Update README.md

README.md

@@ -29,7 +29,7 @@ size_categories:
 [![License](https://img.shields.io/badge/License-Mixed%20%2F%20Other-lightgrey)](#licensing)
 
 **Prepared for the bachelor thesis  
-_A Study of “Stochastic Depth” for Regularizing Residual Convolutional Neural Networks_**
+_A Study of “Stochastic Depth” for Regularizing Residual Convolutional Neural Networks_**
 
 </div>
 
@@ -46,7 +46,7 @@ The preparation pipeline was designed for reproducibility:
 - unreadable files are recorded;
 - exact duplicates are detected deterministically;
 - duplicate handling is documented separately for every dataset;
-- large images are converted to a common RGB `224 × 224` representation;
+- large images are converted to a common RGB `224 Г— 224` representation;
 - training, validation, and test membership is recorded in machine-readable manifests;
 - dataset-specific RGB normalization statistics are estimated only from each final training split and stored in `normalization.json`;
 - validation and test images are never used to estimate normalization parameters;
@@ -56,15 +56,31 @@ The source code used to prepare and load the datasets is available in the accomp
 
 > **Code:** https://github.com/gerageragera39/A-Study-of-Stochastic-Depth-for-Regularizing-Residual-Convolutional-Neural-Networks
 
+## Published thesis results
+
+In addition to the prepared datasets, this repository contains the experimental artifacts reported in the thesis under [`results/`](./results/).
+
+All final main experiments used one NVIDIA A40 GPU per SLURM task, batch size 256, 300 epochs, `pL = 0.5`, and training seeds `20917`, `723421`, and `901344`. Each dataset includes four conditions: `no_sd`, `sd_per_batch`, `sd_per_sample`, and `dropout`.
+
+| Dataset | Architecture | Precision |
+|---|---|---|
+| CIFAR-100 | CIFAR-style full pre-activation ResNet-110 | FP32 |
+| Caltech-256 | full pre-activation ResNet-101 | BF16 CUDA autocast without gradient scaling |
+| Google Scraped Image Dataset | full pre-activation ResNet-101 | BF16 CUDA autocast without gradient scaling |
+| Oxford Flowers-102 | full pre-activation ResNet-101 | FP32 |
+| Stanford Dogs | full pre-activation ResNet-101 | FP32 |
+
+The separate CIFAR-100 bias–variance analysis is stored under [`results/bias_variance/cifar100/resnet110/batch_256/fp32/pL_0.5/`](./results/bias_variance/cifar100/resnet110/batch_256/fp32/pL_0.5/). It uses the 45,000/5,000 training/validation split, excludes the official test set from the diagnostic analysis, and compares `no_sd` with `sd_per_sample` over seeds `111111`, `129143`, `222222`, `363801`, and `548357`.
+
 ## Dataset overview
 
 | Dataset ID | Benchmark | Classes | Experimental split | Stored resolution | Main preparation rule |
 |---|---:|---:|---|---:|---|
-| `cifar100` | CIFAR-100 | 100 | 45,000 train / 5,000 validation / 10,000 official test | `32 × 32` | Official test preserved; exact-image anomalies are diagnostic only |
-| `caltech256` | Caltech-256 | 256 | 20,839 train / 4,510 validation / 4,414 test | `224 × 224` RGB | Clutter excluded; exact duplicates handled before splitting |
-| `google4` | Google Scraped Image Dataset | 4 | 27,298 train / 3,413 validation / 3,412 test | `224 × 224` RGB | Source train/test folders merged; class aliases normalized |
-| `oxford102` | Oxford Flowers-102 | 102 | 6,551 train / 818 validation / 819 test | `224 × 224` RGB | Cross-split exact duplicates removed without random resplitting |
-| `stanford120` | Stanford Dogs | 120 | 10,802 train / 1,198 validation / 8,580 official test | `224 × 224` RGB | Official metadata required; test membership is preserved |
+| `cifar100` | CIFAR-100 | 100 | 45,000 train / 5,000 validation / 10,000 official test | `32 Г— 32` | Official test preserved; exact-image anomalies are diagnostic only |
+| `caltech256` | Caltech-256 | 256 | 20,839 train / 4,510 validation / 4,414 test | `224 Г— 224` RGB | Clutter excluded; exact duplicates handled before splitting |
+| `google4` | Google Scraped Image Dataset | 4 | 27,298 train / 3,413 validation / 3,412 test | `224 Г— 224` RGB | Source train/test folders merged; class aliases normalized |
+| `oxford102` | Oxford Flowers-102 | 102 | 6,551 train / 818 validation / 819 test | `224 Г— 224` RGB | Cross-split exact duplicates removed without random resplitting |
+| `stanford120` | Stanford Dogs | 120 | 10,802 train / 1,198 validation / 8,580 official test | `224 Г— 224` RGB | Official metadata required; test membership is preserved |
 
 The final counts above are also stored in each dataset's `split_summary.csv` and `split_config.json`. These generated files are the authoritative source for the published release.
 
@@ -79,12 +95,12 @@ For Caltech-256, Google-4, Oxford Flowers-102, and Stanford Dogs:
 1. verify that the image can be decoded;
 2. convert it to RGB;
 3. resize with bilinear interpolation so that the shorter side becomes 224 pixels;
-4. take a centered `224 × 224` crop;
-5. preserve already compatible RGB `224 × 224` files without unnecessary re-encoding.
+4. take a centered `224 Г— 224` crop;
+5. preserve already compatible RGB `224 Г— 224` files without unnecessary re-encoding.
 
 ### Dataset-specific normalization and online augmentation
 
-The thesis models for the four `224 × 224` datasets are trained from randomly initialized weights. No ImageNet-pretrained parameters are used, and ImageNet normalization constants are not part of the final protocol.
+The thesis models for the four `224 Г— 224` datasets are trained from randomly initialized weights. No ImageNet-pretrained parameters are used, and ImageNet normalization constants are not part of the final protocol.
 
 Instead, each higher-resolution dataset has its own `normalization.json`. The channel-wise mean and population standard deviation were calculated from the **final prepared training split only**, after deterministic RGB conversion, resizing, and center cropping, but before random augmentation. Validation and test images were not used to estimate these values.
 
@@ -153,7 +169,7 @@ def make_224_transforms(
     return train_transform, eval_transform
 ```
 
-CIFAR-100 uses a separate `32 × 32` pipeline and its established CIFAR-100 channel statistics:
+CIFAR-100 uses a separate `32 Г— 32` pipeline and its established CIFAR-100 channel statistics:
 
 ```python
 from torchvision import transforms
@@ -232,7 +248,7 @@ Each prepared dataset is accompanied by machine-readable metadata. Depending on
 | `split_summary.csv` | Per-class sample counts for each split |
 | `class_to_idx.json` | Deterministic mapping from class names to integer labels |
 | `split_config.json` | Split policy, seed, image-preparation settings, counts, warnings, and dataset-specific decisions |
-| `normalization.json` | Training-split RGB mean and population standard deviation used by the `224 × 224` data loader |
+| `normalization.json` | Training-split RGB mean and population standard deviation used by the `224 Г— 224` data loader |
 | `corrupt_images.json` | Source files that could not be decoded |
 | `duplicates.json` | Detected same-label exact-duplicate groups |
 | `label_conflicts.json` | Exact hashes observed under more than one label |
@@ -241,7 +257,7 @@ Each prepared dataset is accompanied by machine-readable metadata. Depending on
 | `excluded_conflicts.csv` | Files excluded because of cross-label exact conflicts |
 | `train_indices.npy` / `val_indices.npy` | Deterministic CIFAR-100 split indices |
 
-An “exact duplicate” in the folder-based datasets means an identical SHA-256 hash of the source file. This method does not detect perceptually similar images that were recompressed, resized, cropped, rotated, or otherwise modified.
+An “exact duplicate” in the folder-based datasets means an identical SHA-256 hash of the source file. This method does not detect perceptually similar images that were recompressed, resized, cropped, rotated, or otherwise modified.
 
 ## Repository layout
 
@@ -249,37 +265,37 @@ The preparation code produces the following logical layout:
 
 ```text
 data/
-├── processed/
-│   ├── cifar100/
-│   │   └── cifar-100-python/
-│   ├── caltech256/
-│   │   ├── train/<class>/*
-│   │   ├── val/<class>/*
-│   │   └── test/<class>/*
-│   ├── google4/
-│   │   ├── train/<class>/*
-│   │   ├── val/<class>/*
-│   │   └── test/<class>/*
-│   ├── oxford102/
-│   │   ├── train/<class>/*
-│   │   ├── val/<class>/*
-│   │   └── test/<class>/*
-│   └── stanford120/
-│       ├── train/<class>/*
-│       ├── val/<class>/*
-│       └── test/<class>/*
-└── splits/
-    ├── cifar100/
-    │   ├── train_indices.npy
-    │   └── val_indices.npy
-    ├── caltech256/
-    │   └── normalization.json
-    ├── google4/
-    │   └── normalization.json
-    ├── oxford102/
-    │   └── normalization.json
-    └── stanford120/
-        └── normalization.json
+в”њв”Ђв”Ђ processed/
+в”‚   в”њв”Ђв”Ђ cifar100/
+в”‚   в”‚   в””в”Ђв”Ђ cifar-100-python/
+в”‚   в”њв”Ђв”Ђ caltech256/
+в”‚   в”‚   в”њв”Ђв”Ђ train/<class>/*
+в”‚   в”‚   в”њв”Ђв”Ђ val/<class>/*
+в”‚   в”‚   в””в”Ђв”Ђ test/<class>/*
+в”‚   в”њв”Ђв”Ђ google4/
+в”‚   в”‚   в”њв”Ђв”Ђ train/<class>/*
+в”‚   в”‚   в”њв”Ђв”Ђ val/<class>/*
+в”‚   в”‚   в””в”Ђв”Ђ test/<class>/*
+в”‚   в”њв”Ђв”Ђ oxford102/
+в”‚   в”‚   в”њв”Ђв”Ђ train/<class>/*
+в”‚   в”‚   в”њв”Ђв”Ђ val/<class>/*
+в”‚   в”‚   в””в”Ђв”Ђ test/<class>/*
+в”‚   в””в”Ђв”Ђ stanford120/
+в”‚       в”њв”Ђв”Ђ train/<class>/*
+в”‚       в”њв”Ђв”Ђ val/<class>/*
+в”‚       в””в”Ђв”Ђ test/<class>/*
+в””в”Ђв”Ђ splits/
+    в”њв”Ђв���Ђ cifar100/
+    в”‚   в”њв”Ђв”Ђ train_indices.npy
+    в”‚   в””в”Ђв”Ђ val_indices.npy
+    в”њв”Ђв”Ђ caltech256/
+    в”‚   в””в”Ђв”Ђ normalization.json
+    в”њв”Ђв”Ђ google4/
+    в”‚   в””в”Ђв”Ђ normalization.json
+    в”њв”Ђв”Ђ oxford102/
+    в”‚   в””в”Ђв”Ђ normalization.json
+    в””в”Ђв”Ђ stanford120/
+        в””в”Ђв”Ђ normalization.json
 ```
 
 If the Hub upload uses a flatter directory structure, the dataset identifiers and metadata filenames remain the same.
@@ -564,7 +580,7 @@ For the most conservative distribution model, publish the split manifests, indic
 | Caltech-256 | https://data.caltech.edu/records/nyy15-4j048 |
 | Google Scraped Image Dataset | https://www.kaggle.com/datasets/duttadebadri/image-classification |
 | Oxford Flowers-102 | https://www.robots.ox.ac.uk/~vgg/data/flowers/102/ |
-| Oxford package used in the experiments | https://www.kaggle.com/datasets/nunenuh/pytorch-challange-flower-dataset |
+| Oxford package used in the experiments | https://www.kaggle.com/datasets/yousefmohamed20/oxford-102-flower-dataset |
 | Stanford Dogs | http://vision.stanford.edu/aditya86/ImageNetDogs/ |
 | Stanford Dogs package used in the experiments | https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset |
 
@@ -578,7 +594,7 @@ When using this prepared collection, please cite both the thesis and the origina
 @thesis{dihtenko2026stochasticdepth,
   author  = {Herman Dihtenko},
   title   = {A Study of ``Stochastic Depth'' for Regularizing Residual Convolutional Neural Networks},
-  school  = {Catholic University of Eichstätt-Ingolstadt},
+  school  = {Catholic University of Eichstätt-Ingolstadt},
   type    = {Bachelor's thesis},
   year    = {2026}
 }
@@ -631,4 +647,4 @@ When using this prepared collection, please cite both the thesis and the origina
 
 **Herman Dihtenko**
 
-For questions about split generation, preprocessing, or the thesis experiments, please use the issue tracker in the accompanying GitHub repository.
+For questions about split generation, preprocessing, or the thesis experiments, please use the issue tracker in the accompanying GitHub repository.