autocrop-music

gkberg/autocrop-music

autocrop-music is a image segmentation model from gkberg. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.

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

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Hugging Face

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At a glance

Task: Image Segmentation
Library: transformers
License: other
Model type: segformer
Access: Public
Created: May 21, 2026
Updated: May 21, 2026
SHA: dcb6c75f

Base models

nvidia/mit-b0

Datasets

generator

Model card

From the Hugging Face model README

autocrop-music

This model is a fine-tuned version of nvidia/mit-b0 on the /home/nbspark/treningsdata/music dataset. It achieves the following results on the evaluation set:

Loss: 0.0130
Mean Iou: 0.4907
Mean Accuracy: 0.9814
Overall Accuracy: 0.9814
Accuracy Background: nan
Accuracy Crop: 0.9814
Iou Background: 0.0
Iou Crop: 0.9814

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 0.1
num_epochs: 50.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Crop	Iou Crop
No log	1.0	75	0.4329	0.4821	0.9642	0.9642	nan	0.9642	0.9642
No log	2.0	150	0.1566	0.4787	0.9574	0.9574	nan	0.9574	0.9574
No log	3.0	225	0.1025	0.4753	0.9505	0.9505	nan	0.9505	0.9505
No log	4.0	300	0.0740	0.4699	0.9397	0.9397	nan	0.9397	0.9397
No log	5.0	375	0.0588	0.4799	0.9597	0.9597	nan	0.9597	0.9597
No log	6.0	450	0.0456	0.4835	0.9669	0.9669	nan	0.9669	0.9669
0.2228	7.0	525	0.0346	0.4847	0.9694	0.9694	nan	0.9694	0.9694
0.2228	8.0	600	0.0290	0.4795	0.9591	0.9591	nan	0.9591	0.9591
0.2228	9.0	675	0.0286	0.4877	0.9754	0.9754	nan	0.9754	0.9754
0.2228	10.0	750	0.0245	0.4866	0.9733	0.9733	nan	0.9733	0.9733
0.2228	11.0	825	0.0225	0.4891	0.9782	0.9782	nan	0.9782	0.9782
0.2228	12.0	900	0.0313	0.4958	0.9916	0.9916	nan	0.9916	0.9916
0.2228	13.0	975	0.0212	0.4924	0.9848	0.9848	nan	0.9848	0.9848
0.0386	14.0	1050	0.0207	0.4913	0.9826	0.9826	nan	0.9826	0.9826
0.0386	15.0	1125	0.0182	0.4924	0.9848	0.9848	nan	0.9848	0.9848
0.0386	16.0	1200	0.0210	0.4945	0.9890	0.9890	nan	0.9890	0.9890
0.0386	17.0	1275	0.0200	0.4814	0.9628	0.9628	nan	0.9628	0.9628
0.0386	18.0	1350	0.0174	0.4873	0.9745	0.9745	nan	0.9745	0.9745
0.0386	19.0	1425	0.0178	0.4910	0.9820	0.9820	nan	0.9820	0.9820
0.0232	20.0	1500	0.0169	0.4923	0.9846	0.9846	nan	0.9846	0.9846
0.0232	21.0	1575	0.0157	0.4904	0.9807	0.9807	nan	0.9807	0.9807
0.0232	22.0	1650	0.0162	0.4921	0.9841	0.9841	nan	0.9841	0.9841
0.0232	23.0	1725	0.0152	0.4892	0.9785	0.9785	nan	0.9785	0.9785
0.0232	24.0	1800	0.0150	0.4905	0.9810	0.9810	nan	0.9810	0.9810
0.0232	25.0	1875	0.0153	0.4918	0.9836	0.9836	nan	0.9836	0.9836
0.0232	26.0	1950	0.0151	0.4877	0.9755	0.9755	nan	0.9755	0.9755
0.0183	27.0	2025	0.0144	0.4901	0.9802	0.9802	nan	0.9802	0.9802
0.0183	28.0	2100	0.0144	0.4888	0.9777	0.9777	nan	0.9777	0.9777
0.0183	29.0	2175	0.0146	0.4922	0.9843	0.9843	nan	0.9843	0.9843
0.0183	30.0	2250	0.0140	0.4907	0.9813	0.9813	nan	0.9813	0.9813
0.0183	31.0	2325	0.0141	0.4936	0.9872	0.9872	nan	0.9872	0.9872
0.0183	32.0	2400	0.0136	0.4913	0.9826	0.9826	nan	0.9826	0.9826
0.0183	33.0	2475	0.0135	0.4920	0.9840	0.9840	nan	0.9840	0.9840
0.0158	34.0	2550	0.0137	0.4928	0.9856	0.9856	nan	0.9856	0.9856
0.0158	35.0	2625	0.0136	0.4926	0.9852	0.9852	nan	0.9852	0.9852
0.0158	36.0	2700	0.0140	0.4929	0.9859	0.9859	nan	0.9859	0.9859
0.0158	37.0	2775	0.0131	0.4920	0.9841	0.9841	nan	0.9841	0.9841
0.0158	38.0	2850	0.0132	0.4916	0.9832	0.9832	nan	0.9832	0.9832
0.0158	39.0	2925	0.0134	0.4920	0.9840	0.9840	nan	0.9840	0.9840
0.0142	40.0	3000	0.0131	0.4925	0.9849	0.9849	nan	0.9849	0.9849
0.0142	41.0	3075	0.0131	0.4926	0.9853	0.9853	nan	0.9853	0.9853
0.0142	42.0	3150	0.0132	0.4920	0.9840	0.9840	nan	0.9840	0.9840
0.0142	43.0	3225	0.0132	0.4923	0.9847	0.9847	nan	0.9847	0.9847
0.0142	44.0	3300	0.0132	0.4924	0.9847	0.9847	nan	0.9847	0.9847
0.0142	45.0	3375	0.0132	0.4921	0.9841	0.9841	nan	0.9841	0.9841
0.0142	46.0	3450	0.0132	0.4921	0.9842	0.9842	nan	0.9842	0.9842
0.0141	47.0	3525	0.0132	0.4921	0.9843	0.9843	nan	0.9843	0.9843
0.0141	48.0	3600	0.0132	0.4920	0.9840	0.9840	nan	0.9840	0.9840
0.0141	49.0	3675	0.0131	0.4922	0.9844	0.9844	nan	0.9844	0.9844
0.0141	50.0	3750	0.0130	0.4907	0.9814	0.9814	nan	0.9814	0.9814

Framework versions

Transformers 5.8.0
Pytorch 2.11.0+cu130
Datasets 4.8.5
Tokenizers 0.22.2

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: May 21, 2026
Updated: May 21, 2026