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amSOwO/
detr_finetuned_cppe5

Name: detr_finetuned_cppe5
Rating: 2.5 (15 reviews)

generated_from_trainer

microsoft/conditional-detr-resnet-50

finetune

apache-2.0

endpoints_compatible

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detr_finetuned_cppe5

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

Loss: 3.5826
Map: 0.0003
Map 50: 0.0007
Map 75: 0.0
Map Small: 0.0014
Map Medium: 0.0001
Map Large: 0.0
Mar 1: 0.0
Mar 10: 0.0021
Mar 100: 0.0075
Mar Small: 0.0118
Mar Medium: 0.0062
Mar Large: 0.01
Map Coverall: 0.0
Mar 100 Coverall: 0.0
Map Face Shield: 0.0
Mar 100 Face Shield: 0.0
Map Gloves: 0.0015
Mar 100 Gloves: 0.0375
Map Goggles: 0.0
Mar 100 Goggles: 0.0
Map Mask: 0.0
Mar 100 Mask: 0.0

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: 0.001
train_batch_size: 8
eval_batch_size: 8
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
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 Gloves	Mar 100 Gloves	Map Mask	Mar 100 Mask
No log	1.0	107	3.5856	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0003	0.0061	0.0006	0.0065	0.0157	0.0	0.0	0.0	0.0307
No log	2.0	214	3.7297	0.0	0.0001	0.0	0.0	0.0	0.0	0.0	0.0002	0.0078	0.0097	0.011	0.0017	0.0	0.0	0.0001	0.0391
No log	3.0	321	3.8304	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0044	0.0	0.0032	0.0149	0.0	0.0	0.0	0.0222
No log	4.0	428	3.6958	0.0	0.0001	0.0	0.0	0.0	0.0	0.0002	0.0004	0.0071	0.0059	0.0105	0.0047	0.0	0.0	0.0001	0.0356
2.8722	5.0	535	4.0271	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0014	0.0	0.0017	0.0034	0.0	0.0	0.0	0.0071
2.8722	6.0	642	3.6288	0.0	0.0001	0.0	0.0001	0.0	0.0	0.0	0.001	0.0125	0.01	0.0159	0.014	0.0	0.0	0.0002	0.0627
2.8722	7.0	749	3.6254	0.0	0.0001	0.0	0.0001	0.0	0.0001	0.0	0.0005	0.0063	0.0105	0.0035	0.0132	0.0001	0.0312	0.0	0.0
2.8722	8.0	856	3.8407	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0003	0.0016	0.0	0.0017	0.0043	0.0	0.0	0.0	0.008
2.8722	9.0	963	3.6509	0.0	0.0002	0.0	0.0001	0.0	0.0	0.0	0.0009	0.0066	0.0072	0.007	0.0059	0.0002	0.033	0.0	0.0
2.5968	10.0	1070	3.6992	0.0	0.0	0.0	0.0	0.0001	0.0	0.0	0.0003	0.0033	0.0033	0.0041	0.0014	0.0	0.0165	0.0	0.0
2.5968	11.0	1177	3.6423	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0012	0.006	0.0006	0.0097	0.0085	0.0	0.0	0.0001	0.0298
2.5968	12.0	1284	3.5987	0.0005	0.0023	0.0	0.0019	0.0001	0.0	0.0002	0.0009	0.0077	0.0092	0.0066	0.0114	0.0024	0.0384	0.0	0.0
2.5968	13.0	1391	3.6200	0.0	0.0001	0.0	0.0005	0.0	0.0	0.0	0.0028	0.0076	0.0099	0.0077	0.0068	0.0002	0.0379	0.0	0.0
2.5968	14.0	1498	3.7723	0.0	0.0001	0.0	0.0	0.0001	0.0	0.0003	0.0006	0.0044	0.0007	0.0044	0.0077	0.0001	0.0219	0.0	0.0
2.5557	15.0	1605	3.6236	0.0	0.0001	0.0	0.0	0.0	0.0	0.0003	0.0011	0.0081	0.0021	0.0099	0.0162	0.0	0.0	0.0001	0.0404
2.5557	16.0	1712	3.6496	0.0001	0.0002	0.0	0.0008	0.0003	0.0	0.0004	0.0017	0.0065	0.0059	0.0066	0.0077	0.0003	0.0326	0.0	0.0
2.5557	17.0	1819	3.6098	0.0	0.0001	0.0	0.0	0.0001	0.0	0.0	0.0009	0.0046	0.0007	0.0039	0.0105	0.0001	0.0232	0.0	0.0
2.5557	18.0	1926	3.6017	0.0001	0.0003	0.0	0.0	0.0005	0.0	0.0004	0.0015	0.0061	0.0026	0.0069	0.0068	0.0003	0.0304	0.0	0.0
2.5084	19.0	2033	3.5866	0.0003	0.0006	0.0004	0.0045	0.0002	0.0	0.0013	0.0029	0.0078	0.0066	0.0079	0.0095	0.0014	0.0388	0.0	0.0
2.5084	20.0	2140	3.6129	0.0002	0.0022	0.0	0.0008	0.0001	0.0	0.0001	0.0015	0.0073	0.0066	0.0069	0.0105	0.0012	0.0366	0.0	0.0
2.5084	21.0	2247	3.5974	0.0004	0.001	0.0	0.002	0.0002	0.0	0.0004	0.0033	0.0083	0.0099	0.0086	0.0077	0.002	0.0415	0.0	0.0
2.5084	22.0	2354	3.5810	0.0	0.0002	0.0	0.0001	0.0001	0.0	0.0	0.0012	0.0075	0.0072	0.0069	0.0109	0.0002	0.0375	0.0	0.0
2.5084	23.0	2461	3.6132	0.0005	0.0013	0.0	0.0016	0.0002	0.0	0.0	0.0028	0.0077	0.0079	0.0079	0.0082	0.0023	0.0384	0.0	0.0
2.4812	24.0	2568	3.5863	0.0002	0.0022	0.0	0.0009	0.0001	0.0	0.0001	0.0018	0.0069	0.0079	0.007	0.0068	0.0012	0.0344	0.0	0.0
2.4812	25.0	2675	3.6469	0.0002	0.0022	0.0	0.0008	0.0001	0.0	0.0001	0.0018	0.007	0.0059	0.0076	0.0068	0.0012	0.0348	0.0	0.0
2.4812	26.0	2782	3.5896	0.0001	0.0006	0.0	0.0004	0.0001	0.0	0.0	0.0017	0.007	0.0099	0.0063	0.0082	0.0004	0.0348	0.0	0.0
2.4812	27.0	2889	3.5994	0.0001	0.0005	0.0	0.0007	0.0001	0.0	0.0004	0.0024	0.0074	0.0112	0.0066	0.0086	0.0006	0.0371	0.0	0.0
2.4812	28.0	2996	3.5806	0.0002	0.0006	0.0	0.0014	0.0001	0.0	0.0	0.0019	0.0072	0.0112	0.0058	0.0105	0.0012	0.0362	0.0	0.0
2.4512	29.0	3103	3.5821	0.0002	0.0006	0.0	0.0014	0.0001	0.0	0.0004	0.0023	0.0075	0.0118	0.0062	0.01	0.0011	0.0375	0.0	0.0
2.4512	30.0	3210	3.5826	0.0003	0.0007	0.0	0.0014	0.0001	0.0	0.0	0.0021	0.0075	0.0118	0.0062	0.01	0.0015	0.0375	0.0	0.0

Framework versions

Transformers 4.55.4
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.21.4

amSOwO/detr_finetuned_cppe5

detr_finetuned_cppe5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

amSOwO/
detr_finetuned_cppe5