HAM10000 ResNet-18 - C1@585 seed 2
This repository contains the formal experiment checkpoint best.pt for an educational,
non-commercial portfolio demonstration. It is not a medical device and must not
be used for diagnosis, treatment, triage, or other clinical decisions.
Model selection
- Architecture: ResNet-18
- Variant: C1@585
- Seed: 2
- Selection rule: highest validation dermatofibroma (
df) F1 among the completed C1@585 seeds 0, 1, and 2 - Seed-2 validation
dfF1: 0.740741 - Test metrics were not used to choose this deployment checkpoint
The matched-585 formal experiment averaged across three seeds produced
df F1 0.660 ± 0.042, macro-F1 0.651 ± 0.021, and df recall
0.604 ± 0.029. These fixed-split results are suggestive only and are not a
claim of statistical significance, clinical validation, or generalization to
other populations.
Input and classes
Images are converted to RGB, resized to 128 × 128, converted to a tensor, and
normalized with ImageNet mean [0.485, 0.456, 0.406] and standard deviation
[0.229, 0.224, 0.225].
Class order:
akiec, bcc, bkl, df, mel, nv, vasc
Published files and integrity
- Formal experiment checkpoint:
best.pt - Formal checkpoint SHA-256:
c55134294687a753cff44ac5bac5a4896f85adde1be943697feab08129350752 - Deployment-only checkpoint:
deploy_weights.pt - Deployment checkpoint SHA-256:
85910296186433c6cdad2d82368646c983b586ec861d605c1338317fb8306a53 - Deployment manifest:
model_manifest.json
deploy_weights.pt is derived from the formal checkpoint and contains only
model_state_dict, config, and class_to_idx; optimizer and training-history
state are omitted to reduce CPU deployment memory. No weights were retrained or
changed. The deployment manifest records both digests, and the demo validates
the deployment digest and checkpoint metadata before serving predictions.
Training data, license, and attribution
The model was trained on the HAM10000 dataset using a fixed lesion-ID group split. HAM10000 Dataset © ViDIR Group, Department of Dermatology, Medical University of Vienna. The ISIC 2018 data page distributes the dataset under CC BY-NC 4.0.
Reference: Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data 5, 180161 (2018). doi:10.1038/sdata.2018.161
Modifications include a fixed train/validation/test split, resizing, normalization, class rebalancing for C1@585, and model training. No endorsement by the original dataset creators or ISIC is implied.
Limitations
The model may be wrong. HAM10000 and the fixed test split do not represent all
devices, acquisition settings, skin tones, lesion types, or patient populations.
The test set contains few df cases, so per-class estimates are uncertain.