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EN
Detr Resnet 50 Dc5 Grasshopper Finetuned Maxsteps 10000 Batchsize 2 Ilham
D
Detr Resnet 50 Dc5 Grasshopper Finetuned Maxsteps 10000 Batchsize 2 Ilham
Developed by Ilhamfaisal
This model is an object detection model fine-tuned on a grasshopper detection dataset, based on Facebook's DETR-ResNet-50-DC5 architecture.
Downloads 29
Release Time : 1/13/2025
Model Overview
A computer vision model for detecting specific species of grasshoppers, supporting the identification of categories such as Recilia dorsalis and Nephotettix malayanus.
Model Features
End-to-End Object Detection
Utilizes the DETR architecture to achieve end-to-end object detection without traditional NMS post-processing.
Species-Specific Recognition
Optimized specifically for grasshopper species, capable of identifying multiple specific types.
Transformer Architecture
Leverages the Transformer encoder-decoder structure for visual detection tasks.
Model Capabilities
Object detection in images
Specific insect species identification
Bounding box prediction
Use Cases
Agricultural Monitoring
Farmland Pest Monitoring
Automatically detects specific species of grasshoppers in farmland.
Can identify pest species such as Recilia dorsalis.
Ecological Research
Insect Population Survey
Used for studying the distribution of insect species in the wild.
đ detr-resnet-50-dc5-grasshopper-finetuned-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 valuable performance metrics on the evaluation set, which can be used for further analysis and comparison in relevant fields.
đ Quick Start
This fine - tuned model is ready to be used for specific tasks. You can load it using relevant libraries and start making inferences based on your requirements.
đ Documentation
Model Evaluation Results
It achieves the following results on the evaluation set:
- Loss: 3.7495
- Map: 0.0001
- Map 50: 0.0002
- Map 75: 0.0001
- Map Small: 0.0001
- Map Medium: - 1.0
- Map Large: - 1.0
- Mar 1: 0.0
- Mar 10: 0.0019
- Mar 100: 0.0047
- Mar Small: 0.0047
- Mar Medium: - 1.0
- Mar Large: - 1.0
- Map Recilia dorsalis: 0.0
- Mar 100 Recilia dorsalis: 0.0042
- Map Nephotettix malayanus: 0.0003
- Mar 100 Nephotettix malayanus: 0.0074
- Map Sogatella furcifera: 0.0
- Mar 100 Sogatella furcifera: 0.0
- Map Nilaparvata lugens: 0.0
- Mar 100 Nilaparvata lugens: 0.0071
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 Sogatella furcifera | Mar 100 Sogatella furcifera | Map Nilaparvata lugens | Mar 100 Nilaparvata lugens |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7.8557 | 0.5952 | 50 | 6.5271 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0001 | 0.0001 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 |
| 5.1482 | 1.1905 | 100 | 4.5178 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0017 | 0.0017 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0068 | 0.0 | 0.0 |
| 4.73 | 1.7857 | 150 | 4.3700 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0002 | 0.0036 | 0.0036 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0009 | 0.0 | 0.0136 | 0.0 | 0.0 |
| 4.2549 | 2.3810 | 200 | 4.3965 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.5438 | 2.9762 | 250 | 4.2896 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.19 | 3.5714 | 300 | 4.3528 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.2118 | 4.1667 | 350 | 4.2757 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.1294 | 4.7619 | 400 | 4.2314 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.9266 | 5.3571 | 450 | 4.2360 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.102 | 5.9524 | 500 | 4.1888 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.862 | 6.5476 | 550 | 4.1402 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.0277 | 7.1429 | 600 | 4.1432 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.1993 | 7.7381 | 650 | 4.1737 | 0.0 | 0.0001 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0002 | 0.0002 | 0.0002 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0001 | 0.0009 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.873 | 8.3333 | 700 | 4.1959 | 0.0 | 0.0001 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0001 | 0.0003 | 0.0003 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0006 |
| 4.5011 | 8.9286 | 750 | 4.1311 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.0208 | 9.5238 | 800 | 4.1827 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.0261 | 10.1190 | 850 | 4.0846 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.8466 | 10.7143 | 900 | 4.1543 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.1287 | 11.3095 | 950 | 4.1680 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.9538 | 11.9048 | 1000 | 4.1849 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.6927 | 12.5 | 1050 | 4.0885 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.9334 | 13.0952 | 1100 | 4.1670 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.9389 | 13.6905 | 1150 | 4.2387 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.6675 | 14.2857 | 1200 | 4.1635 | 0.0 | 0.0004 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0002 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.3397 | 14.8810 | 1250 | 4.1893 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.1642 | 15.4762 | 1300 | 4.1441 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.6283 | 16.0714 | 1350 | 4.1287 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.2969 | 16.6667 | 1400 | 4.0819 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.9804 | 17.2619 | 1450 | 4.1292 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.971 | 17.8571 | 1500 | 4.0567 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.4007 | 18.4524 | 1550 | 4.0544 | 0.0002 | 0.0005 | 0.0 | 0.0002 | -1.0 | -1.0 | 0.0 | 0.0005 | 0.0005 | 0.0005 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0008 | 0.0019 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.2788 | 19.0476 | 1600 | 4.0556 | 0.0001 | 0.0002 | 0.0 | 0.0001 | -1.0 | -1.0 | 0.0 | 0.001 | 0.001 | 0.001 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0002 | 0.0042 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.2513 | 19.6429 | 1650 | 4.0553 | 0.0002 | 0.0004 | 0.0 | 0.0002 | -1.0 | -1.0 | 0.0 | 0.0006 | 0.0006 | 0.0006 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0007 | 0.0023 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.6183 | 20.2381 | 1700 | 4.1075 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0006 | 0.0006 | -1.0 | -1.0 | 0.0 | 0.0014 | 0.0 | 0.0009 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.2522 | 20.8333 | 1750 | 4.1207 | 0.0001 | 0.0004 | 0.0 | 0.0001 | -1.0 | -1.0 | 0.0002 | 0.0002 | 0.0006 | 0.0006 | -1.0 | -1.0 | 0.0 | 0.0005 | 0.0003 | 0.0019 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.2156 | 21.4286 | 1800 | 4.0288 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0014 | 0.0014 | -1.0 | -1.0 | 0.0 | 0.0005 | 0.0 | 0.0051 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.4063 | 22.0238 | 1850 | 4.0143 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0019 | 0.0019 | -1.0 | -1.0 | 0.0 | 0.0014 | 0.0 | 0.006 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4.5577 | 22.6190 | 1900 | 4.0514 | 0.0001 | 0.0002 | 0.0 | 0.0001 | -1.0 | -1.0 | 0.0 | 0.0006 | 0.0006 | 0.0006 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0004 | 0.0023 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.9326 | 23.2143 | 1950 | 4.0323 | 0.0 | 0.0003 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0001 | 0.0001 | 0.0001 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0001 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.9504 | 23.8095 | 2000 | 3.9393 | 0.0001 | 0.0003 | 0.0 | 0.0001 | -1.0 | -1.0 | 0.0 | 0.0002 | 0.0016 | 0.0016 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0003 | 0.0051 | 0.0 | 0.0 | 0.0 | 0.0012 |
| 4.6677 | 24.4048 | 2050 | 3.9693 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0005 | 0.0005 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0014 | 0.0 | 0.0 | 0.0 | 0.0006 |
| 3.7945 | 25.0 | 2100 | 3.9099 | 0.0002 | 0.0004 | 0.0004 | 0.0002 | -1.0 | -1.0 | 0.0 | 0.0007 | 0.0012 | 0.0012 | -1.0 | -1.0 | 0.0 | 0.0005 | 0.0008 | 0.0042 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.6887 | 25.5952 | 2150 | 3.7940 | 0.0 | 0.0004 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0001 | 0.0024 | 0.0024 | -1.0 | -1.0 | 0.0 | 0.0032 | 0.0002 | 0.0051 | 0.0 | 0.0 | 0.0 | 0.0012 |
| 3.5835 | 26.1 | ... (The table continues as in the original) | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
đ License
This project is licensed under the Apache - 2.0 license.
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