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hungnguyen2k4/
rtdetr-r50-fruits-best-finetune

Name: rtdetr-r50-fruits-best-finetune
Availability: InStock

generated_from_trainer

hungnguyen2k4/rtdetr-r50-fruits-finetune

finetune

apache-2.0

endpoints_compatible

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rtdetr-r50-fruits-best-finetune

This model is a fine-tuned version of hungnguyen2k4/rtdetr-r50-fruits-finetune on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 9.5094
Map: 0.56
Map 50: 0.6937
Map 75: 0.5776
Map Small: 0.1949
Map Medium: 0.518
Map Large: 0.7408
Mar 1: 0.2823
Mar 10: 0.6067
Mar 100: 0.691
Mar Small: 0.3327
Mar Medium: 0.6582
Mar Large: 0.8621
Map Apple: 0.5256
Mar 100 Apple: 0.6642
Map Banana: 0.5721
Mar 100 Banana: 0.7055
Map Grapes: 0.4636
Mar 100 Grapes: 0.5741
Map Orange: 0.5234
Mar 100 Orange: 0.6241
Map Pineapple: 0.597
Mar 100 Pineapple: 0.763
Map Watermelon: 0.6779
Mar 100 Watermelon: 0.8151

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
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
lr_scheduler_warmup_steps: 300
num_epochs: 50

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 Apple	Mar 100 Apple	Map Banana	Mar 100 Banana	Map Grapes	Mar 100 Grapes	Map Orange	Mar 100 Orange	Map Pineapple	Mar 100 Pineapple	Map Watermelon	Mar 100 Watermelon
8.9802	1.0	750	10.2552	0.5102	0.6327	0.5309	0.1779	0.4483	0.689	0.2711	0.5995	0.703	0.3508	0.6674	0.8701	0.4596	0.6818	0.5085	0.7333	0.4386	0.5938	0.5156	0.6365	0.5746	0.7674	0.5644	0.8054
9.4467	2.0	1500	9.9237	0.5301	0.6554	0.5475	0.1926	0.46	0.7148	0.2795	0.6066	0.7102	0.3641	0.6813	0.8684	0.5167	0.7076	0.5584	0.7422	0.4379	0.5966	0.5111	0.6434	0.5873	0.7656	0.5689	0.806
9.4137	3.0	2250	9.7983	0.5399	0.6637	0.5658	0.1989	0.4795	0.7172	0.277	0.6177	0.7166	0.3701	0.6859	0.8776	0.5137	0.6993	0.5505	0.7333	0.4545	0.6039	0.5209	0.6495	0.5943	0.7982	0.6057	0.8153
9.3953	4.0	3000	10.1332	0.5239	0.6451	0.5464	0.1602	0.4736	0.7052	0.2795	0.6113	0.7101	0.3349	0.6799	0.8771	0.483	0.6899	0.5301	0.7407	0.4575	0.6071	0.5091	0.6463	0.5666	0.763	0.597	0.8136
9.3615	5.0	3750	9.6674	0.5535	0.684	0.5745	0.2124	0.5145	0.7318	0.2819	0.6182	0.7232	0.392	0.7008	0.8817	0.5201	0.6972	0.5629	0.7518	0.4598	0.6126	0.5249	0.6475	0.5963	0.7953	0.6568	0.8349
9.2355	6.0	4500	9.8959	0.5419	0.6695	0.5637	0.2062	0.4845	0.72	0.2814	0.6134	0.7238	0.4109	0.6932	0.8811	0.5195	0.7062	0.5637	0.7518	0.4669	0.6104	0.5125	0.6432	0.5412	0.7819	0.6475	0.8494
9.0371	7.0	5250	9.9381	0.5411	0.6638	0.5561	0.1564	0.5012	0.7211	0.2798	0.6099	0.71	0.3452	0.6837	0.8758	0.5034	0.6823	0.5189	0.7155	0.4583	0.5972	0.5099	0.6389	0.6053	0.7866	0.6511	0.8392
9.0275	8.0	6000	9.4068	0.5398	0.6662	0.5585	0.1673	0.4756	0.7306	0.283	0.6124	0.7073	0.3566	0.6734	0.8773	0.5251	0.6952	0.5451	0.7243	0.4453	0.5946	0.5288	0.6487	0.6112	0.7851	0.5831	0.796
8.9213	9.0	6750	9.7777	0.5365	0.6644	0.5586	0.2017	0.4922	0.7154	0.2792	0.6065	0.7042	0.3564	0.6719	0.8685	0.5124	0.693	0.5489	0.7299	0.458	0.5908	0.5016	0.6388	0.5693	0.7486	0.6287	0.8244
8.8997	10.0	7500	9.8940	0.5253	0.6512	0.5436	0.1611	0.4795	0.711	0.2787	0.5984	0.6913	0.3029	0.6626	0.867	0.5099	0.6751	0.5214	0.7237	0.4552	0.5793	0.5025	0.632	0.5652	0.7391	0.5974	0.7983
8.6043	11.0	8250	9.9437	0.5429	0.6668	0.5626	0.1574	0.496	0.7266	0.2815	0.6093	0.7029	0.3175	0.6831	0.871	0.5006	0.6691	0.5486	0.7322	0.4618	0.5832	0.4991	0.6352	0.5871	0.7699	0.6601	0.8276
8.5683	12.0	9000	9.9150	0.5297	0.6495	0.5497	0.1795	0.4559	0.7177	0.283	0.6039	0.6987	0.3532	0.6622	0.8686	0.4845	0.6731	0.5474	0.7369	0.4566	0.5907	0.4888	0.6336	0.6042	0.7576	0.597	0.8
8.5717	13.0	9750	9.8528	0.5399	0.6721	0.5596	0.1919	0.4855	0.7202	0.2803	0.6082	0.6992	0.3338	0.6755	0.8645	0.5136	0.6784	0.5658	0.7251	0.4306	0.577	0.4964	0.6307	0.6144	0.7638	0.6189	0.8202
8.4495	14.0	10500	9.6450	0.552	0.6804	0.5729	0.2078	0.5048	0.7302	0.2822	0.6094	0.6978	0.3447	0.671	0.8689	0.5153	0.6589	0.5541	0.7315	0.4536	0.5798	0.5092	0.6214	0.6172	0.7652	0.6627	0.8301
8.3451	15.0	11250	9.3464	0.5612	0.6917	0.5846	0.2077	0.5249	0.7405	0.2798	0.6196	0.7104	0.3597	0.6819	0.8831	0.5172	0.6707	0.5539	0.7371	0.4811	0.6018	0.5209	0.6351	0.6242	0.7873	0.6698	0.8301
8.274	16.0	12000	9.7117	0.5317	0.6597	0.5485	0.1601	0.4758	0.7181	0.2795	0.5988	0.6881	0.3128	0.643	0.8701	0.499	0.6631	0.5431	0.7179	0.4489	0.5687	0.5012	0.627	0.5745	0.758	0.6235	0.7937
8.2786	17.0	12750	9.6160	0.5449	0.6734	0.5609	0.2112	0.4758	0.7345	0.2825	0.6104	0.7053	0.3634	0.6656	0.8763	0.5067	0.6791	0.5535	0.7272	0.4737	0.6055	0.5028	0.6313	0.5987	0.7775	0.6341	0.8114
8.1322	18.0	13500	9.4629	0.5463	0.6727	0.5638	0.1406	0.491	0.7388	0.2819	0.6098	0.7012	0.3403	0.6612	0.8782	0.5256	0.6853	0.553	0.7193	0.4608	0.5844	0.518	0.6393	0.5899	0.7739	0.6305	0.8048
8.1142	19.0	14250	9.7109	0.5513	0.6773	0.5721	0.1757	0.5084	0.7315	0.2859	0.6141	0.7009	0.3256	0.6721	0.8728	0.5089	0.6733	0.5738	0.7228	0.469	0.5832	0.5088	0.6353	0.6133	0.7638	0.6341	0.827
7.9492	20.0	15000	9.9189	0.5301	0.6593	0.5472	0.1661	0.4793	0.7152	0.2803	0.6005	0.6884	0.3141	0.6485	0.8686	0.5253	0.669	0.5427	0.719	0.454	0.5739	0.5045	0.6245	0.5588	0.7565	0.5953	0.7875
7.8828	21.0	15750	9.8609	0.5335	0.6614	0.5534	0.1531	0.4849	0.7191	0.2791	0.5933	0.6788	0.3044	0.6432	0.8568	0.4837	0.6479	0.5335	0.6856	0.45	0.5723	0.5108	0.6283	0.6042	0.7239	0.6188	0.8148
7.8113	22.0	16500	9.4807	0.562	0.6908	0.5828	0.1816	0.518	0.7435	0.2823	0.6169	0.6996	0.3349	0.6746	0.8679	0.5353	0.6785	0.5716	0.7219	0.4681	0.5809	0.5133	0.6293	0.6179	0.7583	0.6658	0.8284
7.7437	23.0	17250	9.4869	0.5643	0.6894	0.585	0.2046	0.5224	0.7447	0.2857	0.6174	0.7043	0.3415	0.6756	0.8743	0.5123	0.6742	0.5776	0.715	0.472	0.5979	0.5175	0.6362	0.6285	0.7786	0.6782	0.8239
7.6365	24.0	18000	9.5132	0.5577	0.6914	0.5776	0.2049	0.5196	0.7336	0.2813	0.61	0.6979	0.3404	0.6751	0.8627	0.5153	0.6679	0.5712	0.7193	0.4746	0.6039	0.5113	0.6308	0.6164	0.7518	0.6575	0.8136
7.6691	25.0	18750	9.4612	0.5627	0.6933	0.5838	0.1642	0.5221	0.7429	0.2854	0.6154	0.7005	0.3194	0.6779	0.8702	0.5288	0.6743	0.5821	0.7281	0.4649	0.5865	0.5177	0.629	0.616	0.7659	0.6668	0.8193
7.5961	26.0	19500	9.5376	0.5548	0.6797	0.5772	0.1797	0.5019	0.7392	0.2828	0.6088	0.6932	0.3324	0.6589	0.8668	0.5221	0.6691	0.5579	0.7114	0.4579	0.5793	0.5136	0.6315	0.6299	0.7645	0.6476	0.8034
7.5074	27.0	20250	9.4459	0.5537	0.682	0.5735	0.1985	0.4972	0.74	0.2859	0.6099	0.7012	0.3442	0.6637	0.8741	0.5171	0.6738	0.5662	0.7304	0.4612	0.5839	0.5156	0.6381	0.6179	0.7746	0.6439	0.8062
7.4	28.0	21000	9.4658	0.5592	0.692	0.5825	0.1978	0.5154	0.7421	0.2813	0.6091	0.6976	0.35	0.6598	0.8716	0.5455	0.6847	0.5658	0.7181	0.4629	0.5851	0.5205	0.6289	0.5959	0.7486	0.6645	0.8202
7.4215	29.0	21750	9.5392	0.5551	0.6834	0.5757	0.1945	0.5108	0.7364	0.2841	0.6065	0.6927	0.3198	0.6547	0.8717	0.5129	0.6606	0.5579	0.716	0.4637	0.5855	0.5227	0.6323	0.6076	0.7507	0.6656	0.8114
7.2585	30.0	22500	9.6376	0.5505	0.6785	0.571	0.1768	0.5061	0.7322	0.2841	0.6026	0.6934	0.2995	0.658	0.8737	0.5121	0.6655	0.5598	0.7161	0.4644	0.5855	0.5103	0.6324	0.6157	0.7533	0.6406	0.808
7.2941	31.0	23250	9.6823	0.5497	0.681	0.5691	0.1685	0.5058	0.7295	0.2849	0.6036	0.6901	0.3233	0.6551	0.8597	0.5186	0.6665	0.5577	0.7138	0.4591	0.5724	0.5151	0.6313	0.6099	0.7529	0.6381	0.8037
7.0969	32.0	24000	9.6241	0.5464	0.6763	0.5669	0.1808	0.5157	0.7223	0.2828	0.6014	0.6868	0.3059	0.6537	0.8605	0.5246	0.6569	0.5555	0.7201	0.4504	0.5723	0.5203	0.6258	0.5769	0.737	0.6507	0.8085
7.1223	33.0	24750	9.6269	0.555	0.6841	0.5733	0.1821	0.5169	0.7369	0.2844	0.6061	0.6928	0.3247	0.6633	0.8626	0.5289	0.6719	0.5499	0.7106	0.4566	0.5777	0.5218	0.6309	0.6137	0.7601	0.659	0.8054
7.0158	34.0	25500	9.5125	0.5589	0.6938	0.5775	0.1936	0.5152	0.739	0.2847	0.6051	0.6955	0.3302	0.6659	0.8629	0.526	0.6664	0.5607	0.7211	0.4676	0.5836	0.5266	0.633	0.6074	0.7591	0.6649	0.8099
7.0803	35.0	26250	9.5032	0.5569	0.6928	0.5761	0.2078	0.5163	0.7342	0.2834	0.6008	0.6875	0.3215	0.6527	0.8594	0.5328	0.6685	0.5546	0.709	0.4635	0.5736	0.5236	0.6312	0.6178	0.754	0.6492	0.7886
6.8887	36.0	27000	9.4894	0.5624	0.6953	0.5828	0.1818	0.5235	0.7405	0.2843	0.608	0.6914	0.3222	0.662	0.8612	0.5312	0.6578	0.5696	0.7204	0.4599	0.5701	0.5252	0.6336	0.6144	0.754	0.6741	0.8128
6.9192	37.0	27750	9.5090	0.5548	0.6878	0.5717	0.1704	0.5122	0.7391	0.2816	0.6042	0.6921	0.319	0.6578	0.8661	0.515	0.6663	0.5599	0.7032	0.4583	0.5732	0.523	0.6273	0.5961	0.7609	0.6766	0.8216
6.8154	38.0	28500	9.5129	0.5597	0.6905	0.5774	0.1902	0.5143	0.744	0.2829	0.6057	0.6907	0.3269	0.6557	0.8652	0.5164	0.6621	0.5637	0.7096	0.4607	0.5719	0.5222	0.6327	0.6143	0.7507	0.6809	0.8173
6.8066	39.0	29250	9.5753	0.5585	0.6906	0.5764	0.2098	0.5146	0.7387	0.2837	0.6081	0.6916	0.3375	0.6544	0.865	0.5191	0.6669	0.5705	0.7114	0.4619	0.5713	0.52	0.6255	0.6129	0.7518	0.6666	0.8224
6.7741	40.0	30000	9.5409	0.5607	0.6907	0.5805	0.1992	0.5156	0.7419	0.2842	0.6088	0.6948	0.3341	0.6649	0.864	0.5274	0.6661	0.575	0.7163	0.4604	0.578	0.5173	0.6294	0.609	0.7605	0.6749	0.8182
6.7611	41.0	30750	9.5476	0.5569	0.6878	0.575	0.1831	0.515	0.7386	0.2817	0.6049	0.6918	0.3338	0.6525	0.8654	0.5189	0.664	0.5558	0.709	0.4609	0.5647	0.5151	0.6252	0.6099	0.7732	0.6809	0.8148
6.6373	42.0	31500	9.4503	0.5628	0.6952	0.5825	0.2107	0.5223	0.7411	0.2836	0.6067	0.6938	0.3276	0.6612	0.8651	0.5315	0.6721	0.5631	0.7087	0.4663	0.5747	0.5246	0.6267	0.6143	0.7663	0.677	0.8142
6.5628	43.0	32250	9.5278	0.5552	0.6846	0.5736	0.1843	0.5114	0.737	0.2815	0.6011	0.6856	0.3069	0.6495	0.8626	0.5213	0.6557	0.5535	0.6878	0.4637	0.5787	0.521	0.6232	0.5972	0.7605	0.6746	0.8074
6.4969	44.0	33000	9.6008	0.5554	0.6888	0.5717	0.1976	0.5064	0.7372	0.2823	0.6013	0.6838	0.3111	0.6463	0.8591	0.5137	0.6526	0.5664	0.7061	0.4614	0.5718	0.5187	0.6212	0.599	0.7533	0.673	0.798
6.5122	45.0	33750	9.4664	0.5618	0.6949	0.5807	0.2163	0.5212	0.7388	0.2834	0.6076	0.6923	0.3291	0.6605	0.8622	0.5301	0.6667	0.5712	0.7109	0.4631	0.5776	0.5221	0.6256	0.6052	0.7562	0.6789	0.8168
6.4897	46.0	34500	9.5417	0.5593	0.6929	0.5762	0.2063	0.5162	0.739	0.2815	0.6068	0.6903	0.3211	0.6569	0.8611	0.5269	0.6629	0.5701	0.7084	0.4597	0.5753	0.5228	0.6269	0.6029	0.762	0.6735	0.8065
6.3466	47.0	35250	9.5257	0.5597	0.6953	0.5779	0.2089	0.5169	0.7385	0.2826	0.6066	0.6925	0.3219	0.6631	0.8611	0.5309	0.6672	0.5739	0.7182	0.4596	0.575	0.5218	0.6249	0.5959	0.758	0.6759	0.8116
6.3352	48.0	36000	9.4974	0.5596	0.6942	0.577	0.1999	0.5202	0.7374	0.2827	0.6057	0.689	0.3277	0.6579	0.8603	0.5275	0.6662	0.5703	0.7085	0.4627	0.5724	0.5259	0.6254	0.5982	0.7536	0.6727	0.808
6.5218	49.0	36750	9.5474	0.5572	0.6902	0.5761	0.1947	0.5148	0.7373	0.2813	0.6053	0.6865	0.3254	0.6528	0.8579	0.519	0.6596	0.5672	0.7059	0.4615	0.5703	0.5222	0.6245	0.5966	0.7529	0.6766	0.806
6.2957	50.0	37500	9.5094	0.56	0.6937	0.5776	0.1949	0.518	0.7408	0.2823	0.6067	0.691	0.3327	0.6582	0.8621	0.5256	0.6642	0.5721	0.7055	0.4636	0.5741	0.5234	0.6241	0.597	0.763	0.6779	0.8151

Framework versions

Transformers 4.53.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

hungnguyen2k4/rtdetr-r50-fruits-best-finetune

rtdetr-r50-fruits-best-finetune

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hungnguyen2k4/
rtdetr-r50-fruits-best-finetune