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---
language: en
license: apache-2.0
tags:
- image-classification
- medical
- dermatology
- skin-disease
- ensemble
datasets:
- merolavtechnology/dermnet-skin40-cleaned-dataset
metrics:
- accuracy
- f1
---
# DermNet-Skin23 — ConvNeXt-V1-XL @ 384
ConvNeXt-V1-XL fine-tuned on a 23-class consolidation of DermNet + Skin40, paired with
[iamcode6/dermnet-skin23-eva02](https://huggingface.co/iamcode6/dermnet-skin23-eva02) for cross-architecture ensembling.
## Results
Single best (EMA): 80.47% acc / 0.7843 macro F1.
5-model cross-architecture ensemble (2× EVA-02-L + 3× ConvNeXt-V1-XL) with 4-aug TTA: **82.86% acc / 0.8113 macro F1**.
## Dataset
Source: merolavtechnology/dermnet-skin40-cleaned-dataset on Kaggle. The 40 fine-grained Skin40 categories were consolidated into 23 broader Dermnet buckets.
Final: 17,557 train / 3,856 test.
## Training
- Hardware: AMD Instinct MI300X (192 GB HBM3), ROCm 7.0
- Backbone: convnext_xlarge.fb_in22k_ft_in1k_384 (~350M params)
- 25 epochs, batch 64, AdamW, cosine LR with 10% warmup, peak LR=1.1e-4
- Mixup α=0.1 + Cutmix α=0.5 at prob=0.5; off in last 20% of epochs
- WeightedRandomSampler with effective-number weights
- EMA decay=0.999, SWA over last 20%, bf16 autocast
## Notes
ConvNeXt V2-Huge was tried first but is bf16-unstable on long runs (GRN issue) — V1-XL is the reliable choice. EMA decay of 0.9999 was too slow for a 25-epoch
fine-tune from a fresh head; 0.999 fixes it.
README |