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Fardan/
c_detr_finetuned_crack

Name: c_detr_finetuned_crack
Availability: InStock

generated_from_trainer

crack_det-single-class

microsoft/conditional-detr-resnet-50

finetune

apache-2.0

endpoints_compatible

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c_detr_finetuned_crack

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the crack_det-single-class dataset. It achieves the following results on the evaluation set:

Loss: 0.7720
Map: 0.7074
Map 50: 0.9889
Map 75: 0.8198
Map Small: -1.0
Map Medium: 0.4631
Map Large: 0.7157
Mar 1: 0.7587
Mar 10: 0.8086
Mar 100: 0.8472
Mar Small: -1.0
Mar Medium: 0.7188
Mar Large: 0.8516
Map Per Class: 0.7074
Mar 100 Per Class: 0.8472
Classes: 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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 30

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 Per Class	Mar 100 Per Class
No log	1.0	210	1.4511	0.1004	0.3026	0.0548	-1.0	0.0077	0.1038	0.2008	0.4616	0.7195	-1.0	0.1688	0.7386	0.1004	0.7195
No log	2.0	420	1.2516	0.0905	0.2453	0.0614	-1.0	0.1688	0.093	0.1899	0.5191	0.7853	-1.0	0.4313	0.7976	0.0905	0.7853
1.6634	3.0	630	1.1064	0.2139	0.4163	0.2028	-1.0	0.1033	0.2192	0.413	0.7189	0.8205	-1.0	0.5938	0.8284	0.2139	0.8205
1.6634	4.0	840	0.9684	0.4121	0.6299	0.4671	-1.0	0.1894	0.4219	0.5736	0.782	0.8398	-1.0	0.7625	0.8425	0.4121	0.8398
1.0881	5.0	1050	0.8984	0.4627	0.6658	0.5446	-1.0	0.2979	0.4723	0.6428	0.8143	0.86	-1.0	0.7937	0.8623	0.4627	0.86
1.0881	6.0	1260	1.0028	0.4664	0.7552	0.5139	-1.0	0.2459	0.4754	0.5501	0.783	0.8325	-1.0	0.6875	0.8375	0.4664	0.8325
1.0881	7.0	1470	0.8905	0.5262	0.8111	0.6015	-1.0	0.371	0.5339	0.6394	0.8321	0.8658	-1.0	0.7625	0.8694	0.5262	0.8658
0.992	8.0	1680	0.8781	0.618	0.9105	0.6986	-1.0	0.3757	0.6277	0.6853	0.8124	0.8564	-1.0	0.7125	0.8614	0.618	0.8564
0.992	9.0	1890	0.8787	0.6643	0.9763	0.7691	-1.0	0.3965	0.6742	0.7086	0.8017	0.8457	-1.0	0.725	0.8499	0.6643	0.8457
0.9551	10.0	2100	0.8690	0.6572	0.9679	0.737	-1.0	0.392	0.6665	0.713	0.8082	0.852	-1.0	0.7937	0.854	0.6572	0.852
0.9551	11.0	2310	0.8356	0.6543	0.9343	0.7416	-1.0	0.414	0.6633	0.7109	0.8172	0.8648	-1.0	0.7125	0.8701	0.6543	0.8648
0.9588	12.0	2520	0.8476	0.6492	0.956	0.7496	-1.0	0.4645	0.6562	0.6971	0.8109	0.8486	-1.0	0.7375	0.8525	0.6492	0.8486
0.9588	13.0	2730	0.9063	0.6379	0.9608	0.7469	-1.0	0.4929	0.644	0.6956	0.7706	0.8096	-1.0	0.7125	0.813	0.6379	0.8096
0.9588	14.0	2940	0.8993	0.647	0.9674	0.7443	-1.0	0.4055	0.6558	0.7013	0.7897	0.8283	-1.0	0.7188	0.8321	0.647	0.8283
0.944	15.0	3150	0.8545	0.6804	0.9804	0.7761	-1.0	0.4121	0.6895	0.7268	0.7874	0.8038	-1.0	0.7063	0.8072	0.6804	0.8038
0.944	16.0	3360	0.8423	0.6671	0.9738	0.7776	-1.0	0.4201	0.6762	0.7243	0.8109	0.8514	-1.0	0.7375	0.8553	0.6671	0.8514
0.9288	17.0	3570	0.8323	0.6785	0.9845	0.7896	-1.0	0.4709	0.6854	0.7302	0.7975	0.8392	-1.0	0.7563	0.8421	0.6785	0.8392
0.9288	18.0	3780	0.8367	0.6676	0.9833	0.7846	-1.0	0.5013	0.6744	0.7233	0.792	0.8444	-1.0	0.6875	0.8499	0.6676	0.8444
0.9288	19.0	3990	0.8186	0.6799	0.9815	0.7832	-1.0	0.454	0.6882	0.7262	0.8004	0.8438	-1.0	0.7312	0.8477	0.6799	0.8438
0.8787	20.0	4200	0.8029	0.6936	0.9888	0.8104	-1.0	0.4717	0.7014	0.7419	0.8063	0.8457	-1.0	0.7	0.8508	0.6936	0.8457
0.8787	21.0	4410	0.7984	0.6925	0.9848	0.8231	-1.0	0.4922	0.6996	0.7472	0.8015	0.8405	-1.0	0.7063	0.8451	0.6925	0.8405
0.871	22.0	4620	0.7899	0.6954	0.9894	0.81	-1.0	0.444	0.7039	0.7562	0.8099	0.8447	-1.0	0.7437	0.8482	0.6954	0.8447
0.871	23.0	4830	0.7953	0.6981	0.9873	0.8177	-1.0	0.4464	0.7065	0.7528	0.804	0.8367	-1.0	0.7	0.8414	0.6981	0.8367
0.8376	24.0	5040	0.7875	0.697	0.9881	0.8162	-1.0	0.4791	0.7054	0.7553	0.805	0.8405	-1.0	0.725	0.8445	0.697	0.8405
0.8376	25.0	5250	0.7761	0.7021	0.9876	0.8154	-1.0	0.4685	0.7104	0.7543	0.8099	0.8472	-1.0	0.7375	0.851	0.7021	0.8472
0.8376	26.0	5460	0.7657	0.7044	0.9875	0.8291	-1.0	0.48	0.7122	0.7532	0.8128	0.8507	-1.0	0.7312	0.8549	0.7044	0.8507
0.8359	27.0	5670	0.7697	0.7052	0.9883	0.8224	-1.0	0.4599	0.7137	0.7553	0.8088	0.8465	-1.0	0.7188	0.851	0.7052	0.8465
0.8359	28.0	5880	0.7708	0.7072	0.9879	0.8273	-1.0	0.4554	0.716	0.7574	0.8096	0.8493	-1.0	0.7	0.8544	0.7072	0.8493
0.8179	29.0	6090	0.7714	0.7076	0.9887	0.8244	-1.0	0.4626	0.7161	0.7589	0.8082	0.8463	-1.0	0.725	0.8505	0.7076	0.8463
0.8179	30.0	6300	0.7720	0.7074	0.9889	0.8198	-1.0	0.4631	0.7157	0.7587	0.8086	0.8472	-1.0	0.7188	0.8516	0.7074	0.8472

Framework versions

Transformers 4.55.4
Pytorch 2.8.0+cu128
Datasets 3.6.0
Tokenizers 0.21.4

Fardan/c_detr_finetuned_crack

c_detr_finetuned_crack

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Fardan/
c_detr_finetuned_crack