Hugging Face's logo

mouadenna
/
segformer-b0-finetuned-segments-pv_v1_normalized_p100_4batch

Image Segmentation
Transformers
TensorBoard
Safetensors
segformer
vision
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
segformer-b0-finetuned-segments-pv_v1_normalized_p100_4batch / README.md
mouadenna's picture
mouadenna
End of training
e10bf06 verified
preview code
|
raw
history blame contribute delete
4.94 kB
metadata
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
metrics:
  - precision
model-index:
  - name: segformer-b0-finetuned-segments-pv_v1_normalized_p100_4batch
    results: []

Visualize in Weights & Biases

segformer-b0-finetuned-segments-pv_v1_normalized_p100_4batch

This model is a fine-tuned version of nvidia/segformer-b0-finetuned-ade-512-512 on the mouadenna/satellite_PV_dataset_train_test_v1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0074
  • Mean Iou: 0.8483
  • Precision: 0.9169

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: 0.0004
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.001
  • num_epochs: 40
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Mean Iou Precision
0.0127 0.9989 229 0.0092 0.7982 0.8641
0.0077 1.9978 458 0.0094 0.7871 0.8456
0.006 2.9967 687 0.0067 0.8140 0.9089
0.0051 4.0 917 0.0058 0.8358 0.8713
0.0045 4.9989 1146 0.0059 0.8258 0.8761
0.0042 5.9978 1375 0.0058 0.8415 0.9018
0.0036 6.9967 1604 0.0051 0.8513 0.9049
0.0038 8.0 1834 0.0062 0.8226 0.9256
0.004 8.9989 2063 0.0057 0.8358 0.8913
0.0035 9.9978 2292 0.0053 0.8485 0.9079
0.0037 10.9967 2521 0.0059 0.8192 0.9056
0.0038 12.0 2751 0.0054 0.8487 0.8921
0.0033 12.9989 2980 0.0053 0.8541 0.9086
0.0028 13.9978 3209 0.0055 0.8551 0.8985
0.0026 14.9967 3438 0.0060 0.8483 0.9085
0.0026 16.0 3668 0.0057 0.8495 0.9076
0.0024 16.9989 3897 0.0058 0.8442 0.9083
0.0038 17.9978 4126 0.0066 0.8113 0.8910
0.0031 18.9967 4355 0.0062 0.8488 0.9108
0.0026 20.0 4585 0.0058 0.8575 0.9126
0.0024 20.9989 4814 0.0057 0.8580 0.9119
0.0025 21.9978 5043 0.0059 0.8505 0.8957
0.0031 22.9967 5272 0.0062 0.8472 0.9135
0.0022 24.0 5502 0.0055 0.8598 0.9147
0.0023 24.9989 5731 0.0058 0.8621 0.9090
0.0023 25.9978 5960 0.0064 0.8498 0.9094
0.0023 26.9967 6189 0.0067 0.8428 0.9137
0.0021 28.0 6419 0.0063 0.8527 0.9076
0.002 28.9989 6648 0.0065 0.8509 0.9187
0.002 29.9978 6877 0.0074 0.8424 0.9179
0.002 30.9967 7106 0.0065 0.8577 0.9116
0.0019 32.0 7336 0.0067 0.8547 0.9141
0.0019 32.9989 7565 0.0072 0.8519 0.9168
0.0019 33.9978 7794 0.0067 0.8569 0.9148
0.0019 34.9967 8023 0.0070 0.8544 0.9139
0.0017 36.0 8253 0.0072 0.8510 0.9124
0.0018 36.9989 8482 0.0081 0.8425 0.9164
0.0017 37.9978 8711 0.0073 0.8512 0.9155
0.0018 38.9967 8940 0.0073 0.8495 0.9164
0.0018 39.9564 9160 0.0074 0.8483 0.9169

Framework versions

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