G17-AMFU-Net-Paper

aee4/G17-AMFU-Net-Paper

G17-AMFU-Net-Paper is a machine learning model from aee4. 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: amfu-net
Access: Public
Created: Aug 8, 2026
Updated: Aug 8, 2026
SHA: 846e9307

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aee4/G17-AMFU-Net-Demo

Model card

From the Hugging Face model README

G17-AMFU-Net-Paper

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

Loss: 0.3496
Dice: 0.7909
Jaccard: 0.6967
Hd95: 30.0976
Asd: 9.8865

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: 0.001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 3
total_train_batch_size: 12
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: linear
num_epochs: 200

Training results

Training Loss	Epoch	Step	Asd	Dice	Hd95	Jaccard	Validation Loss
0.7389	1.4124	250	38.5936	0.5238	104.3756	0.3899	0.8055
0.6071	2.8249	500	24.4305	0.6501	70.5583	0.5265	0.5725
0.5341	4.2373	750	16.9775	0.6619	50.3622	0.5438	0.5378
0.5531	5.6497	1000	0.5912	0.6367	0.5040	76.2560	25.6322
0.5457	7.0621	1250	0.5005	0.6828	0.5698	46.2656	17.3494
0.4978	8.4746	1500	0.4582	0.7193	0.6052	46.9904	15.0216
0.4514	9.8870	1750	0.4697	0.7175	0.6031	50.9456	16.4252
0.4297	11.2994	2000	0.4159	0.7442	0.6375	38.9589	13.1164
0.4224	12.7119	2250	0.4034	0.7557	0.6502	38.6538	12.3197
0.4307	14.1243	2500	0.4182	0.7483	0.6391	44.3559	14.5023
0.401	15.5367	2750	0.3990	0.7608	0.6590	35.7307	11.9326
0.3966	16.9492	3000	0.4132	0.7471	0.6398	42.3880	13.9349
0.3871	18.3616	3250	0.3921	0.7636	0.6626	38.3225	12.2851
0.4202	19.7740	3500	0.3947	0.7566	0.6482	33.8341	11.3610
0.3247	21.1864	3750	0.3750	0.7749	0.6727	37.9185	11.9961
0.3342	22.5989	4000	0.3918	0.7662	0.6614	42.2135	13.5831
0.3555	24.0113	4250	0.3778	0.7720	0.6706	34.3589	11.6043
0.3318	25.4237	4500	0.3750	0.7721	0.6721	35.0297	11.3211
0.3786	26.8362	4750	0.3774	0.7727	0.6683	41.1416	13.0270
0.3009	28.2486	5000	0.3757	0.7707	0.6711	32.5325	10.9100
0.3123	29.6610	5250	0.3699	0.7770	0.6795	30.7301	10.7341
0.3086	31.0734	5500	0.3578	0.7834	0.6884	32.8322	10.5589
0.2918	32.4859	5750	0.3732	0.7759	0.6796	30.2249	10.4415
0.3211	33.8983	6000	0.3672	0.7802	0.6846	30.9604	10.4802
0.3372	35.3107	6250	0.3806	0.7736	0.6771	29.8470	10.2667
0.3022	36.7232	6500	0.3560	0.7852	0.6885	31.3705	10.2234
0.3051	38.1356	6750	0.3623	0.7830	0.6873	29.4657	9.9103
0.3055	39.5480	7000	0.3618	0.7849	0.6893	30.2915	9.9328
0.2786	40.9605	7250	0.3540	0.7877	0.6919	31.9463	10.3530
0.2705	42.3729	7500	0.3483	0.7904	0.6945	32.1582	10.4397
0.271	43.7853	7750	0.3585	0.7853	0.6899	30.0640	10.2347
0.2849	45.1977	8000	0.3590	0.7830	0.6882	30.3822	10.2435
0.2648	46.6102	8250	0.3501	0.7900	0.6932	32.2145	10.8766
0.264	48.0226	8500	0.3549	0.7871	0.6924	30.8388	10.1933
0.2883	49.4350	8750	0.3474	0.7897	0.6953	31.7965	10.2905
0.2694	50.8475	9000	0.3584	0.7861	0.6916	30.0670	10.1097
0.2532	52.2599	9250	0.3581	0.7856	0.6908	31.0802	10.1921
0.2829	53.6723	9500	0.3476	0.7903	0.6947	31.9132	10.5729
0.2888	55.0847	9750	0.3495	0.7893	0.6948	31.8618	10.2558
0.2699	56.4972	10000	0.3457	0.7908	0.6957	32.0846	10.4567
0.2513	57.9096	10250	0.3486	0.7899	0.6962	30.5884	10.1494
0.2661	59.3220	10500	0.3489	0.7895	0.6950	31.5616	10.4309
0.2732	60.7345	10750	0.3543	0.7883	0.6942	29.5717	9.9337
0.2829	62.1469	11000	0.3455	0.7911	0.6971	31.0813	10.2404
0.2938	63.5593	11250	0.3471	0.7915	0.6971	30.4424	10.0673
0.2606	64.9718	11500	0.3464	0.7905	0.6955	31.9298	10.6125
0.2646	66.3842	11750	0.3491	0.7913	0.6969	30.2795	9.8441
0.2516	67.7966	12000	0.3468	0.7915	0.6975	29.8304	9.7218
0.2869	69.2090	12250	0.3517	0.7904	0.6960	31.0597	10.0999
0.261	70.6215	12500	0.3495	0.7907	0.6966	30.4694	9.9219
0.2554	72.0339	12750	0.3496	0.7912	0.6969	30.9471	10.1219
0.248	73.4463	13000	0.3482	0.7904	0.6956	32.2843	10.6262
0.2791	74.8588	13250	0.3448	0.7921	0.6980	30.3742	10.0979
0.2459	76.2712	13500	0.3470	0.7907	0.6962	31.0974	10.2727
0.2913	77.6836	13750	0.3507	0.7907	0.6964	30.3586	9.9545
0.2657	79.0960	14000	0.3481	0.7909	0.6963	30.8855	10.1784
0.248	80.5085	14250	0.3456	0.7917	0.6976	30.0486	10.0548
0.2583	81.9209	14500	0.3477	0.7913	0.6973	29.2632	9.8539
0.2655	83.3333	14750	0.3488	0.7919	0.6982	30.1899	9.7378
0.2671	84.7458	15000	0.3476	0.7921	0.6982	30.3738	9.8575
0.2744	86.1582	15250	0.3477	0.7921	0.6978	31.0608	10.1258
0.2582	87.5706	15500	0.3495	0.7906	0.6967	30.1395	9.8876
0.2635	88.9831	15750	0.3530	0.7893	0.6952	30.0033	9.7234
0.2886	90.3955	16000	0.3507	0.7920	0.6985	30.3988	9.8475
0.2737	91.8079	16250	0.3489	0.7908	0.6966	30.4467	9.8894
0.2593	93.2203	16500	0.3462	0.7911	0.6966	31.7076	10.3841
0.2541	94.6328	16750	0.3479	0.7919	0.6981	30.6190	9.9648
0.2549	96.0452	17000	0.3496	0.7913	0.6974	30.9751	9.9748
0.2485	97.4576	17250	0.3451	0.7925	0.6983	31.2473	10.1220
0.2485	98.8701	17500	0.3468	0.7916	0.6972	31.2395	10.1335
0.2618	100.2825	17750	0.3497	0.7908	0.6971	30.0951	9.7453
0.2593	101.6949	18000	0.3467	0.7919	0.6980	30.1628	9.9642
0.2719	103.1073	18250	0.3479	0.7928	0.6994	29.8435	9.7739
0.2667	104.5198	18500	0.3443	0.7932	0.6993	31.3466	10.1526
0.2513	105.9322	18750	0.3466	0.7920	0.6980	31.0591	10.2166
0.2622	107.3446	19000	0.3509	0.7907	0.6968	30.0248	9.9479
0.2555	108.7571	19250	0.3457	0.7918	0.6975	31.1739	10.2218
0.2679	110.1695	19500	0.3511	0.7900	0.6956	30.8967	10.0636
0.2432	111.5819	19750	0.3492	0.7905	0.6963	30.6068	10.1251
0.2811	112.9944	20000	0.3519	0.7897	0.6959	29.6713	9.8362
0.2376	114.4068	20250	0.3506	0.7913	0.6976	30.1932	9.8955
0.2575	115.8192	20500	0.3479	0.7909	0.6971	30.4310	10.0271
0.2473	117.2316	20750	0.3498	0.7912	0.6977	29.4755	9.7462
0.2502	118.6441	21000	0.3465	0.7916	0.6970	31.3217	10.3354
0.2532	120.0565	21250	0.3481	0.7915	0.6975	31.2661	10.0386
0.2469	121.4689	21500	0.3496	0.7910	0.6973	30.8048	10.0485
0.2542	122.8814	21750	0.3473	0.7918	0.6974	30.8065	10.1343
0.2459	124.2938	22000	0.3473	0.7910	0.6970	30.5939	10.0567
0.2743	125.7062	22250	0.3489	0.7910	0.6967	30.4530	10.0447
0.2959	127.1186	22500	0.3469	0.7915	0.6975	30.3645	9.9545
0.2602	128.5311	22750	0.3458	0.7913	0.6970	31.1398	10.1557
0.2557	129.9435	23000	0.3510	0.7917	0.6977	30.0252	9.7831
0.2627	131.3559	23250	0.3487	0.7905	0.6968	30.0623	9.8507
0.2538	132.7684	23500	0.3472	0.7925	0.6989	29.7250	9.8968
0.25	134.1808	23750	0.3463	0.7922	0.6980	30.4820	10.0544
0.239	135.5932	24000	0.3492	0.7911	0.6976	29.9095	9.8149
0.2695	137.0056	24250	0.3447	0.7922	0.6976	31.3291	10.3158
0.276	138.4181	24500	0.3468	0.7927	0.6989	30.6967	9.9231
0.265	139.8305	24750	0.3464	0.7915	0.6981	29.3216	9.7071
0.2421	141.2429	25000	0.3455	0.7919	0.6978	31.1443	10.1200
0.245	142.6554	25250	0.3502	0.7907	0.6967	30.5402	9.9200
0.2644	144.0678	25500	0.3454	0.7924	0.6984	30.6106	10.0256
0.2642	145.4802	25750	0.3494	0.7913	0.6974	30.3045	9.7284
0.2736	146.8927	26000	0.3531	0.7903	0.6967	30.0741	9.7293
0.2837	148.3051	26250	0.3506	0.7919	0.6977	30.3323	10.0468
0.2669	149.7175	26500	0.3478	0.7913	0.6976	29.8362	9.7996
0.2389	151.1299	26750	0.3477	0.7906	0.6964	30.9778	10.1554
0.2433	152.5424	27000	0.3444	0.7923	0.6982	30.5612	9.9517
0.2507	153.9548	27250	0.3498	0.7911	0.6971	30.5388	10.0399
0.3	155.3672	27500	0.3528	0.7898	0.6961	30.0155	9.8287
0.2444	156.7797	27750	0.3469	0.7913	0.6969	31.5767	10.2693
0.2693	158.1921	28000	0.3482	0.7915	0.6975	30.7161	9.9847
0.2576	159.6045	28250	0.3469	0.7920	0.6981	31.5987	10.1164
0.2452	161.0169	28500	0.3500	0.7904	0.6961	31.2783	10.1386
0.256	162.4294	28750	0.3487	0.7913	0.6977	29.3927	9.7377
0.2609	163.8418	29000	0.3488	0.7912	0.6972	30.7578	10.0702
0.2493	165.2542	29250	0.3476	0.7908	0.6966	31.0479	10.1655
0.2611	166.6667	29500	0.3483	0.7918	0.6981	30.5419	9.9485
0.258	168.0791	29750	0.3473	0.7910	0.6969	30.9598	10.1742
0.2756	169.4915	30000	0.3479	0.7911	0.6970	30.4461	9.9733
0.26	170.9040	30250	0.3493	0.7915	0.6975	30.7870	10.0493
0.2716	172.3164	30500	0.3487	0.7913	0.6975	30.8341	9.8859
0.2515	173.7288	30750	0.3461	0.7924	0.6983	31.2209	10.1903
0.2546	175.1412	31000	0.3483	0.7919	0.6979	30.4488	9.9073
0.2542	176.5537	31250	0.3483	0.7906	0.6966	30.2269	9.9971
0.2574	177.9661	31500	0.3467	0.7924	0.6987	30.3149	9.9697
0.2531	179.3785	31750	0.3460	0.7920	0.6976	30.8770	10.1790
0.2677	180.7910	32000	0.3485	0.7916	0.6977	30.2612	9.8976
0.2744	182.2034	32250	0.3506	0.7913	0.6972	30.8237	10.1292
0.2568	183.6158	32500	0.3510	0.7908	0.6968	30.0119	9.7927
0.2414	185.0282	32750	0.3456	0.7912	0.6969	30.5987	9.9543
0.2833	186.4407	33000	0.3494	0.7913	0.6980	28.8245	9.7017
0.2437	187.8531	33250	0.3476	0.7923	0.6983	31.3336	10.1063
0.2417	189.2655	33500	0.3498	0.7906	0.6966	30.9630	10.0274
0.2562	190.6780	33750	0.3472	0.7919	0.6979	30.7585	9.9717
0.2689	192.0904	34000	0.3441	0.7924	0.6979	31.0385	10.1692
0.2865	193.5028	34250	0.3505	0.7903	0.6964	29.7483	9.8632
0.2514	194.9153	34500	0.3472	0.7916	0.6978	30.6949	9.9969
0.25	196.3277	34750	0.3497	0.7917	0.6981	30.8708	9.9481
0.2588	197.7401	35000	0.3506	0.7907	0.6964	31.0115	9.9669
0.2602	199.1525	35250	0.3496	0.7909	0.6967	30.0976	9.8865

Framework versions

Transformers 4.57.1
Pytorch 2.10.0+cu128
Datasets 4.8.5
Tokenizers 0.22.2

Library: transformers
Type: amfu-net
Created: Aug 8, 2026
Updated: Aug 8, 2026