segformer-b0-finetuned-segments-sidewalk-oct-22

This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.7960
• eval_mean_iou: 0.2053
• eval_mean_accuracy: 0.2533
• eval_overall_accuracy: 0.7980
• eval_accuracy_unlabeled: nan
• eval_accuracy_flat-road: 0.8959
• eval_accuracy_flat-sidewalk: 0.9420
• eval_accuracy_flat-crosswalk: 0.0
• eval_accuracy_flat-cyclinglane: 0.4535
• eval_accuracy_flat-parkingdriveway: 0.2273
• eval_accuracy_flat-railtrack: nan
• eval_accuracy_flat-curb: 0.3520
• eval_accuracy_human-person: 0.0
• eval_accuracy_human-rider: 0.0
• eval_accuracy_vehicle-car: 0.9346
• eval_accuracy_vehicle-truck: 0.0
• eval_accuracy_vehicle-bus: 0.0
• eval_accuracy_vehicle-tramtrain: nan
• eval_accuracy_vehicle-motorcycle: 0.0
• eval_accuracy_vehicle-bicycle: 0.0
• eval_accuracy_vehicle-caravan: 0.0
• eval_accuracy_vehicle-cartrailer: 0.0
• eval_accuracy_construction-building: 0.8792
• eval_accuracy_construction-door: 0.0
• eval_accuracy_construction-wall: 0.3746
• eval_accuracy_construction-fenceguardrail: 0.0
• eval_accuracy_construction-bridge: 0.0
• eval_accuracy_construction-tunnel: nan
• eval_accuracy_construction-stairs: 0.0
• eval_accuracy_object-pole: 0.0065
• eval_accuracy_object-trafficsign: 0.0
• eval_accuracy_object-trafficlight: 0.0
• eval_accuracy_nature-vegetation: 0.9279
• eval_accuracy_nature-terrain: 0.8991
• eval_accuracy_sky: 0.9585
• eval_accuracy_void-ground: 0.0
• eval_accuracy_void-dynamic: 0.0
• eval_accuracy_void-static: 0.0014
• eval_accuracy_void-unclear: 0.0
• eval_iou_unlabeled: nan
• eval_iou_flat-road: 0.6699
• eval_iou_flat-sidewalk: 0.7999
• eval_iou_flat-crosswalk: 0.0
• eval_iou_flat-cyclinglane: 0.4306
• eval_iou_flat-parkingdriveway: 0.1874
• eval_iou_flat-railtrack: nan
• eval_iou_flat-curb: 0.2837
• eval_iou_human-person: 0.0
• eval_iou_human-rider: 0.0
• eval_iou_vehicle-car: 0.6757
• eval_iou_vehicle-truck: 0.0
• eval_iou_vehicle-bus: 0.0
• eval_iou_vehicle-tramtrain: nan
• eval_iou_vehicle-motorcycle: 0.0
• eval_iou_vehicle-bicycle: 0.0
• eval_iou_vehicle-caravan: 0.0
• eval_iou_vehicle-cartrailer: 0.0
• eval_iou_construction-building: 0.6298
• eval_iou_construction-door: 0.0
• eval_iou_construction-wall: 0.2766
• eval_iou_construction-fenceguardrail: 0.0
• eval_iou_construction-bridge: 0.0
• eval_iou_construction-tunnel: nan
• eval_iou_construction-stairs: 0.0
• eval_iou_object-pole: 0.0065
• eval_iou_object-trafficsign: 0.0
• eval_iou_object-trafficlight: 0.0
• eval_iou_nature-vegetation: 0.8333
• eval_iou_nature-terrain: 0.6916
• eval_iou_sky: 0.8777
• eval_iou_void-ground: 0.0
• eval_iou_void-dynamic: 0.0
• eval_iou_void-static: 0.0014
• eval_iou_void-unclear: 0.0
• eval_runtime: 34.3451
• eval_samples_per_second: 5.823
• eval_steps_per_second: 2.912
• epoch: 2.05
• step: 820

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: 6e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1