Upload trained artifacts for CheXVision-ResNet_best

CheXVision-ResNet_best.pth

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CheXVision-ResNet_history.json

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+    0.6388931123612589,
+    0.6315628815628815,
+    0.642644431209053,
+    0.6471539932342991,
+    0.6272024729520865,
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   "learning_rate": [
     0.0009997532801828658,
@@ -440,25 +345,6 @@
     0.000452945843340743,
     0.00043733338321784806,
     0.0004217827674798847,
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-    0.00016934406733817422,
-    0.0001577264470356557,
-    0.00014644660940672634
+    0.00040630934270713783
   ]
 }

README.md

@@ -1,95 +1,167 @@
----
-license: mit
-language:
-- en
-library_name: pytorch
-pipeline_tag: image-classification
-tags:
-- chexvision
-- medical-imaging
-- chest-xray
-- radiology
-- pytorch
-- multi-label-classification
-datasets:
-- HlexNC/chest-xray-14-320
----
-
-# CheXVision-ResNet
-
-## Project Resources
-
-- [GitHub repository](https://github.com/arudaev/chexvision)
-- [Presentation deck](https://arudaev.github.io/chexvision/)
-- [Live demo](https://huggingface.co/spaces/arudaev/chexvision-demo)
-- [Dataset](https://huggingface.co/datasets/arudaev/chest-xray-14-320)
-
-> **CheXVision** — Deep Learning & Big Data university project.
-> 14-class chest X-ray pathology detection + binary normal/abnormal classification
-> on the NIH Chest X-ray14 dataset (112,120 images).
-
-## Architecture
-
-<p align="center">
-  <img src="https://huggingface.co/HlexNC/chexvision-scratch/resolve/main/arch_scratch.png" width="88%" alt="SE-ResNet Architecture"/>
-</p>
-
-## Training Pipeline
-
-<p align="center">
-  <img src="https://huggingface.co/HlexNC/chexvision-scratch/resolve/main/pipeline_training.png" width="42%" alt="Training Pipeline"/>
-</p>
-
-## Training Metrics
-
-- Best validation macro AUC-ROC: `0.8008`
-- Best validation binary AUC-ROC: `0.7571`
-- Best validation binary F1: `0.6474`
-- Best checkpoint epoch: `60`
-
-
-## Training Configuration
-
-- Repository: `HlexNC/chexvision-scratch`
-- Dataset: [HlexNC/chest-xray-14-320](https://huggingface.co/datasets/HlexNC/chest-xray-14-320) · revision `44443e6ee968b3c6094b63f14a27698c40b50680`
-- Architecture: Custom residual CNN with Squeeze-Excitation channel attention (depth [3, 4, 6, 3]) trained from scratch with shared features and dual classification heads.
-- Platform: Kaggle GPU kernel (NVIDIA T4 / P100)
-- Batch size: `24` × grad_accum `4` = **effective batch `96`**
-- AMP (fp16): `enabled`
-- CLAHE preprocessing: `disabled`
-- Label smoothing: `0.0`
-- Optimizer: AdamW  ·  Scheduler: CosineAnnealingLR
-- Epochs configured: `100`  ·  Early stop patience: `15`
-
-## Intended Use
-
-This model is intended for research and educational work on automated chest X-ray pathology detection.
-It outputs two predictions per image:
-1. **Multi-label scores** — independent sigmoid probability for each of 14 NIH pathologies
-2. **Binary score** — sigmoid probability of any abnormality (Normal vs. Abnormal)
-
-## Limitations
-
-- Not validated for clinical use. Predictions must not substitute professional medical judgment.
-- Trained on NIH Chest X-ray14, which contains noisy radiologist annotations (patient-level labels, not lesion-level).
-- Performance degrades on images from equipment, patient populations, or preprocessing pipelines
-  that differ from the NIH training distribution.
-- Reported AUC metrics are on the validation split, not the held-out test set.
-
-## CheXNet Benchmark Context
-
-CheXNet (Rajpurkar et al., 2017) — the seminal paper establishing DenseNet-121 for chest X-ray
-classification — reported **0.841 macro AUC-ROC** on a comparable split of this dataset.
-CheXVision-DenseNet matches this benchmark. See the
-[CheXVision demo](https://huggingface.co/spaces/HlexNC/chexvision-demo) for live inference.
-
-## Citation
-
-```bibtex
-@misc{chexvision2026,
-  title={CheXVision: Dual-Task Chest X-ray Classification with Custom CNN and DenseNet-121},
-  author={BIG D(ATA) Team},
-  year={2026},
-  howpublished={\url{https://huggingface.co/HlexNC/chexvision-scratch}}
-}
-```
+---
+license: mit
+language:
+- en
+library_name: pytorch
+pipeline_tag: image-classification
+tags:
+- chexvision
+- medical-imaging
+- chest-xray
+- radiology
+- pytorch
+- multi-label-classification
+datasets:
+- arudaev/chest-xray-14-320
+---
+
+# CheXVision-ResNet
+
+> **CheXVision** — Deep Learning & Big Data university project.
+> 14-class chest X-ray pathology detection + binary normal/abnormal classification
+> on the NIH Chest X-ray14 dataset (112,120 images).
+
+## Project Resources
+
+- [GitHub repository](https://github.com/arudaev/chexvision)
+- [Presentation deck](https://arudaev.github.io/chexvision/)
+- [Live demo](https://huggingface.co/spaces/arudaev/chexvision-demo)
+- [Dataset](https://huggingface.co/datasets/arudaev/chest-xray-14-320)
+
+## Architecture
+
+```mermaid
+graph LR
+    IN["Input
+    3 × 320 × 320"] --> STEM["Stem
+    7×7 Conv · BN · ReLU
+    3→64ch · MaxPool ÷2"]
+    STEM --> S1["Stage 1
+    3× SE-ResBlock
+    64ch"]
+    S1 --> S2["Stage 2 ↓½
+    4× SE-ResBlock
+    128ch"]
+    S2 --> S3["Stage 3 ↓½
+    6× SE-ResBlock
+    256ch"]
+    S3 --> S4["Stage 4 ↓½
+    3× SE-ResBlock
+    512ch"]
+    S4 --> GAP["Global Avg Pool
+    Dropout(0.5)
+    512-dim"]
+    GAP --> MLH["Multilabel Head
+    Linear 512→14
+    sigmoid · 14 pathologies"]
+    GAP --> BH["Binary Head
+    Linear 512→1
+    sigmoid · Normal/Abnormal"]
+    style MLH fill:#2e7d32,color:#fff
+    style BH fill:#1565c0,color:#fff
+    style IN fill:#37474f,color:#fff
+```
+
+## Training Pipeline
+
+```mermaid
+flowchart TD
+    DS[("🗄️ arudaev/chest-xray-14-320
+112,120 images · 36 shards · ~7.97 GB")]
+    DS -->|snapshot_download| PREP["📂 data/images · data/labels.csv
+train 78,468 · val 11,210 · test 22,442"]
+    PREP --> AUG["Augmentation Pipeline
+HFlip · Rotate±15° · RandomAffine
+ColorJitter · GaussianBlur · RandomErasing"]
+    AUG --> FWD["⚡ Model Forward Pass
+torch.cuda.amp.autocast · fp16"]
+    FWD --> ML["multilabel_logits B×14
+WeightedBCE + pos_weight · 14 classes"]
+    FWD --> BIN["binary_logits B×1
+BCE · Normal vs. Abnormal"]
+    ML --> LOSS["Combined Loss
+1.0 × multilabel + 0.5 × binary"]
+    BIN --> LOSS
+    LOSS --> BACK["Backward · Grad Clip 1.0
+Gradient Accumulation ×4 · eff. batch 96"]
+    BACK --> OPT["AdamW · CosineAnnealingLR
+early stop patience = 15"]
+    OPT -->|"↑ best val AUC-ROC"| BEST["💾 Best Checkpoint
+model_state · best_val_metrics · config"]
+    BEST -->|upload_model_artifacts| HUB["🤗 HF Hub
+checkpoint · history.json · model card"]
+```
+
+## Training Metrics
+
+- Best validation macro AUC-ROC: `0.8141`
+- Best validation binary AUC-ROC: `0.7739`
+- Best validation binary F1: `0.6587`
+- Best checkpoint epoch: `41`
+
+
+## Per-Class AUC-ROC at Best Epoch
+
+| Pathology            | AUC-ROC  | Visual        |
+|----------------------|----------|---------------|
+| Atelectasis          | `0.8022` | `████████░░` |
+| Cardiomegaly         | `0.9059` | `█████████░` |
+| Effusion             | `0.8831` | `█████████░` |
+| Infiltration         | `0.7060` | `███████░░░` |
+| Mass                 | `0.8596` | `█████████░` |
+| Nodule               | `0.7525` | `████████░░` |
+| Pneumonia            | `0.7298` | `███████░░░` |
+| Pneumothorax         | `0.8329` | `████████░░` |
+| Consolidation        | `0.8080` | `████████░░` |
+| Edema                | `0.9122` | `█████████░` |
+| Emphysema            | `0.8545` | `█████████░` |
+| Fibrosis             | `0.7622` | `████████░░` |
+| Pleural_Thickening   | `0.7782` | `████████░░` |
+| Hernia               | `0.8101` | `████████░░` |
+
+## Training Configuration
+
+- Repository: `arudaev/chexvision-scratch`
+- Dataset: [arudaev/chest-xray-14-320](https://huggingface.co/datasets/arudaev/chest-xray-14-320) · revision `44443e6ee968b3c6094b63f14a27698c40b50680`
+- Architecture: Custom residual CNN with Squeeze-Excitation channel attention (depth [3, 4, 6, 3]) trained from scratch with shared features and dual classification heads.
+- Platform: Kaggle GPU kernel (NVIDIA T4 / P100)
+- Batch size: `24` × grad_accum `4` = **effective batch `96`**
+- AMP (fp16): `enabled`
+- CLAHE preprocessing: `enabled`
+- Label smoothing: `0.1`
+- Optimizer: AdamW  ·  Scheduler: CosineAnnealingLR
+- Epochs configured: `100`  ·  Early stop patience: `15`
+
+## Intended Use
+
+This model is intended for research and educational work on automated chest X-ray pathology detection.
+It outputs two predictions per image:
+1. **Multi-label scores** — independent sigmoid probability for each of 14 NIH pathologies
+2. **Binary score** — sigmoid probability of any abnormality (Normal vs. Abnormal)
+
+## Limitations
+
+- Not validated for clinical use. Predictions must not substitute professional medical judgment.
+- Trained on NIH Chest X-ray14, which contains noisy radiologist annotations (patient-level labels, not lesion-level).
+- Performance degrades on images from equipment, patient populations, or preprocessing pipelines
+  that differ from the NIH training distribution.
+- Reported AUC metrics are on the validation split, not the held-out test set.
+
+## CheXNet Benchmark Context
+
+CheXNet (Rajpurkar et al., 2017) — the seminal paper establishing DenseNet-121 for chest X-ray
+classification — reported **0.841 macro AUC-ROC** on a comparable split of this dataset.
+CheXVision-DenseNet matches this benchmark. See the
+[CheXVision demo](https://huggingface.co/spaces/arudaev/chexvision-demo) for live inference, or the
+[presentation deck](https://arudaev.github.io/chexvision/) for the project walkthrough.
+
+## Citation
+
+```bibtex
+@misc{chexvision2026,
+  title={CheXVision: Dual-Task Chest X-ray Classification with Custom CNN and DenseNet-121},
+  author={BIG D(ATA) Team},
+  year={2026},
+  howpublished={\url{https://huggingface.co/arudaev/chexvision-scratch}}
+}
+```

training_config.json

@@ -1,8 +1,9 @@
 {
   "seed": 42,
   "data": {
-    "dataset_name": "HlexNC/chest-xray-14-320",
+    "dataset_name": "arudaev/chest-xray-14-320",
     "image_size": 320,
+    "clahe": true,
     "num_workers": 4,
     "pin_memory": true,
     "train_split": 0.7,
@@ -38,7 +39,7 @@
       "Hernia"
     ],
     "data_dir": "/kaggle/working/data",
-    "hf_dataset_repo": "HlexNC/chest-xray-14-320",
+    "hf_dataset_repo": "arudaev/chest-xray-14-320",
     "hf_dataset_revision": "44443e6ee968b3c6094b63f14a27698c40b50680"
   },
   "training": {
@@ -54,7 +55,8 @@
     "use_amp": true,
     "grad_accum_steps": 4,
     "multilabel_weight": 1.0,
-    "binary_weight": 0.5
+    "binary_weight": 0.5,
+    "label_smoothing": 0.1
   },
   "evaluation": {
     "primary_metric": "auc_roc_macro",
@@ -67,10 +69,10 @@
     "log_every_n_steps": 100
   },
   "huggingface": {
-    "dataset_repo": "HlexNC/chest-xray-14-320",
-    "scratch_model_repo": "HlexNC/chexvision-scratch",
-    "transfer_model_repo": "HlexNC/chexvision-densenet",
-    "space_repo": "HlexNC/chexvision-demo"
+    "dataset_repo": "arudaev/chest-xray-14-320",
+    "scratch_model_repo": "arudaev/chexvision-scratch",
+    "transfer_model_repo": "arudaev/chexvision-densenet",
+    "space_repo": "arudaev/chexvision-demo"
   },
   "model": {
     "type": "scratch",