Hugging Face's logo

mouadenna
/
segformer_b2_finetuned_segment_pv_p100_4batch

Image Segmentation
Transformers
TensorBoard
Safetensors
segformer
vision
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
segformer_b2_finetuned_segment_pv_p100_4batch / README.md
mouadenna's picture
mouadenna
End of training
4adbd9c verified
preview code
|
raw
history blame contribute delete
4.82 kB
metadata
license: other
base_model: nvidia/segformer-b2-finetuned-ade-512-512
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
metrics:
  - precision
model-index:
  - name: segformer_b2_finetuned_segment_pv_p100_4batch
    results: []

Visualize in Weights & Biases

segformer_b2_finetuned_segment_pv_p100_4batch

This model is a fine-tuned version of nvidia/segformer-b2-finetuned-ade-512-512 on the mouadenna/satellite_PV_dataset_train_test_v1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0090
  • Mean Iou: 0.8765
  • Precision: 0.9192

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: 4e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 40
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Mean Iou Precision
0.5015 1.0 917 0.1494 0.5660 0.6026
0.0714 2.0 1834 0.0237 0.7528 0.7988
0.0171 3.0 2751 0.0101 0.7978 0.8930
0.0087 4.0 3668 0.0072 0.8260 0.8534
0.0058 5.0 4585 0.0067 0.8418 0.8981
0.0046 6.0 5502 0.0056 0.8457 0.8971
0.0038 7.0 6419 0.0056 0.8530 0.8770
0.0034 8.0 7336 0.0056 0.8525 0.8978
0.003 9.0 8253 0.0052 0.8643 0.9063
0.0028 10.0 9170 0.0054 0.8641 0.9010
0.0027 11.0 10087 0.0065 0.8489 0.9236
0.0025 12.0 11004 0.0066 0.8432 0.9006
0.0024 13.0 11921 0.0055 0.8637 0.9242
0.0022 14.0 12838 0.0054 0.8679 0.9104
0.0024 15.0 13755 0.0055 0.8719 0.9171
0.0019 16.0 14672 0.0055 0.8746 0.9219
0.0019 17.0 15589 0.0056 0.8668 0.9062
0.0018 18.0 16506 0.0063 0.8703 0.9121
0.0017 19.0 17423 0.0062 0.8694 0.9084
0.0016 20.0 18340 0.0063 0.8719 0.9133
0.0015 21.0 19257 0.0065 0.8734 0.9159
0.0014 22.0 20174 0.0068 0.8730 0.9155
0.0015 23.0 21091 0.0069 0.8719 0.9228
0.0013 24.0 22008 0.0069 0.8745 0.9162
0.0013 25.0 22925 0.0069 0.8757 0.9196
0.0012 26.0 23842 0.0075 0.8747 0.9138
0.0012 27.0 24759 0.0074 0.8750 0.9159
0.0012 28.0 25676 0.0074 0.8755 0.9213
0.0011 29.0 26593 0.0081 0.8762 0.9154
0.0011 30.0 27510 0.0083 0.8754 0.9162
0.0011 31.0 28427 0.0084 0.8753 0.9168
0.001 32.0 29344 0.0083 0.8754 0.9202
0.001 33.0 30261 0.0085 0.8758 0.9174
0.001 34.0 31178 0.0085 0.8758 0.9208
0.0009 35.0 32095 0.0088 0.8763 0.9191
0.0009 36.0 33012 0.0090 0.8756 0.9172
0.0009 37.0 33929 0.0090 0.8760 0.9181
0.0009 38.0 34846 0.0087 0.8764 0.9195
0.0009 39.0 35763 0.0090 0.8763 0.9184
0.0009 40.0 36680 0.0090 0.8765 0.9192

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.1.2
  • Datasets 2.20.0
  • Tokenizers 0.19.1