mit-b0-Image_segmentation_Dominoes_v2

DunnBC22/mit-b0-Image_segmentation_Dominoes_v2

mit-b0-Image_segmentation_Dominoes_v2 is a image segmentation model from DunnBC22. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

This model is a fine-tuned version of nvidia/mit-b0.

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Updated Apr 3, 2026

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421 MB

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At a glance

Task: Image Segmentation
License: apache-2.0
Access: Public
Created: Jul 26, 2023
Updated: Apr 3, 2026
SHA: 5f7220f0

Datasets

adelavega/dominoes_raw

Model card

From the Hugging Face model README

mit-b0-Image_segmentation_Dominoes_v2

This model is a fine-tuned version of nvidia/mit-b0.

It achieves the following results on the evaluation set:

Loss: 0.1149
Mean Iou: 0.9198
Mean Accuracy: 0.9515
Overall Accuracy: 0.9778
Per Category Iou:
- Segment 0: 0.974110559111975
- Segment 1: 0.8655745252092782
Per Category Accuracy
- Segment 0: 0.9897833441005461
- Segment 1: 0.913253525550903

Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Segmentation/Dominoes/Fine-Tuning%20-%20Dominoes%20-%20Image%20Segmentation%20with%20LoRA.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://huggingface.co/datasets/adelavega/dominoes_raw

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou Segment 0	Per Category Iou Segment 1	Per Category Accuracy Segment 0	Per Category Accuracy Segment 1
0.0461	1.0	86	0.1233	0.9150	0.9527	0.9762	0.9721967854031923	0.8578619172251059	0.9869082633464498	0.9184139264010376
0.0708	2.0	172	0.1366	0.9172	0.9490	0.9771	0.9732821853093164	0.8611008788165083	0.9898473600751747	0.9082362492748777
0.048	3.0	258	0.1260	0.9199	0.9534	0.9777	0.9740118174014271	0.8658241844233872	0.9888392553004053	0.9179240730467295
0.0535	4.0	344	0.1184	0.9200	0.9520	0.9778	0.974142444792198	0.8658711064023369	0.9896291184589182	0.9142864290038782
0.0185	5.0	430	0.1296	0.9182	0.9477	0.9775	0.9737715695013129	0.8627108292167807	0.9910418746696423	0.904378218719681
0.036	6.0	516	0.1410	0.9213	0.9538	0.9782	0.9745002408443008	0.8680673581922554	0.9892677512186527	0.9182967669045321
0.0376	7.0	602	0.1451	0.9206	0.9550	0.9779	0.9741455743906073	0.8669703237367214	0.9883004639689904	0.9216576612178001
0.0186	8.0	688	0.1380	0.9175	0.9496	0.9772	0.9733616852468584	0.8616466350192237	0.9897043519116697	0.9094762400541087
0.0162	9.0	774	0.1459	0.9218	0.9539	0.9783	0.9746840649852051	0.8688930149000804	0.989455276913138	0.9182917005479264
0.0169	10.0	860	0.1467	0.9191	0.9502	0.9776	0.9739086600912814	0.8642187978193332	0.9901195747929759	0.9102564589713776
0.0102	11.0	946	0.1549	0.9191	0.9524	0.9775	0.9737696499931041	0.8644247331609153	0.9889789745698009	0.915789237032027
0.0204	12.0	1032	0.1502	0.9215	0.9527	0.9783	0.974639596078376	0.8682964916021273	0.989902977623774	0.9155653673995151
0.0268	13.0	1118	0.1413	0.9194	0.9505	0.9777	0.9740020531855834	0.8647199376136	0.99011699066189	0.9107963425971664
0.0166	14.0	1204	0.1584	0.9173	0.9518	0.9770	0.9731154475737929	0.8614276032542578	0.9884142831972749	0.9152366875147241
0.0159	15.0	1290	0.1563	0.9170	0.9492	0.9770	0.9731832402253996	0.8607442858381036	0.9896456803899689	0.9087960816798012
0.0211	16.0	1376	0.1435	0.9150	0.9481	0.9764	0.9725201360275898	0.8574847000491036	0.989323310037	0.9068449010920532
0.0128	17.0	1462	0.1421	0.9212	0.9519	0.9782	0.9745789801464504	0.8677394402794754	0.9901920479238856	0.9136255861141298
0.0167	18.0	1548	0.1558	0.9217	0.9532	0.9783	0.9746811993626879	0.8686470009484697	0.9897428202266988	0.9166850322093621
0.0201	19.0	1634	0.1623	0.9156	0.9484	0.9766	0.9727184720007118	0.8584339325695252	0.9894484642039114	0.9072695251050635
0.0133	20.0	1720	0.1573	0.9189	0.9505	0.9776	0.9738320500157303	0.8640203613069115	0.9898665061373113	0.9112263496140702
0.012	21.0	1806	0.1631	0.9165	0.9472	0.9769	0.9731344243001482	0.8597866189796295	0.9904592118400188	0.9040137576913626
0.0148	22.0	1892	0.1629	0.9181	0.9507	0.9773	0.9735162429121835	0.8627239955489192	0.9894034768309156	0.9120129014770962
0.0137	23.0	1978	0.1701	0.9136	0.9484	0.9760	0.9719681843338751	0.8552607882028388	0.9885083690609032	0.908250815050119
0.0142	24.0	2064	0.1646	0.9146	0.9488	0.9763	0.9723134197764093	0.8568918401744342	0.9887405884771245	0.9089100747034281
0.0156	25.0	2150	0.1615	0.9144	0.9465	0.9763	0.9723929259786395	0.856345354289624	0.9898487696012216	0.9032139066422469

Framework versions

Transformers 4.26.1
Pytorch 2.0.1
Datasets 2.13.1
Tokenizers 0.13.3

License Notice

This model is a fine-tuned derivative of a pretrained model. Users must comply with the original model license.

Dataset Notice

This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.

Task: Image Segmentation
License: apache-2.0
Languages: en
Created: Jul 26, 2023
Updated: Apr 3, 2026