msoft_detr_finetuned_cppe5_4

jaxnwagner/msoft_detr_finetuned_cppe5_4

msoft_detr_finetuned_cppe5_4 is a machine learning model from jaxnwagner. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

License: apache-2.0
Model type: conditional_detr
Access: Public
Created: Oct 21, 2024
Updated: Oct 21, 2024
SHA: 509075a0

Base models

microsoft/conditional-detr-resnet-50

Model card

From the Hugging Face model README

msoft_detr_finetuned_cppe5_4

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: 2.2977
Map: 0.021
Map 50: 0.0499
Map 75: 0.0154
Map Small: 0.0068
Map Medium: 0.0134
Map Large: 0.0527
Mar 1: 0.0707
Mar 10: 0.1568
Mar 100: 0.2057
Mar Small: 0.0544
Mar Medium: 0.1672
Mar Large: 0.2741
Map Coverall: 0.06
Mar 100 Coverall: 0.5324
Map Face Shield: 0.0055
Mar 100 Face Shield: 0.0688
Map Gloves: 0.0015
Mar 100 Gloves: 0.0946
Map Goggles: 0.0083
Mar 100 Goggles: 0.0639
Map Mask: 0.03
Mar 100 Mask: 0.2691

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 7

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 Coverall	Mar 100 Coverall	Map Face Shield	Mar 100 Face Shield	Map Gloves	Mar 100 Gloves	Map Goggles	Mar 100 Goggles	Map Mask	Mar 100 Mask
No log	1.0	32	3.7102	0.0005	0.0021	0.0003	0.0001	0.0004	0.0013	0.0026	0.0261	0.0464	0.0019	0.0354	0.0536	0.0016	0.1713	0.0	0.0	0.0001	0.0123	0.0	0.0111	0.0008	0.0375
No log	2.0	64	2.7691	0.0051	0.0161	0.0015	0.0023	0.0065	0.0113	0.0115	0.0494	0.0884	0.0324	0.0666	0.1259	0.0052	0.2228	0.0	0.0	0.0001	0.0438	0.0007	0.0194	0.0194	0.1559
No log	3.0	96	2.5735	0.0178	0.0407	0.0149	0.0033	0.0102	0.0334	0.0415	0.1076	0.1578	0.0249	0.1105	0.2077	0.0637	0.539	0.0	0.0	0.0002	0.0446	0.0028	0.0222	0.0222	0.1831
No log	4.0	128	2.5173	0.0246	0.0528	0.0225	0.0048	0.0136	0.0308	0.0557	0.1141	0.1519	0.027	0.1288	0.2036	0.0974	0.5074	0.0	0.0	0.0004	0.0692	0.0	0.0	0.0254	0.1831
No log	5.0	160	2.3440	0.0167	0.0413	0.0111	0.0066	0.0106	0.0438	0.0517	0.1312	0.183	0.0472	0.1398	0.2513	0.0437	0.5022	0.0029	0.0312	0.0009	0.0877	0.0074	0.0528	0.0287	0.2412
No log	6.0	192	2.3166	0.0203	0.0471	0.0156	0.0064	0.0131	0.05	0.0655	0.1514	0.2036	0.0635	0.1626	0.2709	0.059	0.5346	0.0039	0.0521	0.0014	0.1015	0.0085	0.0722	0.0286	0.2574
No log	7.0	224	2.2977	0.021	0.0499	0.0154	0.0068	0.0134	0.0527	0.0707	0.1568	0.2057	0.0544	0.1672	0.2741	0.06	0.5324	0.0055	0.0688	0.0015	0.0946	0.0083	0.0639	0.03	0.2691

Framework versions

Transformers 4.44.0
Pytorch 2.5.0+cu124
Datasets 2.21.0
Tokenizers 0.19.1

Type: conditional_detr
License: apache-2.0
Created: Oct 21, 2024
Updated: Oct 21, 2024