Instructions to use chanc031965/Tesla_Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chanc031965/Tesla_Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="chanc031965/Tesla_Detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("chanc031965/Tesla_Detection") model = AutoModelForImageClassification.from_pretrained("chanc031965/Tesla_Detection") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: zjs81/Electric-Car-Brand-Classifier | |
| tags: | |
| - image-classification | |
| - transformers | |
| - binary-classification | |
| metrics: | |
| - accuracy | |
| - recall | |
| - f1 | |
| pipeline_tag: image-classification | |
| # Tesla Binary Image Classifier | |
| A fine-tuned image classification model that answers one question: | |
| **Is this car a Tesla? (Yes / No)** | |
| ## Model Description | |
| This model is fine-tuned from | |
| [zjs81/Electric-Car-Brand-Classifier](https://huggingface.co/zjs81/Electric-Car-Brand-Classifier) | |
| on a custom Tesla vs. Not Tesla image dataset. | |
| The classification head was replaced and retrained for binary classification, | |
| optimising for **Tesla Recall** to minimise missed detections. | |
| ## Training Details | |
| | Parameter | Value | | |
| |------------------|------------------------------------| | |
| | Base Model | zjs81/Electric-Car-Brand-Classifier | | |
| | Task | Binary Image Classification | | |
| | Classes | `tesla`, `not_tesla` | | |
| | Learning Rate | 2e-5 | | |
| | Batch Size | 8 | | |
| | Epochs | 5 (best at Epoch 4) | | |
| | Optimizer Metric | Tesla Recall | | |
| | Inference Threshold | 0.6 | | |
| ## Evaluation Results (Test Set) | |
| | Metric | Score | | |
| |---------------------|--------| | |
| | Accuracy | 95.0% | | |
| | Precision (Tesla) | 93.3% | | |
| | Recall (Tesla) | 97.0% | | |
| | F1 Score (Tesla) | 95.1% | | |
| | F1 Score (Not Tesla)| 94.9% | |