Create README.md

README.md

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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