Edit model card

bhaskarSingha/segformer-finetuned-paddyV1

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

  • Train Loss: nan
  • Validation Loss: nan
  • Validation Mean Iou: 0.0004
  • Validation Mean Accuracy: 0.5
  • Validation Overall Accuracy: 0.1499
  • Validation Accuracy Healthy: 1.0
  • Validation Accuracy Brownspot: 0.0
  • Validation Accuracy Leafblast: nan
  • Validation Iou Healthy: 0.0009
  • Validation Iou Brownspot: 0.0
  • Validation Iou Leafblast: nan
  • Epoch: 2

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': {'module': 'keras.optimizers.schedules', 'class_name': 'CosineDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1000, 'alpha': 0.0, 'name': 'CosineDecay', 'warmup_target': 5e-05, 'warmup_steps': 100}, 'registered_name': None}, '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 Healthy Validation Accuracy Brownspot Validation Accuracy Leafblast Validation Iou Healthy Validation Iou Brownspot Validation Iou Leafblast Epoch
nan nan 0.0004 0.5 0.1499 1.0 0.0 nan 0.0009 0.0 nan 0
nan nan 0.0004 0.5 0.1499 1.0 0.0 nan 0.0009 0.0 nan 1
nan nan 0.0004 0.5 0.1499 1.0 0.0 nan 0.0009 0.0 nan 2

Framework versions

  • Transformers 4.40.2
  • TensorFlow 2.15.0
  • Datasets 2.15.0
  • Tokenizers 0.19.1
Downloads last month
0
Unable to determine this model’s pipeline type. Check the docs .

Finetuned from