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--- |
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library_name: transformers |
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license: other |
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base_model: nvidia/segformer-b2-finetuned-cityscapes-1024-1024 |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: SegFormer_b2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# SegFormer_b2 |
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This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b2-finetuned-cityscapes-1024-1024) on the Cityscapes dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.2516 |
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- eval_mean_iou: 0.3875 |
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- eval_mean_accuracy: 0.5066 |
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- eval_overall_accuracy: 0.9043 |
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- eval_accuracy_unlabeled: nan |
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- eval_accuracy_ego vehicle: nan |
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- eval_accuracy_rectification border: nan |
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- eval_accuracy_out of roi: nan |
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- eval_accuracy_static: nan |
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- eval_accuracy_dynamic: nan |
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- eval_accuracy_ground: nan |
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- eval_accuracy_road: 0.9832 |
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- eval_accuracy_sidewalk: 0.8421 |
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- eval_accuracy_parking: nan |
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- eval_accuracy_rail track: nan |
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- eval_accuracy_building: 0.9158 |
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- eval_accuracy_wall: 0.0 |
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- eval_accuracy_fence: 0.0 |
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- eval_accuracy_guard rail: nan |
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- eval_accuracy_bridge: nan |
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- eval_accuracy_tunnel: nan |
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- eval_accuracy_pole: 0.5362 |
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- eval_accuracy_polegroup: nan |
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- eval_accuracy_traffic light: 0.5814 |
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- eval_accuracy_traffic sign: 0.7376 |
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- eval_accuracy_vegetation: 0.9188 |
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- eval_accuracy_terrain: 0.6737 |
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- eval_accuracy_sky: 0.9746 |
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- eval_accuracy_person: 0.7788 |
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- eval_accuracy_rider: 0.0 |
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- eval_accuracy_car: 0.9354 |
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- eval_accuracy_truck: 0.0 |
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- eval_accuracy_bus: 0.0 |
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- eval_accuracy_caravan: nan |
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- eval_accuracy_trailer: nan |
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- eval_accuracy_train: 0.0 |
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- eval_accuracy_motorcycle: 0.0 |
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- eval_accuracy_bicycle: 0.7472 |
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- eval_accuracy_license plate: nan |
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- eval_iou_unlabeled: nan |
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- eval_iou_ego vehicle: nan |
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- eval_iou_rectification border: nan |
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- eval_iou_out of roi: nan |
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- eval_iou_static: 0.0 |
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- eval_iou_dynamic: nan |
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- eval_iou_ground: nan |
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- eval_iou_road: 0.9649 |
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- eval_iou_sidewalk: 0.7403 |
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- eval_iou_parking: nan |
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- eval_iou_rail track: nan |
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- eval_iou_building: 0.8430 |
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- eval_iou_wall: 0.0 |
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- eval_iou_fence: 0.0 |
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- eval_iou_guard rail: nan |
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- eval_iou_bridge: nan |
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- eval_iou_tunnel: nan |
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- eval_iou_pole: 0.3619 |
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- eval_iou_polegroup: nan |
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- eval_iou_traffic light: 0.4506 |
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- eval_iou_traffic sign: 0.5317 |
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- eval_iou_vegetation: 0.8647 |
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- eval_iou_terrain: 0.4610 |
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- eval_iou_sky: 0.8806 |
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- eval_iou_person: 0.5967 |
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- eval_iou_rider: 0.0 |
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- eval_iou_car: 0.8756 |
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- eval_iou_truck: 0.0 |
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- eval_iou_bus: 0.0 |
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- eval_iou_caravan: nan |
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- eval_iou_trailer: nan |
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- eval_iou_train: 0.0 |
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- eval_iou_motorcycle: 0.0 |
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- eval_iou_bicycle: 0.5665 |
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- eval_iou_license plate: 0.0 |
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- eval_runtime: 185.4692 |
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- eval_samples_per_second: 2.696 |
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- eval_steps_per_second: 0.674 |
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- epoch: 20.4301 |
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- step: 3800 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0006 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 100 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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