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deeplabv3-mobilevit-small_corm

Image Segmentation
Transformers
Safetensors
mobilevit
vision
Generated from Trainer
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metadata
library_name: transformers
license: apple-amlr
base_model: apple/deeplabv3-mobilevit-small
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: deeplabv3-mobilevit-small_corm
    results: []

deeplabv3-mobilevit-small_corm

This model is a fine-tuned version of apple/deeplabv3-mobilevit-small on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7777
  • Mean Iou: 0.4137
  • Mean Accuracy: 0.5038
  • Overall Accuracy: 0.7714
  • Accuracy Background: 0.9998
  • Accuracy Corm: 0.0748
  • Accuracy Damage: 0.4368
  • Iou Background: 0.7626
  • Iou Corm: 0.0734
  • Iou Damage: 0.4050

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: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Corm Accuracy Damage Iou Background Iou Corm Iou Damage
1.0611 0.3077 20 1.0627 0.4251 0.6398 0.6594 0.6709 0.5632 0.6853 0.6508 0.2723 0.3522
1.0077 0.6154 40 1.0227 0.5359 0.6715 0.8018 0.9058 0.4028 0.7060 0.8285 0.3049 0.4743
1.0089 0.9231 60 0.9859 0.5131 0.6138 0.8167 0.9804 0.2104 0.6506 0.8345 0.1883 0.5167
0.9397 1.2308 80 0.9637 0.4767 0.5831 0.8103 0.9867 0.0722 0.6904 0.8293 0.0701 0.5307
0.9347 1.5385 100 0.9257 0.4544 0.5545 0.7991 0.9946 0.0517 0.6172 0.8103 0.0504 0.5024
0.9007 1.8462 120 0.9054 0.4458 0.5428 0.7926 0.9968 0.0678 0.5637 0.8012 0.0658 0.4705
0.8787 2.1538 140 0.8756 0.4195 0.5140 0.7780 0.9986 0.0506 0.4927 0.7790 0.0495 0.4301
0.8757 2.4615 160 0.8501 0.4035 0.4967 0.7677 0.9994 0.0659 0.4247 0.7656 0.0645 0.3804
0.841 2.7692 180 0.8339 0.4199 0.5148 0.7799 0.9987 0.0283 0.5174 0.7791 0.0279 0.4528
0.8268 3.0769 200 0.8246 0.4358 0.5279 0.7844 0.9989 0.0809 0.5040 0.7826 0.0789 0.4460
0.8306 3.3846 220 0.8095 0.4034 0.4968 0.7690 0.9995 0.0461 0.4448 0.7653 0.0455 0.3995
0.826 3.6923 240 0.7928 0.4174 0.5078 0.7731 0.9997 0.0846 0.4391 0.7663 0.0826 0.4034
0.7873 4.0 260 0.7915 0.4150 0.5046 0.7713 0.9996 0.0842 0.4299 0.7616 0.0824 0.4009
0.8031 4.3077 280 0.7805 0.4022 0.4928 0.7648 0.9998 0.0826 0.3960 0.7557 0.0811 0.3699
0.7881 4.6154 300 0.7791 0.4352 0.5265 0.7848 0.9995 0.0659 0.5142 0.7808 0.0645 0.4604
0.7883 4.9231 320 0.7777 0.4137 0.5038 0.7714 0.9998 0.0748 0.4368 0.7626 0.0734 0.4050

Framework versions

  • Transformers 4.44.1
  • Pytorch 2.6.0+cpu
  • Datasets 2.21.0
  • Tokenizers 0.19.1