Ddpm Derm C1 Seed2

--- license: cc-by-nc-4.0 library_name: pytorch pipeline_tag: image-classification tags: - ham10000 - resnet18 - medical-imaging - non-commercial --- # 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 `df` F1: 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](https://creativecommons...

--- license: cc-by-nc-4.0 library_name: pytorch pipeline_tag: image-classification tags: - ham10000 - resnet18 - medical-imaging - non-commercial --- # HAM10000 ResNet-18 — C1@585 seed 2 This repository contains the deployment 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 `df` F1: 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` ## File integrity - File: `best.pt` - SHA-256: `c55134294687a753cff44ac5bac5a4896f85adde1be943697feab08129350752` The demo validates this digest and the 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](https://creativecommons.org/licenses/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](https://doi.org/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 de...

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