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--- |
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license: apache-2.0 |
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datasets: |
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- Panoramax/classified_fr_road_subsigns |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Road-Subsigns-Classification |
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- SigLIP2 |
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- Traffic |
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--- |
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# **Road-Subsigns-Classification** |
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> **Road-Subsigns-Classification** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify images of road subsigns using the **SiglipForImageClassification** architecture. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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M1 0.9907 0.9815 0.9860 324 |
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M11c1-E 1.0000 0.9787 0.9892 47 |
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M2 0.9950 0.9853 0.9901 204 |
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M3a-droite 0.9699 0.9680 0.9690 500 |
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M3a-gauche 0.9431 0.9375 0.9403 336 |
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M3b-gauche 1.0000 1.0000 1.0000 14 |
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M4a 0.9914 0.9664 0.9787 119 |
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M4b 0.8929 1.0000 0.9434 25 |
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M4c 0.8947 1.0000 0.9444 17 |
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M4d1 0.9887 1.0000 0.9943 175 |
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M4d2 0.9844 0.9844 0.9844 64 |
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M4f 0.9826 1.0000 0.9912 452 |
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M4g 0.9940 1.0000 0.9970 329 |
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M4h 0.0000 0.0000 0.0000 1 |
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M4u 0.8571 0.9231 0.8889 13 |
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M4v 1.0000 1.0000 1.0000 100 |
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M4z1 1.0000 1.0000 1.0000 45 |
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M4z2 0.0000 0.0000 0.0000 1 |
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M5-STOP 1.0000 0.9872 0.9935 234 |
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M6a 0.9940 0.9920 0.9930 500 |
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M6h 1.0000 0.9943 0.9972 353 |
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M6i 0.9885 1.0000 0.9942 86 |
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M6j 0.9855 1.0000 0.9927 68 |
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M8a 0.9619 0.9528 0.9573 106 |
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M8b 0.7407 0.9091 0.8163 22 |
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M8c 0.8485 0.9825 0.9106 57 |
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M8d 0.9739 0.9739 0.9739 115 |
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M8e 0.9754 0.9835 0.9794 121 |
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M8f 0.9972 0.9756 0.9863 369 |
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M9Z-INTERDIT-HORS-CASES 0.9787 0.9919 0.9852 370 |
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M9Z-SAUF-BUS 0.9650 0.9452 0.9550 146 |
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M9Z-SAUF-BUS-SCOLAIRE 0.9688 0.9394 0.9538 66 |
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M9c 0.9843 1.0000 0.9921 500 |
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M9d 0.9945 0.9759 0.9851 373 |
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M9v 0.9952 1.0000 0.9976 418 |
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M9z 0.7760 0.7132 0.7433 136 |
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M9z-DES-DEUX-COTES 0.9741 0.9496 0.9617 119 |
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M9z-ECOLE 1.0000 0.9474 0.9730 38 |
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M9z-PARKING-PRIVE 1.0000 1.0000 1.0000 9 |
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M9z-PASSAGE-SURELEVE 0.9808 0.9808 0.9808 104 |
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M9z-PROPRIETE-PRIVEE 0.9091 0.8333 0.8696 12 |
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M9z-RAPPEL 0.9933 0.9978 0.9955 447 |
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M9z-SAUF-CHANTIER 1.0000 0.7273 0.8421 11 |
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M9z-SAUF-CONVOIS-EXCEPT 0.0000 0.0000 0.0000 2 |
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M9z-SAUF-CYCLISTES 0.9626 0.9836 0.9730 183 |
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M9z-SAUF-DESSERTE 0.9307 0.9792 0.9543 96 |
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M9z-SAUF-LIVRAISONS 0.8478 0.9286 0.8864 42 |
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M9z-SAUF-POLICE 1.0000 0.8667 0.9286 15 |
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M9z-SAUF-RIVERAINS 0.9677 0.9615 0.9646 312 |
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M9z-SAUF-SERVICE 0.9160 0.9375 0.9266 128 |
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M9z-SAUF-TAXIS 0.7778 0.8235 0.8000 17 |
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M9z-SAUF-VEHICULES-AGRICOLES 0.9712 0.9018 0.9352 112 |
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M9z-SAUF-VEHICULES-AUTORISES 0.9253 0.9817 0.9527 164 |
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M9z-SECOURS 1.0000 0.6667 0.8000 9 |
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M9z-SIGNAL-AUTO 0.9892 0.9892 0.9892 93 |
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M9z-SORTIE-POMPIERS 0.9062 0.9355 0.9206 31 |
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M9z-SORTIE-VEHICULES 1.0000 0.7857 0.8800 14 |
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M9z-SUR-LE-TROTTOIR 0.9444 0.9444 0.9444 18 |
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M9z-VERGLAS 1.0000 0.6875 0.8148 16 |
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zz 0.9486 0.9600 0.9543 500 |
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accuracy 0.9732 9298 |
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macro avg 0.9093 0.8968 0.9009 9298 |
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weighted avg 0.9731 0.9732 0.9729 9298 |
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``` |
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The model categorizes road subsigns into 60 classes: |
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- **Class 0:** "M1" |
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- **Class 1:** "M11c1-E" |
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- **Class 2:** "M2" |
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- **Class 3:** "M3a-droite" |
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- **Class 4:** "M3a-gauche" |
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- **Class 5:** "M3b-gauche" |
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- **Class 6:** "M4a" |
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- **Class 7:** "M4b" |
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- **Class 8:** "M4c" |
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- **Class 9:** "M4d1" |
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- **Class 10:** "M4d2" |
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- **Class 11:** "M4f" |
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- **Class 12:** "M4g" |
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- **Class 13:** "M4h" |
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- **Class 14:** "M4u" |
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- **Class 15:** "M4v" |
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- **Class 16:** "M4z1" |
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- **Class 17:** "M4z2" |
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- **Class 18:** "M5-STOP" |
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- **Class 19:** "M6a" |
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- **Class 20:** "M6h" |
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- **Class 21:** "M6i" |
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- **Class 22:** "M6j" |
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- **Class 23:** "M8a" |
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- **Class 24:** "M8b" |
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- **Class 25:** "M8c" |
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- **Class 26:** "M8d" |
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- **Class 27:** "M8e" |
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- **Class 28:** "M8f" |
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- **Class 29:** "M9Z-INTERDIT-HORS-CASES" |
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- **Class 30:** "M9Z-SAUF-BUS" |
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- **Class 31:** "M9Z-SAUF-BUS-SCOLAIRE" |
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- **Class 32:** "M9c" |
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- **Class 33:** "M9d" |
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- **Class 34:** "M9v" |
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- **Class 35:** "M9z" |
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- **Class 36:** "M9z-DES-DEUX-COTES" |
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- **Class 37:** "M9z-ECOLE" |
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- **Class 38:** "M9z-PARKING-PRIVE" |
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- **Class 39:** "M9z-PASSAGE-SURELEVE" |
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- **Class 40:** "M9z-PROPRIETE-PRIVEE" |
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- **Class 41:** "M9z-RAPPEL" |
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- **Class 42:** "M9z-SAUF-CHANTIER" |
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- **Class 43:** "M9z-SAUF-CONVOIS-EXCEPT" |
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- **Class 44:** "M9z-SAUF-CYCLISTES" |
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- **Class 45:** "M9z-SAUF-DESSERTE" |
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- **Class 46:** "M9z-SAUF-LIVRAISONS" |
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- **Class 47:** "M9z-SAUF-POLICE" |
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- **Class 48:** "M9z-SAUF-RIVERAINS" |
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- **Class 49:** "M9z-SAUF-SERVICE" |
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- **Class 50:** "M9z-SAUF-TAXIS" |
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- **Class 51:** "M9z-SAUF-VEHICULES-AGRICOLES" |
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- **Class 52:** "M9z-SAUF-VEHICULES-AUTORISES" |
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- **Class 53:** "M9z-SECOURS" |
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- **Class 54:** "M9z-SIGNAL-AUTO" |
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- **Class 55:** "M9z-SORTIE-POMPIERS" |
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- **Class 56:** "M9z-SORTIE-VEHICULES" |
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- **Class 57:** "M9z-SUR-LE-TROTTOIR" |
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- **Class 58:** "M9z-VERGLAS" |
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- **Class 59:** "zz" |
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# **Run with Transformers🤗** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```py |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/Road-Subsigns-Classification" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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labels = { |
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"0": "M1", "1": "M11c1-E", "2": "M2", "3": "M3a-droite", "4": "M3a-gauche", |
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"5": "M3b-gauche", "6": "M4a", "7": "M4b", "8": "M4c", "9": "M4d1", |
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"10": "M4d2", "11": "M4f", "12": "M4g", "13": "M4h", "14": "M4u", |
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"15": "M4v", "16": "M4z1", "17": "M4z2", "18": "M5-STOP", "19": "M6a", |
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"20": "M6h", "21": "M6i", "22": "M6j", "23": "M8a", "24": "M8b", |
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"25": "M8c", "26": "M8d", "27": "M8e", "28": "M8f", "29": "M9Z-INTERDIT-HORS-CASES", |
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"30": "M9Z-SAUF-BUS", "31": "M9Z-SAUF-BUS-SCOLAIRE", "32": "M9c", "33": "M9d", "34": "M9v", |
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"35": "M9z", "36": "M9z-DES-DEUX-COTES", "37": "M9z-ECOLE", "38": "M9z-PARKING-PRIVE", |
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"39": "M9z-PASSAGE-SURELEVE", "40": "M9z-PROPRIETE-PRIVEE", "41": "M9z-RAPPEL", |
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"42": "M9z-SAUF-CHANTIER", "43": "M9z-SAUF-CONVOIS-EXCEPT", "44": "M9z-SAUF-CYCLISTES", |
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"45": "M9z-SAUF-DESSERTE", "46": "M9z-SAUF-LIVRAISONS", "47": "M9z-SAUF-POLICE", |
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"48": "M9z-SAUF-RIVERAINS", "49": "M9z-SAUF-SERVICE", "50": "M9z-SAUF-TAXIS", |
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"51": "M9z-SAUF-VEHICULES-AGRICOLES", "52": "M9z-SAUF-VEHICULES-AUTORISES", "53": "M9z-SECOURS", |
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"54": "M9z-SIGNAL-AUTO", "55": "M9z-SORTIE-POMPIERS", "56": "M9z-SORTIE-VEHICULES", |
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"57": "M9z-SUR-LE-TROTTOIR", "58": "M9z-VERGLAS", "59": "zz" |
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} |
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def classify_subsign(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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return {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=classify_subsign, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="Road Subsigns Classification", |
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description="Upload an image to predict the road subsign category." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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# **Intended Use:** |
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The **Road-Subsigns-Classification** model is designed to classify images of road subsigns into 60 categories. Potential use cases include: |
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- **Traffic Management:** Assisting in automated monitoring and analysis of road signs. |
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- **Autonomous Vehicles:** Helping vehicles understand road sign information. |
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- **Smart Cities:** Enhancing traffic regulation systems. |
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- **Driver Assistance Systems:** Providing visual cues for safer driving. |
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- **Urban Planning:** Analyzing road sign data for infrastructure improvements. |
