Image Classification
Transformers
Safetensors
English
mobilevit
sft
vision
medical-imaging
chest-xray
pneumonia
covid19
tuberculosis
Instructions to use Jesteban247/mobilevit_small-chest_xray with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jesteban247/mobilevit_small-chest_xray with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Jesteban247/mobilevit_small-chest_xray") 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("Jesteban247/mobilevit_small-chest_xray") model = AutoModelForImageClassification.from_pretrained("Jesteban247/mobilevit_small-chest_xray") - Notebooks
- Google Colab
- Kaggle
π« MobileViT-Small β Chest X-Ray Classifier (LoRA Fine-tune)
This model is a LoRA fine-tuned version of apple/mobilevit-small
for chest X-ray classification on Chest X-ray Pneumonia, COVID-19 & Tuberculosis dataset.
π§© Configuration
| Attribute | Value |
|---|---|
| Base Model | apple/mobilevit-small |
| Fine-tuning Method | LoRA (Low-Rank Adaptation) |
| Dataset | Chest X-ray Pneumonia/COVID-19/Tuberculosis |
| Classes | COVID19, NORMAL, PNEUMONIA, TUBERCULOSIS |
| Epochs | 10 |
| Batch Size | 32 |
| Learning Rate | 0.0005 |
| Optimizer | AdamW |
| LoRA Config | r=4, Ξ±=32, dropout=0.1, target_modules=[query, value] |
| Trainable Params | 30.9K / 4.97M (0.62%) |
π« Example Image
π Evaluation Results
| Metric | Avg | COVID19 | NORMAL | PNEUMONIA | TUBERCULOSIS |
|---|---|---|---|---|---|
| Accuracy | 0.9798 | 0.9909 | 0.9661 | 0.9648 | 0.9974 |
| Precision | 0.9616 | 0.9640 | 0.9130 | 0.9696 | 1.0000 |
| Recall | 0.9591 | 0.9727 | 0.9245 | 0.9673 | 0.9718 |
| F1 Score | 0.9603 | 0.9683 | 0.9188 | 0.9684 | 0.9857 |
| AUC | 0.9968 | 0.9991 | 0.9927 | 0.9959 | 0.9997 |
Test loss: 0.0938ββInference time: 0.476 s
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apple/mobilevit-small