mario-semantic-1

karthik540/mario-semantic-1

mario-semantic-1 is a image segmentation model from karthik540. 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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At a glance

Task: Image Segmentation
Library: transformers
License: other
Model type: segformer
Access: Public
Created: Apr 26, 2024
Updated: Apr 27, 2024
SHA: c2b2ace1

Base models

nvidia/mit-b0

Model card

From the Hugging Face model README

mario-semantic-1

This model is a fine-tuned version of nvidia/mit-b0 on the Custom mario Dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.0721
Mean Iou: 0.0
Mean Accuracy: 0.0
Overall Accuracy: 0.0
Accuracy Unlabeled: nan
Accuracy Mario: 0.0
Accuracy Ground: 0.0
Accuracy Enemy: 0.0
Accuracy Bricks: 0.0
Accuracy Question: 0.0
Iou Unlabeled: 0.0
Iou Mario: 0.0
Iou Ground: 0.0
Iou Enemy: 0.0
Iou Bricks: 0.0
Iou Question: 0.0

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Mario	Accuracy Enemy	Accuracy Bricks	Accuracy Question	Iou Mario	Iou Enemy	Iou Bricks	Iou Question
1.1471	0.2222	10	1.3150	0.0054	0.0409	0.0429	nan	0.0587	0.0305	0.0481	0.0674	0.0141	0.0110	0.0010	0.0063
1.0399	0.4444	20	1.1597	0.0042	0.0247	0.0335	nan	0.0687	0.0054	0.0098	0.0397	0.0136	0.0029	0.0005	0.0081
0.8368	0.6667	30	0.9484	0.0018	0.0052	0.0054	nan	0.0024	0.0098	0.0018	0.0121	0.0012	0.0049	0.0002	0.0046
0.9264	0.8889	40	0.7115	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.7753	1.1111	50	0.7572	0.0010	0.0023	0.0038	nan	0.0	0.0113	0.0	0.0	0.0	0.0062	0.0	0.0
0.6295	1.3333	60	0.5617	0.0001	0.0002	0.0003	nan	0.0	0.0009	0.0	0.0	0.0	0.0009	0.0	0.0
0.5956	1.5556	70	0.4135	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.5756	1.7778	80	0.2028	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.5318	2.0	90	0.1185	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.5351	2.2222	100	0.3064	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.5706	2.4444	110	0.1378	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.4863	2.6667	120	0.1121	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.3226	2.8889	130	0.2038	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.4139	3.1111	140	0.1520	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.3983	3.3333	150	0.1070	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.3672	3.5556	160	0.1282	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.3324	3.7778	170	0.1075	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.2806	4.0	180	0.2677	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.2854	4.2222	190	0.1020	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.3463	4.4444	200	0.0551	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1957	4.6667	210	0.1982	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.3063	4.8889	220	0.0962	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1933	5.1111	230	0.1172	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1833	5.3333	240	0.0600	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.231	5.5556	250	0.0519	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1516	5.7778	260	0.0575	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.172	6.0	270	0.1182	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1307	6.2222	280	0.0989	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1454	6.4444	290	0.1045	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1319	6.6667	300	0.0793	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1154	6.8889	310	0.0567	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1241	7.1111	320	0.0562	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1379	7.3333	330	0.0700	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1183	7.5556	340	0.0616	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.108	7.7778	350	0.0823	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1204	8.0	360	0.0661	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1391	8.2222	370	0.0578	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1554	8.4444	380	0.0643	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1338	8.6667	390	0.0822	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1358	8.8889	400	0.0997	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1704	9.1111	410	0.0503	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1242	9.3333	420	0.0692	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.1153	9.5556	430	0.1003	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0999	9.7778	440	0.0909	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0968	10.0	450	0.0721	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

Framework versions

Transformers 4.40.1
Pytorch 2.3.0
Datasets 2.19.0
Tokenizers 0.19.1

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Apr 26, 2024
Updated: Apr 27, 2024