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furion-123/
conditional_detr_finetuned_rsna_2018

Name: conditional_detr_finetuned_rsna_2018
Availability: InStock

tensorboard

safetensors

conditional_detr

generated_from_trainer

imagefolder

microsoft/conditional-detr-resnet-50

finetune

apache-2.0

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conditional_detr_finetuned_rsna_2018

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

Loss: 1.5677
Map: 0.1803
Map 50: 0.4846
Map 75: 0.0855
Map Small: 0.0382
Map Medium: 0.1840
Map Large: 0.3268
Mar 1: 0.1569
Mar 10: 0.4727
Mar 100: 0.5551
Mar Small: 0.2600
Mar Medium: 0.5593
Mar Large: 0.7209
Map Pneumonia: 0.1803
Mar 100 Pneumonia: 0.5551

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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 Pneumonia	Mar 100 Pneumonia
No log	1.0	301	1.9488	0.0513	0.1423	0.0284	0.0377	0.0500	0.1513	0.0992	0.2874	0.5043	0.1433	0.5163	0.6419	0.0513	0.5043
2.2805	2.0	602	1.8181	0.0830	0.2514	0.0312	0.0257	0.0860	0.2336	0.1296	0.3520	0.5340	0.1367	0.5524	0.6349	0.0830	0.5340
2.2805	3.0	903	1.7791	0.1209	0.3504	0.0546	0.0745	0.1222	0.2082	0.1402	0.3894	0.5315	0.1800	0.5373	0.7209	0.1209	0.5315
1.8321	4.0	1204	1.7326	0.1502	0.4111	0.0696	0.0687	0.1511	0.2655	0.1565	0.4149	0.5234	0.1733	0.5278	0.7256	0.1502	0.5234
1.7202	5.0	1505	1.6750	0.1687	0.4482	0.0909	0.0735	0.1647	0.3322	0.1576	0.4331	0.5540	0.2567	0.5585	0.7186	0.1687	0.5540
1.7202	6.0	1806	1.6909	0.1545	0.4284	0.0581	0.0423	0.1532	0.3184	0.1362	0.4255	0.5414	0.2933	0.5466	0.6651	0.1545	0.5414
1.682	7.0	2107	1.6553	0.1773	0.4797	0.0769	0.0545	0.1776	0.2898	0.1582	0.4366	0.5387	0.3667	0.5351	0.6930	0.1773	0.5387
1.682	8.0	2408	1.6456	0.1724	0.4565	0.0972	0.0449	0.1717	0.3068	0.1551	0.4437	0.5658	0.1967	0.5766	0.7209	0.1724	0.5658
1.6419	9.0	2709	1.6573	0.1622	0.4276	0.0852	0.0254	0.1647	0.2599	0.1565	0.4387	0.5582	0.2433	0.5646	0.7163	0.1622	0.5582
1.6106	10.0	3010	1.6278	0.1826	0.4852	0.0858	0.0252	0.1842	0.3113	0.1615	0.4441	0.5437	0.2867	0.5488	0.6744	0.1826	0.5437
1.6106	11.0	3311	1.6080	0.1731	0.4664	0.0786	0.0230	0.1781	0.2634	0.1627	0.4609	0.5530	0.25	0.5566	0.7302	0.1731	0.5530
1.588	12.0	3612	1.6030	0.1839	0.4810	0.0916	0.0460	0.1859	0.3233	0.1617	0.4677	0.5638	0.2233	0.5722	0.7209	0.1839	0.5638
1.588	13.0	3913	1.6086	0.1734	0.4531	0.0921	0.0542	0.1737	0.3057	0.1596	0.4621	0.5671	0.2333	0.5759	0.7163	0.1734	0.5671
1.5646	14.0	4214	1.5800	0.1730	0.4562	0.0912	0.0366	0.1747	0.3100	0.1567	0.4644	0.5609	0.2900	0.5639	0.7209	0.1730	0.5609
1.5312	15.0	4515	1.5822	0.1705	0.4566	0.0959	0.0509	0.1730	0.2935	0.1474	0.4654	0.5600	0.2633	0.5646	0.7233	0.1705	0.5600
1.5312	16.0	4816	1.5861	0.1667	0.4606	0.0922	0.0556	0.1673	0.3411	0.1526	0.4731	0.5679	0.2800	0.5707	0.7419	0.1667	0.5679
1.5032	17.0	5117	1.5751	0.1769	0.4898	0.0831	0.0528	0.1766	0.3836	0.1534	0.4615	0.5576	0.2633	0.5627	0.7140	0.1769	0.5576
1.5032	18.0	5418	1.5776	0.1735	0.4671	0.0882	0.0478	0.1745	0.3308	0.1507	0.4675	0.5615	0.2867	0.5641	0.7279	0.1735	0.5615
1.4755	19.0	5719	1.5677	0.1726	0.4612	0.0800	0.0465	0.1745	0.3157	0.1513	0.4729	0.5677	0.3067	0.5707	0.7209	0.1726	0.5677
1.4443	20.0	6020	1.5620	0.1801	0.4676	0.0913	0.0390	0.1828	0.3136	0.1586	0.4640	0.5534	0.3033	0.5534	0.7279	0.1801	0.5534
1.4443	21.0	6321	1.5681	0.1695	0.4654	0.0804	0.0523	0.1718	0.3198	0.1542	0.4702	0.5553	0.2867	0.5578	0.7186	0.1695	0.5553
1.4177	22.0	6622	1.5607	0.1755	0.4630	0.0964	0.0368	0.1784	0.3274	0.1503	0.4803	0.5640	0.2767	0.5698	0.7093	0.1755	0.5640
1.4177	23.0	6923	1.5607	0.1727	0.4731	0.0842	0.0437	0.1761	0.3094	0.1536	0.4708	0.5613	0.3000	0.5634	0.7233	0.1727	0.5613
1.3905	24.0	7224	1.5741	0.1828	0.4882	0.0895	0.0526	0.1836	0.3334	0.1557	0.4729	0.5582	0.2533	0.5629	0.7256	0.1828	0.5582
1.3678	25.0	7525	1.5690	0.1813	0.4763	0.0865	0.0422	0.1849	0.3365	0.1584	0.4683	0.5547	0.2633	0.5583	0.7233	0.1813	0.5547
1.3678	26.0	7826	1.5682	0.1801	0.4813	0.0878	0.0337	0.1854	0.3141	0.1524	0.4733	0.5553	0.2633	0.5598	0.7163	0.1801	0.5553
1.3564	27.0	8127	1.5674	0.1799	0.4849	0.0936	0.0397	0.1841	0.3230	0.1540	0.4743	0.5569	0.2633	0.5615	0.7186	0.1799	0.5569
1.3564	28.0	8428	1.5669	0.1792	0.4826	0.0939	0.0356	0.1832	0.3217	0.1553	0.4733	0.5553	0.2567	0.5602	0.7163	0.1792	0.5553
1.3456	29.0	8729	1.5679	0.1808	0.4848	0.0931	0.0382	0.1844	0.3260	0.1580	0.4733	0.5561	0.2600	0.5607	0.7186	0.1808	0.5561
1.3366	30.0	9030	1.5677	0.1803	0.4846	0.0855	0.0382	0.1840	0.3268	0.1569	0.4727	0.5551	0.2600	0.5593	0.7209	0.1803	0.5551

Framework versions

Transformers 4.43.3
Pytorch 2.4.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

furion-123/conditional_detr_finetuned_rsna_2018

conditional_detr_finetuned_rsna_2018

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

furion-123/
conditional_detr_finetuned_rsna_2018