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segformer-b1-finetuned-segments-pv_v1_x3_normalized_p100_4batch

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metadata
license: other
base_model: nvidia/segformer-b1-finetuned-ade-512-512
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
metrics:
  - precision
model-index:
  - name: segformer-b1-finetuned-segments-pv_v1_x3_normalized_p100_4batch
    results: []

Visualize in Weights & Biases

segformer-b1-finetuned-segments-pv_v1_x3_normalized_p100_4batch

This model is a fine-tuned version of nvidia/segformer-b1-finetuned-ade-512-512 on the mouadenna/satellite_PV_dataset_train_test_v1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0064
  • Mean Iou: 0.8466
  • Precision: 0.9220

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
0.0084 0.9993 687 0.0063 0.8160 0.8736
0.007 2.0 1375 0.0060 0.8262 0.9006
0.006 2.9993 2062 0.0066 0.8072 0.9214
0.0049 4.0 2750 0.0054 0.8283 0.9287
0.004 4.9993 3437 0.0070 0.8326 0.9068
0.0042 6.0 4125 0.0053 0.8318 0.8834
0.004 6.9993 4812 0.0053 0.8370 0.8893
0.0037 8.0 5500 0.0075 0.8049 0.9404
0.0036 8.9993 6187 0.0074 0.8222 0.9106
0.0033 10.0 6875 0.0061 0.8297 0.9161
0.0031 10.9993 7562 0.0055 0.8427 0.9086
0.0033 12.0 8250 0.0052 0.8437 0.9152
0.0037 12.9993 8937 0.0055 0.8387 0.9186
0.0028 14.0 9625 0.0060 0.8416 0.9137
0.0027 14.9993 10312 0.0052 0.8489 0.9212
0.003 16.0 11000 0.0065 0.8393 0.9158
0.0025 16.9993 11687 0.0063 0.8347 0.9245
0.0027 18.0 12375 0.0065 0.8439 0.9093
0.0032 18.9993 13062 0.0056 0.8495 0.9186
0.0024 20.0 13750 0.0064 0.8466 0.9220

Framework versions

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