01_06_2026_multilabel_pipeline_version_run

aholk/01_06_2026_multilabel_pipeline_version_run

01_06_2026_multilabel_pipeline_version_run is a machine learning model from aholk. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
Model type: Unet
Access: Public
Created: Jan 13, 2026
Updated: Jan 13, 2026
SHA: eb493f73

Model card

From the Hugging Face model README

01_06_2026_multilabel_pipeline_version_run

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

Model Preparation Time: 0.001
Accuracy: 0.7473
F1: 0.0115
Iou: 0.0059
Per Class Metrics: {0: {'f1': 0.00054, 'iou': 0.00027, 'accuracy': 0.98717}, 1: {'f1': 0.0044, 'iou': 0.0022, 'accuracy': 0.93948}, 2: {'f1': 0.04082, 'iou': 0.02084, 'accuracy': 0.27112}, 3: {'f1': 0.0002, 'iou': 0.0001, 'accuracy': 0.79159}}
Loss: 3.8334

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
num_epochs: 0.02

Training results

Training Loss	Epoch	Step	Model Preparation Time		Class Metrics	Validation Loss
No log	0.0020	1	0.001	0.0059	{0: {'f1': 0.00054, 'iou': 0.00027, 'accuracy': 0.98717}, 1: {'f1': 0.0044, 'iou': 0.0022, 'accuracy': 0.93948}, 2: {'f1': 0.04082, 'iou': 0.02084, 'accuracy': 0.27112}, 3: {'f1': 0.0002, 'iou': 0.0001, 'accuracy': 0.79159}}	3.8334
No log	0.0040	2	0.001	0.0068	{0: {'f1': 0.00077, 'iou': 0.00038, 'accuracy': 0.9871}, 1: {'f1': 0.00927, 'iou': 0.00465, 'accuracy': 0.93936}, 2: {'f1': 0.04319, 'iou': 0.02207, 'accuracy': 0.27015}, 3: {'f1': 0.00035, 'iou': 0.00018, 'accuracy': 0.78979}}	3.8348
No log	0.0060	3	0.001	0.0062	{0: {'f1': 0.00263, 'iou': 0.00132, 'accuracy': 0.98681}, 1: {'f1': 0.00116, 'iou': 0.00058, 'accuracy': 0.93957}, 2: {'f1': 0.04461, 'iou': 0.02281, 'accuracy': 0.26424}, 3: {'f1': 4e-05, 'iou': 2e-05, 'accuracy': 0.7952}}	3.8417
No log	0.0080	4	0.001	0.0067	{0: {'f1': 0.00513, 'iou': 0.00257, 'accuracy': 0.9865}, 1: {'f1': 0.00205, 'iou': 0.00103, 'accuracy': 0.93957}, 2: {'f1': 0.045, 'iou': 0.02302, 'accuracy': 0.26854}, 3: {'f1': 5e-05, 'iou': 2e-05, 'accuracy': 0.79513}}	3.8442

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 2.21.0
Tokenizers 0.21.4

Library: transformers
Type: Unet
Created: Jan 13, 2026
Updated: Jan 13, 2026