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SegFormer_b2_mappillary_ – AI Model by Vrjb | AlphaNeural AI
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SegFormer_b2_mappillary_
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transformers
safetensors
segformer
generated_from_trainer
nvidia/segformer-b2-finetuned-cityscapes-1024-1024
finetune
other
endpoints_compatible
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SegFormer_b2_mappillary_
This model is a fine-tuned version of
nvidia/segformer-b2-finetuned-cityscapes-1024-1024
on an unknown dataset. It achieves the following results on the evaluation set:
eval_loss: 0.9598
eval_mean_iou: 0.6780
eval_mean_accuracy: 0.7951
eval_overall_accuracy: 0.9391
eval_accuracy_construction--barrier--fence: 0.6674
eval_accuracy_construction--barrier--guard-rail: 0.7787
eval_accuracy_construction--barrier--other-barrier: 0.7093
eval_accuracy_construction--barrier--wall: 0.6692
eval_accuracy_construction--flat--road: 0.9505
eval_accuracy_construction--flat--service-lane: 0.5410
eval_accuracy_construction--flat--sidewalk: 0.9029
eval_accuracy_construction--structure--building: 0.9494
eval_accuracy_human--person: 0.8428
eval_accuracy_human--rider--bicyclist: 0.7374
eval_accuracy_marking--crosswalk-zebra: 0.8275
eval_accuracy_marking--general: 0.6969
eval_accuracy_nature--sky: 0.9902
eval_accuracy_nature--terrain: 0.8238
eval_accuracy_nature--vegetation: 0.9447
eval_accuracy_object--support--pole: 0.5732
eval_accuracy_object--support--traffic-sign-frame: 0.6710
eval_accuracy_object--traffic-light: 0.7524
eval_accuracy_object--traffic-sign--front: 0.8163
eval_accuracy_object--vehicle--bicycle: 0.7771
eval_accuracy_object--vehicle--bus: 0.8829
eval_accuracy_object--vehicle--car: 0.9659
eval_accuracy_object--vehicle--truck: 0.8158
eval_iou_construction--barrier--fence: 0.5508
eval_iou_construction--barrier--guard-rail: 0.6288
eval_iou_construction--barrier--other-barrier: 0.5638
eval_iou_construction--barrier--wall: 0.5354
eval_iou_construction--flat--road: 0.9129
eval_iou_construction--flat--service-lane: 0.4333
eval_iou_construction--flat--sidewalk: 0.7696
eval_iou_construction--structure--building: 0.8821
eval_iou_human--person: 0.6700
eval_iou_human--rider--bicyclist: 0.5363
eval_iou_marking--crosswalk-zebra: 0.7082
eval_iou_marking--general: 0.5822
eval_iou_nature--sky: 0.9811
eval_iou_nature--terrain: 0.6964
eval_iou_nature--vegetation: 0.8935
eval_iou_object--support--pole: 0.4515
eval_iou_object--support--traffic-sign-frame: 0.5508
eval_iou_object--traffic-light: 0.5782
eval_iou_object--traffic-sign--front: 0.7134
eval_iou_object--vehicle--bicycle: 0.5514
eval_iou_object--vehicle--bus: 0.7858
eval_iou_object--vehicle--car: 0.9004
eval_iou_object--vehicle--truck: 0.7182
eval_runtime: 1416.4555
eval_samples_per_second: 1.412
eval_steps_per_second: 0.706
epoch: 14.0
step: 31500
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: 9e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 30
mixed_precision_training: Native AMP
Framework versions
Transformers 4.48.1
Pytorch 2.1.2+cu121
Datasets 3.2.0
Tokenizers 0.21.0
