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segformer-b0-scene-parse-150 – AI Model by SGliese | AlphaNeural AI
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SGliese
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segformer-b0-scene-parse-150
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transformers
tensorboard
safetensors
segformer
generated_from_trainer
scene_parse_150
nvidia/mit-b0
finetune
other
endpoints_compatible
us
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segformer-b0-scene-parse-150
This model is a fine-tuned version of
nvidia/mit-b0
on the scene_parse_150 dataset. It achieves the following results on the evaluation set:
eval_loss: 3.4741
eval_mean_iou: 0.0469
eval_mean_accuracy: 0.0897
eval_overall_accuracy: 0.4191
eval_per_category_iou: [0.2665166173083251, 0.4167168366238619, 0.8361890939548855, 0.33169163825937054, 0.3642621950031498, 0.07457156680502151, 0.38010872244357347, 0.0, 0.028505594009054825, 0.0, 0.08140381534876948, 0.0, 0.0, 0.0, nan, 0.002712308267863823, 0.21742988456929635, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0]
eval_per_category_accuracy: [0.7619990215632408, 0.5866148267878205, 0.9802105067142923, 0.7539035299949252, 0.7073735188957063, 0.0895565749235474, 0.924393826615639, nan, 0.029600897280119637, 0.0, 0.09706546275395034, 0.0, 0.0, 0.0, nan, 0.002747513027001421, 0.3571304001195874, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0]
eval_runtime: 14.2264
eval_samples_per_second: 0.703
eval_steps_per_second: 0.351
epoch: 4.0
step: 80
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 4.38.1
Pytorch 2.1.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2
