Image Classification
timm
English
medical-imaging
knee-mri
acl-tear-detection
deep-learning
convnext
self-attention
masked-slice-modeling
radiology
orthopedics
Eval Results (legacy)
Instructions to use shareefch1413/ACL-LKNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use shareefch1413/ACL-LKNet with timm:
import timm model = timm.create_model("hf-hub:shareefch1413/ACL-LKNet", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download benchmark_metrics/cv_progress.json from shareefch1413/ACL-LKNet: direct link, hf CLI and curl.
- Browser
- Download file 610 Bytes
-
https://huggingface.co/shareefch1413/ACL-LKNet/resolve/main/benchmark_metrics/cv_progress.json
- Command line
-
hf download hf://shareefch1413/ACL-LKNet/benchmark_metrics/cv_progress.json
-
curl -L -o cv_progress.json https://huggingface.co/shareefch1413/ACL-LKNet/resolve/main/benchmark_metrics/cv_progress.json
610 Bytes
| [ | |
| { | |
| "auroc": 0.9086438923395445, | |
| "auprc": 0.7110100982131922, | |
| "accuracy": 0.8805309734513275, | |
| "balanced_acc": 0.7428830227743272, | |
| "f1": 0.6197183098591549, | |
| "mcc": 0.5649741787757137, | |
| "sensitivity": 0.5238095238095238, | |
| "specificity": 0.9619565217391305, | |
| "fold": 4 | |
| }, | |
| { | |
| "auroc": 0.8667831262939958, | |
| "auprc": 0.6765005627552587, | |
| "accuracy": 0.8628318584070797, | |
| "balanced_acc": 0.7320134575569358, | |
| "f1": 0.5866666666666667, | |
| "mcc": 0.5111414540586645, | |
| "sensitivity": 0.5238095238095238, | |
| "specificity": 0.9402173913043478, | |
| "fold": 5 | |
| } | |
| ] |