The open-source object detection model DETR - ResNet - 50 - DC5, fine-tuned on the locust dataset for applicable detection tasks

Detr Resnet 50 Dc5 Grasshopper Testdata Finetuned2.0 Maxsteps 10000 Batchsize 2 Ilham

Developed by Ilhamfaisal

This model is a fine-tuned version of facebook/detr-resnet-50-dc5 on a grasshopper test dataset, primarily used for object detection tasks.

Object Detection

Transformers

Open Source License:Apache-2.0 #Insect Object Detection #Agricultural Pest Identification #Fine-tuned DETR Model

Downloads 92

Release Time : 1/22/2025

Model Overview

This model is an object detection model based on the DETR (Detection Transformer) architecture, specifically fine-tuned for detecting insects such as grasshoppers.

Model Features

Transformer-based Object Detection

Utilizes the DETR architecture, combining the strengths of Transformer and CNN for end-to-end object detection.

Optimized for Insect Detection

Specially fine-tuned for detecting insects like grasshoppers, suitable for agricultural pest monitoring scenarios.

Efficient Detection

Uses ResNet-50 as the backbone network, achieving relatively efficient detection while maintaining high accuracy.

Model Capabilities

Object Detection

Insect Identification

Agricultural Pest Monitoring

Use Cases

Agricultural Monitoring

Grasshopper Population Monitoring

Used to monitor the density and distribution of grasshopper populations in farmland

Capable of detecting various pests such as black-tailed leafhoppers and Malayan leafhoppers

Pest Warning System

Integrated into agricultural warning systems to promptly detect the risk of pest outbreaks

🚀 detr-resnet-50-dc5-grasshopper-testdata-finetuned2.0-maxsteps-10000-batchsize-2-ilham

This model is a fine - tuned version of [facebook/detr - resnet - 50 - dc5](https://huggingface.co/facebook/detr - resnet - 50 - dc5) on the None dataset. It offers a set of evaluation metrics that can be used to assess its performance in relevant tasks.

🚀 Quick Start

This fine - tuned model is based on [facebook/detr - resnet - 50 - dc5](https://huggingface.co/facebook/detr - resnet - 50 - dc5). You can use it according to the general usage methods of the transformers library.

✨ Features

Fine - tuned: It is a fine - tuned version of [facebook/detr - resnet - 50 - dc5](https://huggingface.co/facebook/detr - resnet - 50 - dc5), potentially better adapted to specific tasks.
Rich Evaluation Metrics: It provides multiple evaluation metrics such as loss, mAP, and mAR, which can comprehensively evaluate the model's performance.

📚 Documentation

Model Performance

This model achieves the following results on the evaluation set:

Loss: 3.0007
Map: 0.0047
Map 50: 0.0173
Map 75: 0.0009
Map Small: 0.0047
Map Medium: - 1.0
Map Large: - 1.0
Mar 1: 0.0017
Mar 10: 0.0117
Mar 100: 0.0426
Mar Small: 0.0426
Mar Medium: - 1.0
Mar Large: - 1.0
Map Recilia dorsalis: 0.0113
Mar 100 Recilia dorsalis: 0.1227
Map Nephotettix malayanus: 0.0075
Mar 100 Nephotettix malayanus: 0.0307
Map Sogatella furcifera: 0.0
Mar 100 Sogatella furcifera: 0.0
Map Nilaparvata lugens: 0.0
Mar 100 Nilaparvata lugens: 0.0171

Training Procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e - 05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
lr_scheduler_type: linear
training_steps: 10000
mixed_precision_training: Native AMP

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 Recilia dorsalis	Mar 100 Recilia dorsalis	Map Nephotettix malayanus	Mar 100 Nephotettix malayanus	Map Nilaparvata lugens	Mar 100 Nilaparvata lugens
4.922	0.5952	50	4.2729	0.0001	0.0003	0.0	0.0001	-1.0	-1.0	0.0	0.0015	0.0085	0.0085	-1.0	-1.0	0.0	0.0	0.0001	0.0177	0.0002	0.0165
4.1668	1.1905	100	3.9789	0.0	0.0001	0.0	0.0	-1.0	-1.0	0.0	0.0008	0.0051	0.0051	-1.0	-1.0	0.0	0.0	0.0001	0.0163	0.0	0.0041
4.5854	1.7857	150	3.7985	0.0001	0.0002	0.0	0.0001	-1.0	-1.0	0.0	0.0015	0.0055	0.0055	-1.0	-1.0	0.0	0.0	0.0001	0.0163	0.0001	0.0059
4.3943	2.3810	200	3.7751	0.0	0.0001	0.0	0.0	-1.0	-1.0	0.0	0.001	0.004	0.004	-1.0	-1.0	0.0	0.0	0.0	0.0042	0.0	0.0118
2.9732	2.9762	250	3.7922	0.0	0.0001	0.0	0.0	-1.0	-1.0	0.0	0.0013	0.0023	0.0023	-1.0	-1.0	0.0	0.0	0.0	0.0005	0.0001	0.0088
3.571	3.5714	300	3.8144	0.0	0.0001	0.0	0.0	-1.0	-1.0	0.0	0.001	0.0018	0.0018	-1.0	-1.0	0.0	0.0	0.0	0.0	0.0001	0.0071
2.4278	4.1667	350	3.7715	0.0	0.0001	0.0	0.0	-1.0	-1.0	0.0002	0.0005	0.0028	0.0028	-1.0	-1.0	0.0	0.0009	0.0	0.0009	0.0	0.0094
4.0768	4.7619	400	3.6700	0.0001	0.0004	0.0	0.0001	-1.0	-1.0	0.0001	0.0011	0.0053	0.0053	-1.0	-1.0	0.0001	0.0125	0.0	0.0	0.0001	0.0088
4.4523	5.3571	450	3.6946	0.0001	0.0004	0.0	0.0001	-1.0	-1.0	0.0	0.0009	0.0074	0.0074	-1.0	-1.0	0.0002	0.0213	0.0	0.0	0.0001	0.0082
3.5476	5.9524	500	3.7100	0.0	0.0002	0.0	0.0	-1.0	-1.0	0.0	0.0016	0.0049	0.0049	-1.0	-1.0	0.0	0.0042	0.0	0.0	0.0001	0.0153
3.5861	6.5476	550	3.6604	0.0001	0.0005	0.0	0.0001	-1.0	-1.0	0.0	0.0008	0.0089	0.0089	-1.0	-1.0	0.0003	0.0343	0.0	0.0	0.0	0.0012
3.32	7.1429	600	3.6814	0.0003	0.0013	0.0	0.0003	-1.0	-1.0	0.0	0.003	0.0167	0.0167	-1.0	-1.0	0.001	0.0667	0.0	0.0	0.0	0.0
4.4471	7.7381	650	3.7570	0.0001	0.0005	0.0	0.0001	-1.0	-1.0	0.0003	0.0003	0.0073	0.0073	-1.0	-1.0	0.0003	0.0292	0.0	0.0	0.0	0.0
5.2238	8.3333	700	3.6898	0.0002	0.0015	0.0	0.0002	-1.0	-1.0	0.0	0.0014	0.0086	0.0086	-1.0	-1.0	0.0009	0.0338	0.0	0.0	0.0	0.0006
3.8461	8.9286	750	3.7262	0.0002	0.001	0.0	0.0002	-1.0	-1.0	0.0	0.0014	0.0133	0.0133	-1.0	-1.0	0.0007	0.0532	0.0	0.0	0.0	0.0
3.2069	9.5238	800	3.7627	0.0001	0.0007	0.0	0.0001	-1.0	-1.0	0.0	0.0008	0.0082	0.0082	-1.0	-1.0	0.0005	0.0329	0.0	0.0	0.0	0.0
4.0347	10.1190	850	3.7339	0.0006	0.0028	0.0	0.0006	-1.0	-1.0	0.0	0.0027	0.0172	0.0172	-1.0	-1.0	0.0023	0.069	0.0	0.0	0.0	0.0
4.0595	10.7143	900	3.6200	0.0005	0.0025	0.0	0.0005	-1.0	-1.0	0.0	0.0019	0.0167	0.0167	-1.0	-1.0	0.0021	0.0667	0.0	0.0	0.0	0.0
4.2273	11.3095	950	3.6058	0.001	0.0059	0.0	0.001	-1.0	-1.0	0.0003	0.0027	0.0157	0.0157	-1.0	-1.0	0.0039	0.063	0.0	0.0	0.0	0.0
2.6219	11.9048	1000	3.5723	0.0011	0.0045	0.0	0.0012	-1.0	-1.0	0.0003	0.0031	0.0115	0.0115	-1.0	-1.0	0.0045	0.0458	0.0	0.0	0.0	0.0
3.5385	12.5	1050	3.5279	0.0018	0.0084	0.0002	0.0018	-1.0	-1.0	0.0002	0.0041	0.0233	0.0233	-1.0	-1.0	0.0072	0.0931	0.0	0.0	0.0	0.0
3.6905	13.0952	1100	3.5101	0.0008	0.0035	0.0001	0.0008	-1.0	-1.0	0.0	0.0022	0.0163	0.0163	-1.0	-1.0	0.0031	0.0653	0.0	0.0	0.0	0.0
3.0729	13.6905	1150	3.5698	0.0007	0.0032	0.0	0.0007	-1.0	-1.0	0.0	0.0024	0.0112	0.0112	-1.0	-1.0	0.0027	0.0449	0.0	0.0	0.0	0.0
3.6636	14.2857	1200	3.4950	0.0011	0.0052	0.0001	0.0011	-1.0	-1.0	0.0	0.0028	0.0199	0.0199	-1.0	-1.0	0.0043	0.0796	0.0	0.0	0.0	0.0
3.4026	14.8810	1250	3.4642	0.0019	0.0088	0.0001	0.0019	-1.0	-1.0	0.0003	0.0061	0.0266	0.0266	-1.0	-1.0	0.0074	0.1065	0.0	0.0	0.0	0.0
3.514	15.4762	1300	3.4471	0.0021	0.0074	0.0005	0.0021	-1.0	-1.0	0.0006	0.0047	0.0293	0.0293	-1.0	-1.0	0.0085	0.1171	0.0	0.0	0.0	0.0
4.4938	16.0714	1350	3.5414	0.0028	0.0095	0.0001	0.0028	-1.0	-1.0	0.0007	0.0043	0.023	0.023	-1.0	-1.0	0.0061	0.0898	0.005	0.0023	0.0	0.0
3.9956	16.6667	1400	3.4033	0.0031	0.012	0.0006	0.0031	-1.0	-1.0	0.0001	0.0084	0.0362	0.0362	-1.0	-1.0	0.0123	0.1449	0.0	0.0	0.0	0.0
4.1115	17.2619	1450	3.4297	0.0022	0.0107	0.0001	0.0022	-1.0	-1.0	0.0007	0.0046	0.0264	0.0264	-1.0	-1.0	0.0086	0.1056	0.0	0.0	0.0	0.0
3.0744	17.8571	1500	3.4763	0.0018	0.0071	0.0001	0.0018	-1.0	-1.0	0.0003	0.0063	0.028	0.028	-1.0	-1.0	0.0073	0.112	0.0	0.0	0.0	0.0
3.3757	18.4524	1550	3.4410	0.002	0.008	0.0004	0.002	-1.0	-1.0	0.0008	0.0066	0.0317	0.0317	-1.0	-1.0	0.008	0.1269	0.0	0.0	0.0	0.0
4.5711	19.0476	1600	3.4374	0.0024	0.0101	0.0003	0.0024	-1.0	-1.0	0.0001	0.0069	0.0322	0.0322	-1.0	-1.0	0.0096	0.1287	0.0	0.0	0.0	0.0
2.8704	19.6429	1650	3.4496	0.0018	0.0074	0.0004	0.0018	-1.0	-1.0	0.0007	0.0057	0.0252	0.0252	-1.0	-1.0	0.0073	0.1009	0.0	0.0	0.0	0.0
3.9042	20.2381	1700	3.3804	0.0029	0.0119	0.0004	0.0029	-1.0	-1.0	0.0002	0.0065	0.0306	0.0306	-1.0	-1.0	0.0117	0.1222	0.0	0.0	0.0	0.0
3.3289	20.8333	1750	3.4044	0.0021	0.0083	0.0	0.0021	-1.0	-1.0	0.001	0.0053	0.0238	0.0238	-1.0	-1.0	0.0086	0.0954	0.0	0.0	0.0	0.0
4.4952	21.4286	1800	3.4224	0.0014	0.0069	0.0001	0.0014	-1.0	-1.0	0.0009	0.003	0.0231	0.0231	-1.0	-1.0	0.0058	0.0926	0.0	0.0	0.0	0.0
3.7537	22.0238	1850	3.3736	0.0012	0.0055	0.0001	0.0012	-1.0	-1.0	0.0	0.0039	0.0223	0.0223	-1.0	-1.0	0.0047	0.0894	0.0	0.0	0.0	0.0
4.7113	22.6190	1900	3.5088	0.0011	0.0045	0.0002	0.0011	-1.0	-1.0	0.0	0.0038	0.0214	0.0214	-1.0	-1.0	0.0045	0.0856	0.0	0.0	0.0	0.0
3.6112	23.2143	1950	3.3937	0.0017	0.0097	0.0	0.0017	-1.0	-1.0	0.0008	0.0043	0.0227	0.0227	-1.0	-1.0	0.0066	0.0903	0.0	0.0	0.0	0.0006
4.0428	23.8095	2000	3.3784	0.0013	0.0054	0.0001	0.0013	-1.0	-1.0	0.0003	0.0045	0.0233	0.0233	-1.0	-1.0	0.0052	0.0931	0.0	0.0	0.0	0.0
4.7735	24.4048	2050	3.3649	0.0025	0.0101	0.0009	0.0025	-1.0	-1.0	0.0017	0.0082	0.03	0.03	-1.0	-1.0	0.0085	0.1116	0.0015	0.006	0.0	0.0024
4.2583	25.0	2100	3.3489	0.0009	0.0043	0.0001	0.0009	-1.0	-1.0	0.0005	0.0032	0.0168	0.0168	-1.0	-1.0	0.0034	0.0671	0.0	0.0	0.0	0.0
3.383	25.5952	2150	3.3785	0.0014	0.0066	0.0001	0.0014	-1.0	-1.0	0.0015	0.0037	0.0221	0.0221	-1.0	-1.0	0.0054	0.0884	0.0	0.0	0.0	0.0

📄 License

This project uses the apache - 2.0 license.

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