Hugging Face's logo

Lit4pCol4b
/
mit-b1_segformer_ADE20k_RGB_IS_v1

Transformers
TensorFlow
segformer
generated_from_keras_callback
Inference Endpoints
Model card Files Community
mit-b1_segformer_ADE20k_RGB_IS_v1 / README.md
Lit4pCol4b's picture
Lit4pCol4b
Training in progress epoch 39
de900b4
preview code
|
raw
history blame contribute delete
14.2 kB
metadata
license: other
base_model: nvidia/mit-b1
tags:
  - generated_from_keras_callback
model-index:
  - name: Lit4pCol4b/mit-b1_segformer_ADE20k_RGB_IS_v1
    results: []

Lit4pCol4b/mit-b1_segformer_ADE20k_RGB_IS_v1

This model is a fine-tuned version of nvidia/mit-b1 on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.0787
  • Validation Loss: 0.1007
  • Validation Mean Iou: 0.7646
  • Validation Mean Accuracy: 0.8701
  • Validation Overall Accuracy: 0.9687
  • Validation Accuracy Unlabeled: 0.6791
  • Validation Accuracy Objeto Interes: 0.9475
  • Validation Accuracy Agua: 0.9838
  • Validation Iou Unlabeled: 0.5173
  • Validation Iou Objeto Interes: 0.8005
  • Validation Iou Agua: 0.9760
  • Epoch: 39

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:

  • optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  • training_precision: float32

Training results

Train Loss Validation Loss Validation Mean Iou Validation Mean Accuracy Validation Overall Accuracy Validation Accuracy Unlabeled Validation Accuracy Objeto Interes Validation Accuracy Agua Validation Iou Unlabeled Validation Iou Objeto Interes Validation Iou Agua Epoch
0.6580 0.6718 0.4196 0.6281 0.8767 0.0254 0.9466 0.9124 0.0232 0.3345 0.9012 0
0.4551 0.5040 0.5131 0.6832 0.9126 0.1813 0.9216 0.9467 0.1234 0.4837 0.9322 1
0.3472 0.2565 0.5381 0.6560 0.9375 0.1035 0.8839 0.9805 0.0930 0.5671 0.9542 2
0.2846 0.2434 0.6188 0.7343 0.9442 0.3415 0.8847 0.9767 0.2514 0.6486 0.9564 3
0.2383 0.2245 0.6401 0.7568 0.9469 0.4203 0.8735 0.9767 0.2975 0.6644 0.9586 4
0.2075 0.2243 0.6606 0.7809 0.9501 0.4690 0.8975 0.9764 0.3332 0.6879 0.9608 5
0.1943 0.1820 0.6721 0.7704 0.9559 0.4301 0.8964 0.9847 0.3423 0.7083 0.9658 6
0.1835 0.2237 0.6866 0.8243 0.9510 0.5945 0.9077 0.9707 0.3844 0.7151 0.9601 7
0.1645 0.1638 0.7110 0.8204 0.9586 0.6026 0.8779 0.9808 0.4292 0.7369 0.9670 8
0.1574 0.1359 0.7140 0.8058 0.9616 0.5380 0.8933 0.9861 0.4197 0.7527 0.9695 9
0.1737 0.1421 0.7075 0.8042 0.9596 0.5320 0.8965 0.9841 0.4037 0.7513 0.9675 10
0.1608 0.1613 0.7046 0.8348 0.9564 0.6010 0.9285 0.9750 0.4156 0.7325 0.9655 11
0.1425 0.1387 0.7268 0.8355 0.9618 0.6140 0.9109 0.9816 0.4499 0.7605 0.9698 12
0.1299 0.1230 0.7198 0.8184 0.9628 0.5475 0.9225 0.9851 0.4286 0.7595 0.9714 13
0.1286 0.1279 0.7267 0.8320 0.9630 0.5856 0.9270 0.9833 0.4473 0.7614 0.9715 14
0.1322 0.1201 0.7428 0.8380 0.9651 0.6334 0.8954 0.9854 0.4772 0.7791 0.9722 15
0.1203 0.1076 0.7439 0.8294 0.9663 0.6001 0.9000 0.9880 0.4712 0.7872 0.9732 16
0.1154 0.1314 0.7417 0.8557 0.9633 0.6671 0.9198 0.9802 0.4752 0.7794 0.9706 17
0.1145 0.1098 0.7446 0.8438 0.9662 0.6183 0.9281 0.9852 0.4827 0.7770 0.9739 18
0.1131 0.0994 0.7500 0.8368 0.9676 0.6077 0.9145 0.9881 0.4834 0.7919 0.9748 19
0.1101 0.1157 0.7590 0.8657 0.9664 0.7130 0.9015 0.9827 0.5107 0.7928 0.9733 20
0.1045 0.1099 0.7513 0.8565 0.9664 0.6570 0.9288 0.9835 0.4959 0.7841 0.9739 21
0.1031 0.1045 0.7511 0.8522 0.9668 0.6398 0.9323 0.9846 0.4911 0.7878 0.9743 22
0.1038 0.1245 0.7335 0.8535 0.9628 0.6322 0.9488 0.9794 0.4609 0.7683 0.9713 23
0.0989 0.1130 0.7476 0.8608 0.9652 0.6641 0.9372 0.9813 0.4895 0.7805 0.9729 24
0.0961 0.0993 0.7534 0.8560 0.9672 0.6481 0.9356 0.9844 0.4949 0.7904 0.9748 25
0.0931 0.0977 0.7616 0.8574 0.9684 0.6623 0.9242 0.9858 0.5099 0.7995 0.9754 26
0.0913 0.0899 0.7685 0.8547 0.9701 0.6575 0.9184 0.9883 0.5192 0.8096 0.9768 27
0.0899 0.0984 0.7572 0.8550 0.9683 0.6393 0.9398 0.9858 0.5015 0.7940 0.9759 28
0.0918 0.1307 0.7440 0.8719 0.9635 0.6838 0.9545 0.9773 0.4872 0.7735 0.9713 29
0.0919 0.1239 0.7405 0.8590 0.9641 0.6442 0.9526 0.9801 0.4707 0.7784 0.9725 30
0.0925 0.0990 0.7699 0.8629 0.9696 0.6859 0.9163 0.9865 0.5271 0.8067 0.9761 31
0.0889 0.1069 0.7563 0.8708 0.9664 0.6864 0.9450 0.9811 0.5038 0.7913 0.9738 32
0.0836 0.0913 0.7707 0.8617 0.9702 0.6714 0.9265 0.9873 0.5265 0.8086 0.9770 33
0.0822 0.1041 0.7645 0.8788 0.9672 0.7170 0.9383 0.9809 0.5161 0.8035 0.9740 34
0.0803 0.0981 0.7699 0.8721 0.9691 0.6987 0.9334 0.9843 0.5291 0.8046 0.9759 35
0.0800 0.1018 0.7597 0.8681 0.9678 0.6728 0.9485 0.9830 0.5104 0.7935 0.9752 36
0.0779 0.0975 0.7727 0.8769 0.9692 0.7185 0.9286 0.9837 0.5349 0.8075 0.9757 37
0.0756 0.0984 0.7697 0.8742 0.9691 0.7003 0.9385 0.9838 0.5280 0.8051 0.9760 38
0.0787 0.1007 0.7646 0.8701 0.9687 0.6791 0.9475 0.9838 0.5173 0.8005 0.9760 39

Framework versions

  • Transformers 4.35.2
  • TensorFlow 2.15.0
  • Datasets 2.16.1
  • Tokenizers 0.15.0