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SegFormer_b2 – AI Model by Vrjb | AlphaNeural AI
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Vrjb
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SegFormer_b2
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transformers
tensorboard
safetensors
segformer
vision
image-segmentation
generated_from_trainer
nvidia/segformer-b2-finetuned-cityscapes-1024-1024
finetune
other
endpoints_compatible
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SegFormer_b2
This model is a fine-tuned version of
nvidia/segformer-b2-finetuned-cityscapes-1024-1024
on the Cityscapes dataset. It achieves the following results on the evaluation set:
eval_loss: 0.2516
eval_mean_iou: 0.3875
eval_mean_accuracy: 0.5066
eval_overall_accuracy: 0.9043
eval_accuracy_unlabeled: nan
eval_accuracy_ego vehicle: nan
eval_accuracy_rectification border: nan
eval_accuracy_out of roi: nan
eval_accuracy_static: nan
eval_accuracy_dynamic: nan
eval_accuracy_ground: nan
eval_accuracy_road: 0.9832
eval_accuracy_sidewalk: 0.8421
eval_accuracy_parking: nan
eval_accuracy_rail track: nan
eval_accuracy_building: 0.9158
eval_accuracy_wall: 0.0
eval_accuracy_fence: 0.0
eval_accuracy_guard rail: nan
eval_accuracy_bridge: nan
eval_accuracy_tunnel: nan
eval_accuracy_pole: 0.5362
eval_accuracy_polegroup: nan
eval_accuracy_traffic light: 0.5814
eval_accuracy_traffic sign: 0.7376
eval_accuracy_vegetation: 0.9188
eval_accuracy_terrain: 0.6737
eval_accuracy_sky: 0.9746
eval_accuracy_person: 0.7788
eval_accuracy_rider: 0.0
eval_accuracy_car: 0.9354
eval_accuracy_truck: 0.0
eval_accuracy_bus: 0.0
eval_accuracy_caravan: nan
eval_accuracy_trailer: nan
eval_accuracy_train: 0.0
eval_accuracy_motorcycle: 0.0
eval_accuracy_bicycle: 0.7472
eval_accuracy_license plate: nan
eval_iou_unlabeled: nan
eval_iou_ego vehicle: nan
eval_iou_rectification border: nan
eval_iou_out of roi: nan
eval_iou_static: 0.0
eval_iou_dynamic: nan
eval_iou_ground: nan
eval_iou_road: 0.9649
eval_iou_sidewalk: 0.7403
eval_iou_parking: nan
eval_iou_rail track: nan
eval_iou_building: 0.8430
eval_iou_wall: 0.0
eval_iou_fence: 0.0
eval_iou_guard rail: nan
eval_iou_bridge: nan
eval_iou_tunnel: nan
eval_iou_pole: 0.3619
eval_iou_polegroup: nan
eval_iou_traffic light: 0.4506
eval_iou_traffic sign: 0.5317
eval_iou_vegetation: 0.8647
eval_iou_terrain: 0.4610
eval_iou_sky: 0.8806
eval_iou_person: 0.5967
eval_iou_rider: 0.0
eval_iou_car: 0.8756
eval_iou_truck: 0.0
eval_iou_bus: 0.0
eval_iou_caravan: nan
eval_iou_trailer: nan
eval_iou_train: 0.0
eval_iou_motorcycle: 0.0
eval_iou_bicycle: 0.5665
eval_iou_license plate: 0.0
eval_runtime: 185.4692
eval_samples_per_second: 2.696
eval_steps_per_second: 0.674
epoch: 20.4301
step: 3800
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: 0.0006
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Use 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: 500
num_epochs: 100
mixed_precision_training: Native AMP
Framework versions
Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0
