, nan, nan, 0.0, 0.05854402613355651, 0.00044776119402985075, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.047829095312229716, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.07703761248138798, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.07446849067873074, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.01401516318920085, nan, nan, nan, 0.0, 0.0, nan, 0.19180411843048867, 0.0, 0.0, 0.0, nan, 0.019722772277227723, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.009541284403669725, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan] [0.5744131249573771, nan, nan, 0.5528940438980444, nan, 0.16952832280881958, nan, nan, 0.049491244343255476, nan, 0.21101055687307166, nan, 0.0, nan, 0.03826399447131997, nan, nan, 0.0, 0.2015802928189635, 0.0007658084749471227, nan, nan, nan, nan, 0.0, nan, nan, 0.7436722539743528, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.09279295085482582, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.08511750864247741, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.019663589388796403, nan, nan, nan, nan, nan, nan, 0.42045454545454547, 0.0, 0.0, nan, nan, 0.08103160720823334, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.019335316442712074, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]
3.711 3.0 60 4.2270 0.0383 0.1038 0.3009 [0.249433620089641, nan, nan, 0.46788635210118407, nan, 0.09850860152341026, nan, nan, 0.10942962177766752, nan, 0.1539391010149831, nan, 0.0, 0.0, 0.13911755661514427, nan, nan, 0.0, 0.002919679802003171, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.037892131386890834, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.19230239727052345, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0031469481754852736, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.16641495327102804, 0.0, 0.0, nan, nan, 0.018944044904402737, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004826897470039946, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] [0.37557636717146203, nan, nan, 0.6328919582050294, nan, 0.13110314801629452, nan, nan, 0.1704753070804171, nan, 0.4496625131861769, nan, 0.0, nan, 0.2176033172080166, nan, nan, 0.0, 0.0035091796421101557, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.899956774410451, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3131469676589274, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0031955378671237253, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.49986525332375137, 0.0, 0.0, nan, nan, 0.030752959362160843, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008180326187301261, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]
3.4504 4.0 80 3.9247 0.0547 0.1105 0.3841 [0.3137979814549847, nan, nan, 0.5066106759266736, nan, 0.25900593118968096, nan, nan, 0.1435600297802091, nan, 0.22105247182143797, nan, 0.0, nan, 0.18116160181766544, nan, nan, 0.0, 1.8487705675725643e-05, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.029307694688302712, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.17504850256501436, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.007133489933853094, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.00296253546866734, nan, nan, nan, nan, nan, nan, 0.1615670226028511, 0.0, 0.0, nan, nan, 0.016052341283426527, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005611149036780499, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] [0.4312945279739015, nan, nan, 0.9086321619656498, nan, 0.425863457565394, nan, nan, 0.18698878488911375, nan, 0.34656842138659094, nan, 0.0, nan, 0.3526579129232896, nan, nan, 0.0, 2.3239600278875203e-05, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.40934633302915324, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.32627639043218903, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.007189960201028382, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0033295137612695393, nan, nan, nan, nan, nan, nan, 0.5355282069708948, 0.0, 0.0, nan, nan, 0.03433266891754465, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008641911990973434, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]
3.2509 5.0 100 3.7527 0.0559 0.1067 0.3727 [0.3160593417612721, nan, nan, 0.4981058286418981, nan, 0.20715121550514953, nan, nan, 0.1669007615779779, nan, 0.18184632800065792, nan, 0.0, nan, 0.21229666540778316, nan, nan, 0.0, 0.0010542438286953236, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.022360427182527447, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.24265195862337635, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0006153670020178313, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0043765833078689784, nan, nan, nan, nan, nan, nan, 0.15517968779963948, 0.0, 0.0, nan, nan, 0.0011087219459750034, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0009503859730788626, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] [0.49131326121175933, nan, nan, 0.6833019996581781, nan, 0.3120119524237508, nan, nan, 0.2413146133738089, nan, 0.2981464002728448, nan, 0.0, nan, 0.4319889426399447, nan, nan, 0.0, 0.0012316988147803858, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.36573651601748236, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.46141099869388075, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0006245824013014554, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004799688668843102, nan, nan, nan, nan, nan, nan, 0.5451850521020482, 0.0, 0.0, nan, nan, 0.0013423910832689257, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.001256539132218689, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]
3.2116 6.0 120 3.6111 0.0637 0.1162 0.4160 [0.34098758350362696, nan, nan, 0.5151821304579437, nan, 0.3520678204694888, nan, nan, 0.19754404122733252, nan, 0.19452453310475598, nan, 0.0, nan, 0.21872351506248297, nan, nan, 0.0, 0.001433184919072825, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.01684102240768548, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2520954606355202, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.001931483175764969, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.022284976451681825, nan, nan, nan, nan, nan, nan, 0.1692149276085871, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008681415738566596, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] [0.47654517897479365, nan, nan, 0.8124353652425053, nan, 0.5896898672860761, nan, nan, 0.2575118756499986, nan, 0.2969963276992996, nan, 0.0, nan, 0.4922515549412578, nan, nan, 0.0, 0.0017429700209156402, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.19499543729888094, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5373608593095113, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.001931847892397525, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.02593345296521307, nan, nan, nan, nan, nan, nan, 0.4871541501976285, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.010334393271104728, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]
2.9533 7.0 140 3.4759 0.0621 0.1189 0.4093 [0.3327814075042001, nan, nan, 0.4985825460173441, nan, 0.3568714365911989, nan, nan, 0.18809348043726679, nan, 0.1912163618951158, nan, 0.0, nan, 0.22283685871739006, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.03442924994903006, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.23634676051513528, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.01931469600642533, nan, nan, nan, nan, nan, nan, 0.15367159010415302, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0025497086047308877, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] [0.49622482112263033, nan, nan, 0.7596151896387406, nan, 0.6418358175854704, nan, nan, 0.2614048402057509, nan, 0.30247701837736657, nan, 0.0, nan, 0.41335729094678647, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.4379712789971663, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.41697370216581864, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.021576978790564937, nan, nan, nan, nan, nan, nan, 0.5261857707509882, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.003051595035388245, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]
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0.7549 50.0 1000 2.1758 0.0921 0.1570 0.4991 [0.41162018857503013, nan, nan, 0.6834138471273037, nan, 0.5660888434390043, nan, nan, 0.3905015281802778, nan, 0.20573930793046236, nan, 0.0, nan, 0.3206614178197826, nan, nan, 0.0, 0.02538924181085966, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.07803424888522502, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.3195535744469181, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.06786386597868335, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.048377340151072555, nan, nan, nan, nan, nan, nan, 0.1889049414760762, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.008826806067357957, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] [0.5880858834057751, nan, nan, 0.8478921464967599, nan, 0.6622915037409686, nan, nan, 0.5198372544059364, nan, 0.28620150857795507, nan, 0.0, nan, 0.5618023496890118, nan, nan, 0.0, 0.05582151986985824, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.6001152682387974, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6449597442345556, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.07278563750980449, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.10115019566298403, nan, nan, nan, nan, nan, nan, 0.69300215594682, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.018489075802646426, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]

Framework versions

  • Transformers 4.33.2
  • Pytorch 1.13.1+cpu
  • Datasets 2.14.5
  • Tokenizers 0.13.3
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Dataset used to train GhaithIA/segformer-b0-scene-parse-150

Evaluation results

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