finetune-instance-segmentation-mini-mask2former_augmentation_default

yeray142/finetune-instance-segmentation-mini-mask2former_augmentation_default

finetune-instance-segmentation-mini-mask2former_augmentation_default is a image segmentation model from yeray142. 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. --

Downloads · 30 days

8% of all-time downloads

All-time downloads

139

Public

Parameters

47.4M

1.9 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors190 MB · 100%

Parameter types

How the weights are stored.

F3247.4M · 100%

Base models

facebook/mask2former-swin-tiny-coco-instance

Model card

From the Hugging Face model README

finetune-instance-segmentation-mini-mask2former_augmentation_default

This model is a fine-tuned version of facebook/mask2former-swin-tiny-coco-instance on the jsalavedra/strawberry_disease dataset. It achieves the following results on the evaluation set:

Loss: 16.2566
Map: 0.6229
Map 50: 0.8172
Map 75: 0.6824
Map Small: 0.35
Map Medium: 0.401
Map Large: 0.7146
Mar 1: 0.4279
Mar 10: 0.7886
Mar 100: 0.831
Mar Small: 0.5
Mar Medium: 0.7108
Mar Large: 0.8802
Map Angular leafspot: 0.54
Mar 100 Angular leafspot: 0.8135
Map Anthracnose fruit rot: 0.405
Mar 100 Anthracnose fruit rot: 0.7118
Map Blossom blight: 0.7372
Mar 100 Blossom blight: 0.8159
Map Gray mold: 0.5767
Mar 100 Gray mold: 0.7648
Map Leaf spot: 0.8783
Mar 100 Leaf spot: 0.9416
Map Powdery mildew fruit: 0.5019
Mar 100 Powdery mildew fruit: 0.8833
Map Powdery mildew leaf: 0.7209
Mar 100 Powdery mildew leaf: 0.8863

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
num_epochs: 10.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Small	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Small	Mar Medium	Mar Large	Map Angular leafspot	Mar 100 Angular leafspot	Map Anthracnose fruit rot	Mar 100 Anthracnose fruit rot	Map Blossom blight	Mar 100 Blossom blight	Map Gray mold	Mar 100 Gray mold	Map Leaf spot	Mar 100 Leaf spot	Map Powdery mildew fruit	Mar 100 Powdery mildew fruit	Map Powdery mildew leaf	Mar 100 Powdery mildew leaf
48.1383	1.0	91	32.7441	0.072	0.0944	0.0789	0.0008	0.028	0.1218	0.1444	0.2815	0.3583	0.2	0.1977	0.4277	0.0097	0.4962	0.001	0.0529	0.0025	0.0727	0.0527	0.2222	0.1818	0.8401	0.0002	0.05	0.2557	0.7737
28.1321	2.0	182	26.8257	0.2203	0.2891	0.2395	0.1267	0.1186	0.2617	0.2987	0.5351	0.5913	0.4	0.3726	0.6649	0.0203	0.5981	0.0049	0.1706	0.1769	0.65	0.1961	0.6324	0.6355	0.9082	0.0087	0.3444	0.4998	0.8353
23.6192	3.0	273	23.0934	0.3141	0.408	0.3423	0.2133	0.195	0.3834	0.3621	0.6516	0.6971	0.4	0.4291	0.7797	0.0457	0.7288	0.0175	0.4059	0.4873	0.7205	0.2984	0.7102	0.7575	0.9249	0.0257	0.5444	0.5666	0.8451
20.6054	4.0	364	21.1837	0.398	0.5303	0.434	0.2115	0.237	0.4998	0.3852	0.7062	0.7568	0.5	0.5624	0.8306	0.264	0.7365	0.0368	0.5882	0.5386	0.75	0.434	0.7287	0.7965	0.9331	0.107	0.7056	0.6088	0.8557
19.4288	5.0	455	19.9111	0.4366	0.5924	0.4686	0.1758	0.256	0.5269	0.4005	0.7295	0.7887	0.4	0.5917	0.8558	0.3157	0.7788	0.0679	0.6471	0.5715	0.7636	0.4758	0.7389	0.8107	0.9315	0.1825	0.8	0.632	0.8608
17.8867	6.0	546	18.9302	0.5064	0.6834	0.5457	0.35	0.3232	0.5693	0.395	0.7462	0.8006	0.45	0.6427	0.8583	0.3472	0.7731	0.1802	0.7	0.6516	0.7909	0.5193	0.7398	0.8209	0.9362	0.3584	0.8	0.6671	0.8639
16.9985	7.0	637	18.2692	0.5458	0.7266	0.6036	0.2667	0.353	0.6138	0.4145	0.7701	0.8142	0.4	0.6488	0.876	0.4325	0.775	0.2601	0.7118	0.7072	0.8159	0.5295	0.7472	0.8566	0.9424	0.3656	0.8333	0.6692	0.8737
16.1493	8.0	728	17.3118	0.5707	0.7557	0.6298	0.4036	0.3684	0.6464	0.4175	0.7831	0.8275	0.45	0.6815	0.8822	0.4954	0.7923	0.3283	0.7588	0.7096	0.8159	0.5474	0.7528	0.8717	0.9436	0.3381	0.8444	0.7045	0.8847
15.3879	9.0	819	16.7216	0.5907	0.7634	0.6611	0.35	0.3824	0.6636	0.4145	0.7808	0.8258	0.45	0.6663	0.8821	0.5305	0.8019	0.3748	0.7529	0.7337	0.8136	0.5658	0.7574	0.8738	0.9377	0.3421	0.8333	0.7144	0.8835
14.4614	10.0	910	16.2566	0.6229	0.8172	0.6824	0.35	0.401	0.7146	0.4279	0.7886	0.831	0.5	0.7108	0.8802	0.54	0.8135	0.405	0.7118	0.7372	0.8159	0.5767	0.7648	0.8783	0.9416	0.5019	0.8833	0.7209	0.8863

Framework versions

Transformers 4.50.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.0

Task: Image Segmentation
Library: transformers
Type: mask2former
License: other
Created: Mar 16, 2025
Updated: Mar 16, 2025