| --- |
| library_name: transformers |
| tags: |
| - image-classification |
| - vision |
| - roadwork-detection |
| - bittensor |
| - natix-subnet |
| --- |
| |
| # roadwork_v3_focal |
|
|
| Vision Transformer (ViT) model for binary roadwork detection, trained for Natix Subnet 72. |
|
|
| ## Model Details |
|
|
| - **Base Model**: google/vit-base-patch16-224-in21k |
| - **Architecture**: ViT-Base (86M parameters) |
| - **Input Size**: 224×224 |
| - **Output Classes**: 2 (None, Roadwork) |
|
|
| ## Performance |
|
|
| | Metric | Value | |
| |---|---| |
| | Accuracy | 0.9839 | |
| | MCC | 0.9443 | |
| | F1 Score | 0.9903 | |
| | AUC | 0.9950 | |
| | Subnet Reward | 0.8962 | |
|
|
| ## Training Details |
|
|
| - **Training Samples**: 34,968 |
| - **Validation Samples**: 4,418 |
| - **Validator Augmentations**: True |
| - **Label Smoothing**: 0.05 |
| - **Confidence Margin**: 0.1 |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoImageProcessor, AutoModelForImageClassification |
| from PIL import Image |
| |
| processor = AutoImageProcessor.from_pretrained("infinite000/in-20001") |
| model = AutoModelForImageClassification.from_pretrained("infinite000/in-20001") |
| |
| image = Image.open("your_image.jpg") |
| inputs = processor(image, return_tensors="pt") |
| outputs = model(**inputs) |
| probs = outputs.logits.softmax(dim=1) |
| roadwork_prob = probs[0][1].item() |
| ``` |
|
|
| ## Model Card |
|
|
| See `model_card.json` for detailed metadata. |
|
|
