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Mobe1/
argos-dentsight-stage1-fdi-v5

Name: argos-dentsight-stage1-fdi-v5
Availability: InStock

generated_from_trainer

Mobe1/argos-dentsight-stage1-fdi-v3

finetune

cc-by-nc-sa-4.0

endpoints_compatible

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argos-dentsight-stage1-fdi-v5

This model is a fine-tuned version of Mobe1/argos-dentsight-stage1-fdi-v3 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.1901
Map: 0.6989
Map 50: 0.823
Map 75: 0.7847
Map Small: 0.2429
Map Medium: 0.7074
Map Large: 0.7896
Mar 1: 0.7931
Mar 10: 0.8815
Mar 100: 0.882
Mar Small: 0.4321
Mar Medium: 0.8848
Mar Large: 0.8922
Map 11: 0.3954
Mar 100 11: 0.8202
Map 12: 0.544
Mar 100 12: 0.8622
Map 13: 0.5921
Mar 100 13: 0.8547
Map 14: 0.7589
Mar 100 14: 0.8447
Map 15: 0.3846
Mar 100 15: 0.894
Map 16: 0.7968
Mar 100 16: 0.8796
Map 17: 0.467
Mar 100 17: 0.9241
Map 18: 0.7507
Mar 100 18: 0.8688
Map 21: 0.7128
Mar 100 21: 0.8259
Map 22: 0.7425
Mar 100 22: 0.8515
Map 23: 0.7821
Mar 100 23: 0.8722
Map 24: 0.7082
Mar 100 24: 0.8633
Map 25: 0.8127
Mar 100 25: 0.89
Map 26: 0.8341
Mar 100 26: 0.9109
Map 27: 0.8889
Mar 100 27: 0.945
Map 28: 0.6077
Mar 100 28: 0.9132
Map 31: 0.5252
Mar 100 31: 0.7541
Map 32: 0.585
Mar 100 32: 0.8215
Map 33: 0.485
Mar 100 33: 0.8883
Map 34: 0.8238
Mar 100 34: 0.9037
Map 35: 0.7018
Mar 100 35: 0.9188
Map 36: 0.9264
Mar 100 36: 0.9608
Map 37: 0.7942
Mar 100 37: 0.9583
Map 38: 0.6572
Mar 100 38: 0.9395
Map 41: 0.5709
Mar 100 41: 0.7495
Map 42: 0.6752
Mar 100 42: 0.7947
Map 43: 0.7588
Mar 100 43: 0.8554
Map 44: 0.7651
Mar 100 44: 0.8971
Map 45: 0.8251
Mar 100 45: 0.8946
Map 46: 0.8968
Mar 100 46: 0.9519
Map 47: 0.9031
Mar 100 47: 0.968
Map 48: 0.6939
Mar 100 48: 0.9473

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: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 12.0

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 11	Mar 100 11	Map 12	Mar 100 12	Map 13	Mar 100 13	Map 14	Mar 100 14	Map 15	Mar 100 15	Map 16	Mar 100 16	Map 17	Mar 100 17	Map 18	Mar 100 18	Map 21	Mar 100 21	Map 22	Mar 100 22	Map 23	Mar 100 23	Map 24	Mar 100 24	Map 25	Mar 100 25	Map 26	Mar 100 26	Map 27	Mar 100 27	Map 28	Mar 100 28	Map 31	Mar 100 31	Map 32	Mar 100 32	Map 33	Mar 100 33	Map 34	Mar 100 34	Map 35	Mar 100 35	Map 36	Mar 100 36	Map 37	Mar 100 37	Map 38	Mar 100 38	Map 41	Mar 100 41	Map 42	Mar 100 42	Map 43	Mar 100 43	Map 44	Mar 100 44	Map 45	Mar 100 45	Map 46	Mar 100 46	Map 47	Mar 100 47	Map 48	Mar 100 48
No log	1.0	424	3.0298	0.6444	0.7648	0.7264	0.2677	0.6541	0.8616	0.7625	0.876	0.8766	0.4548	0.8793	0.8914	0.6408	0.8162	0.618	0.8602	0.6306	0.877	0.6898	0.8044	0.5115	0.8981	0.8245	0.8838	0.3434	0.9181	0.6807	0.8919	0.7273	0.8343	0.7523	0.8498	0.7094	0.8468	0.6609	0.8721	0.6254	0.8944	0.8409	0.9037	0.8526	0.9419	0.6705	0.9159	0.4846	0.756	0.5043	0.8203	0.322	0.8074	0.7677	0.8903	0.3324	0.9082	0.9082	0.9573	0.6016	0.9574	0.5796	0.939	0.5786	0.7441	0.6561	0.7931	0.6972	0.8511	0.7015	0.8982	0.747	0.8662	0.8483	0.9508	0.856	0.9662	0.2557	0.9355
15.0942	2.0	848	3.0714	0.6411	0.7632	0.7239	0.2165	0.652	0.7861	0.7526	0.8751	0.877	0.3819	0.8803	0.9086	0.6219	0.8214	0.7311	0.8531	0.5664	0.8156	0.7149	0.8322	0.2812	0.8929	0.8064	0.8811	0.182	0.9189	0.5818	0.8683	0.7517	0.8294	0.7585	0.8406	0.75	0.8663	0.5769	0.861	0.6733	0.8922	0.8222	0.905	0.8816	0.9453	0.6877	0.9085	0.4696	0.7511	0.4885	0.8138	0.3916	0.8713	0.8276	0.8968	0.3921	0.9196	0.9007	0.9488	0.4603	0.9518	0.3928	0.9367	0.5877	0.7473	0.6674	0.7883	0.7324	0.8585	0.784	0.8943	0.7947	0.8878	0.8938	0.9525	0.818	0.9682	0.526	0.9467
14.7757	3.0	1272	3.0818	0.6753	0.7961	0.759	0.2207	0.6867	0.7043	0.7832	0.8722	0.8735	0.4291	0.8762	0.878	0.5035	0.818	0.6145	0.8577	0.4619	0.7659	0.6845	0.8011	0.3539	0.8942	0.7997	0.8829	0.4461	0.9208	0.5565	0.852	0.7243	0.8128	0.7626	0.852	0.7012	0.832	0.6759	0.8713	0.7722	0.8781	0.8372	0.9043	0.87	0.9407	0.6328	0.9011	0.5198	0.7555	0.62	0.8237	0.4605	0.8811	0.8098	0.8933	0.6066	0.9252	0.9221	0.9631	0.7568	0.9681	0.5742	0.9452	0.503	0.751	0.6726	0.7928	0.7481	0.8616	0.7784	0.8891	0.7977	0.8741	0.8829	0.9404	0.8451	0.9588	0.7168	0.9456
14.6389	4.0	1696	3.1031	0.6803	0.8042	0.7633	0.2575	0.69	0.7705	0.7843	0.8779	0.8792	0.4001	0.8826	0.8629	0.4907	0.8232	0.7252	0.8607	0.6123	0.8686	0.7564	0.8384	0.4921	0.8973	0.7973	0.8766	0.2181	0.9238	0.6706	0.8647	0.7299	0.8284	0.7575	0.8455	0.7278	0.8517	0.6527	0.868	0.786	0.8889	0.8205	0.909	0.8862	0.9473	0.6634	0.9175	0.4721	0.7423	0.5989	0.8121	0.3679	0.8742	0.786	0.8813	0.6527	0.9244	0.918	0.9546	0.7208	0.958	0.4463	0.9316	0.5769	0.7422	0.6583	0.7821	0.7514	0.8582	0.7651	0.9052	0.8322	0.897	0.8936	0.9471	0.8753	0.9685	0.6679	0.9473
14.4705	5.0	2120	3.1906	0.6725	0.7921	0.7558	0.201	0.6838	0.783	0.7728	0.8761	0.8771	0.4107	0.88	0.8771	0.3635	0.8202	0.6404	0.861	0.5411	0.8177	0.7221	0.8357	0.3829	0.8915	0.7796	0.8751	0.2449	0.9214	0.6787	0.8579	0.7003	0.8086	0.762	0.8468	0.7589	0.864	0.5372	0.8644	0.8128	0.8892	0.8325	0.9062	0.885	0.9447	0.5639	0.9085	0.5112	0.7496	0.5434	0.8145	0.4543	0.8818	0.8053	0.8828	0.7138	0.9228	0.9193	0.9587	0.8014	0.9583	0.4893	0.9333	0.5731	0.7493	0.6883	0.7955	0.7568	0.858	0.7762	0.8992	0.8241	0.8905	0.8984	0.9498	0.8974	0.9688	0.6639	0.9408
14.2978	6.0	2544	3.0802	0.7007	0.8284	0.7888	0.2202	0.7106	0.8053	0.7975	0.879	0.8799	0.452	0.8827	0.8971	0.4023	0.8172	0.6001	0.8584	0.6125	0.8607	0.7499	0.8371	0.3733	0.8846	0.8019	0.8784	0.4	0.9186	0.7275	0.8656	0.7376	0.8259	0.7483	0.8475	0.8005	0.87	0.718	0.8622	0.8055	0.8864	0.8311	0.9102	0.8804	0.9385	0.6254	0.9074	0.5316	0.7491	0.5831	0.8208	0.5223	0.884	0.7975	0.893	0.6439	0.9223	0.9228	0.9577	0.8049	0.9632	0.6778	0.9373	0.5921	0.7498	0.6764	0.7919	0.7374	0.8573	0.7456	0.8997	0.8199	0.89	0.8892	0.9529	0.9051	0.9694	0.7599	0.9509
14.2978	7.0	2968	3.0640	0.6964	0.8217	0.7824	0.2577	0.704	0.788	0.7908	0.8808	0.8817	0.4239	0.8846	0.8984	0.3976	0.8195	0.5516	0.8612	0.6315	0.8561	0.7629	0.8439	0.3215	0.8912	0.778	0.8787	0.3397	0.9208	0.7391	0.8747	0.7265	0.8232	0.7626	0.848	0.784	0.8722	0.7087	0.8729	0.8239	0.8906	0.8367	0.9075	0.8951	0.9436	0.667	0.9122	0.5262	0.7541	0.5823	0.8167	0.4901	0.8876	0.8059	0.8958	0.692	0.9194	0.9295	0.9618	0.8295	0.9564	0.6538	0.9429	0.5651	0.7512	0.6677	0.7911	0.76	0.8625	0.7848	0.8987	0.8371	0.9027	0.8965	0.9525	0.9159	0.9674	0.6222	0.9385
14.1763	8.0	3392	3.1510	0.7013	0.8246	0.7865	0.2258	0.7097	0.7944	0.7944	0.8823	0.8829	0.4282	0.8858	0.8943	0.4158	0.8167	0.5694	0.8637	0.6206	0.8676	0.7626	0.8439	0.3559	0.8907	0.7923	0.8769	0.3346	0.9211	0.7374	0.8701	0.7117	0.8252	0.7555	0.8495	0.7957	0.8813	0.7198	0.8646	0.8181	0.8908	0.8353	0.9106	0.8812	0.943	0.6532	0.9095	0.5282	0.7518	0.5918	0.8189	0.4879	0.8878	0.8298	0.9052	0.7177	0.9239	0.9301	0.9621	0.8336	0.9604	0.6702	0.9418	0.5787	0.7532	0.668	0.7981	0.7619	0.8561	0.772	0.8997	0.8315	0.8968	0.8908	0.9529	0.9111	0.9697	0.6786	0.9479
14.0968	9.0	3816	3.1846	0.6969	0.821	0.782	0.2747	0.7051	0.7894	0.7916	0.8813	0.8817	0.4685	0.8845	0.8922	0.4022	0.8195	0.5804	0.8607	0.5774	0.8616	0.7579	0.8414	0.4387	0.8942	0.8017	0.8778	0.4795	0.9241	0.7583	0.8778	0.7039	0.8183	0.7404	0.851	0.7665	0.8744	0.6949	0.871	0.8155	0.8861	0.8285	0.9071	0.8838	0.943	0.6009	0.9111	0.5246	0.7555	0.5794	0.8191	0.4376	0.888	0.8077	0.8995	0.6802	0.921	0.9272	0.9614	0.8006	0.9482	0.6288	0.939	0.5772	0.749	0.6772	0.7945	0.755	0.8527	0.7352	0.8966	0.8286	0.9011	0.8962	0.9532	0.919	0.97	0.6963	0.9473
13.9544	10.0	4240	3.1939	0.6991	0.8236	0.7851	0.2478	0.7076	0.7919	0.7946	0.8808	0.8814	0.4431	0.8841	0.9065	0.423	0.8217	0.5606	0.8607	0.5728	0.8487	0.7599	0.8436	0.4114	0.8945	0.8002	0.8829	0.4998	0.9244	0.7479	0.8665	0.7105	0.8212	0.7365	0.8478	0.7798	0.8714	0.695	0.8646	0.8146	0.8892	0.8274	0.9093	0.8889	0.9444	0.596	0.9074	0.5084	0.7526	0.5929	0.8206	0.4836	0.8895	0.8227	0.9042	0.7048	0.9204	0.9317	0.9621	0.8007	0.95	0.6308	0.9407	0.5677	0.7502	0.6728	0.7974	0.7573	0.8549	0.7483	0.8956	0.8297	0.8995	0.8963	0.9549	0.904	0.9682	0.6942	0.9467
13.8974	11.0	4664	3.1962	0.7007	0.8248	0.7863	0.2399	0.7095	0.7896	0.7949	0.8817	0.8823	0.4276	0.8851	0.8922	0.399	0.8217	0.5545	0.8622	0.5937	0.8537	0.7544	0.8387	0.3838	0.8959	0.7986	0.8829	0.4822	0.9247	0.7522	0.8697	0.7155	0.8242	0.7419	0.8512	0.7823	0.8734	0.72	0.8644	0.8176	0.89	0.8353	0.9099	0.8878	0.945	0.6075	0.9122	0.5276	0.7582	0.5909	0.8211	0.4775	0.888	0.8206	0.9032	0.7039	0.9186	0.9272	0.9604	0.7922	0.9589	0.656	0.9401	0.569	0.75	0.6744	0.7955	0.7623	0.8554	0.767	0.899	0.8253	0.8954	0.8979	0.9532	0.9041	0.9685	0.7004	0.9473
13.8997	12.0	5088	3.1901	0.6989	0.823	0.7847	0.2429	0.7074	0.7896	0.7931	0.8815	0.882	0.4321	0.8848	0.8922	0.3954	0.8202	0.544	0.8622	0.5921	0.8547	0.7589	0.8447	0.3846	0.894	0.7968	0.8796	0.467	0.9241	0.7507	0.8688	0.7128	0.8259	0.7425	0.8515	0.7821	0.8722	0.7082	0.8633	0.8127	0.89	0.8341	0.9109	0.8889	0.945	0.6077	0.9132	0.5252	0.7541	0.585	0.8215	0.485	0.8883	0.8238	0.9037	0.7018	0.9188	0.9264	0.9608	0.7942	0.9583	0.6572	0.9395	0.5709	0.7495	0.6752	0.7947	0.7588	0.8554	0.7651	0.8971	0.8251	0.8946	0.8968	0.9519	0.9031	0.968	0.6939	0.9473

Framework versions

Transformers 4.57.6
Pytorch 2.11.0+cu130
Datasets 4.8.5
Tokenizers 0.22.2

Mobe1/argos-dentsight-stage1-fdi-v5

argos-dentsight-stage1-fdi-v5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Mobe1/
argos-dentsight-stage1-fdi-v5