Upload trained artifacts for CheXVision-ResNet_best

CheXVision-ResNet_best.pth

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 version https://git-lfs.github.com/spec/v1
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CheXVision-ResNet_history.json

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 }

README.md

@@ -10,41 +10,152 @@ tags:
 - chest-xray
 - radiology
 - pytorch
+- multi-label-classification
 datasets:
 - HlexNC/chest-xray-14
 ---
 
 # CheXVision-ResNet
 
-## Model Details
+> **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).
 
-- Repository: `HlexNC/chexvision-scratch`
-- Training platform: Kaggle GPU kernel
-- Dataset: [HlexNC/chest-xray-14](https://huggingface.co/datasets/HlexNC/chest-xray-14)
-- Dataset revision: `c4e9a86b38de3b1604afa6e9f514d156eb9d20bf`
-- Architecture: Custom residual CNN trained from scratch with shared features and dual classification heads.
-- Training epochs configured: `50`
-- Batch size configured: `32`
+## Architecture
+
+```mermaid
+graph LR
+    IN["Input
+    3 × 224 × 224"] --> 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[("🗄️ HlexNC/chest-xray-14
+    112,120 chest X-ray images
+    36 parquet shards · ~4.7 GB")] -->|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
+    ImageNet normalize  ⟨mean=[0.485,0.456,0.406]⟩"]
+    AUG --> FWD["⚡ Model Forward Pass
+    torch.cuda.amp.autocast  ·  fp16"]
+    FWD --> ML["multilabel_logits  B×14
+    WeightedBCE + pos_weight
+    14 pathology classes"]
+    FWD --> BIN["binary_logits  B×1
+    BCE loss
+    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
+    effective batch = 128"]
+    BACK --> OPT["AdamW step  ·  CosineAnnealingLR
+    early stop patience = 15"]
+    OPT -->|"↑ val macro 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: `0.7967`
-- Best validation binary AUC-ROC: `0.7571`
-- Best validation binary F1: `0.6371`
-- Saved checkpoint epoch: `24`
+- Best validation macro AUC-ROC: `0.7976`
+- Best validation binary AUC-ROC: `0.7554`
+- Best validation binary F1: `0.6295`
+- Best checkpoint epoch: `65`
+
+
+## Per-Class AUC-ROC at Best Epoch
+
+| Pathology            | AUC-ROC  | Visual        |
+|----------------------|----------|---------------|
+| Atelectasis          | `0.7842` | `████████░░` |
+| Cardiomegaly         | `0.8929` | `█████████░` |
+| Effusion             | `0.8602` | `█████████░` |
+| Infiltration         | `0.6903` | `███████░░░` |
+| Mass                 | `0.8168` | `████████░░` |
+| Nodule               | `0.6634` | `███████░░░` |
+| Pneumonia            | `0.6807` | `███████░░░` |
+| Pneumothorax         | `0.8208` | `████████░░` |
+| Consolidation        | `0.8184` | `████████░░` |
+| Edema                | `0.9103` | `█████████░` |
+| Emphysema            | `0.8355` | `████████░░` |
+| Fibrosis             | `0.7604` | `████████░░` |
+| Pleural_Thickening   | `0.7702` | `████████░░` |
+| Hernia               | `0.8619` | `█████████░` |
+
+## Training Configuration
+
+- Repository: `HlexNC/chexvision-scratch`
+- Dataset: [HlexNC/chest-xray-14](https://huggingface.co/datasets/HlexNC/chest-xray-14) · revision `c4e9a86b`
+- 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: `32` × grad_accum `4` = **effective batch `128`**
+- AMP (fp16): `enabled`
+- 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 predicts both 14 pathology labels and a binary normal-vs-abnormal signal for the CheXVision project.
+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
 
-- This repository does not provide clinical-grade validation.
-- Predictions must not be used as a substitute for professional medical judgement.
-- Performance can degrade on populations, devices, or preprocessing pipelines that differ from the training data.
+- 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.
 
-## Training Procedure
+## Citation
 
-Training is orchestrated from the CheXVision repository and runs on Kaggle GPU kernels.
-The training kernels download a pinned snapshot of the public Hugging Face dataset, save the best checkpoint, and upload the checkpoint plus metadata back to this public model repository.
+```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}}
+}
+```

training_config.json

@@ -10,11 +10,16 @@
     "test_split": 0.2,
     "augmentation": {
       "horizontal_flip": true,
-      "rotation_degrees": 10,
+      "rotation_degrees": 15,
+      "affine_translate": 0.05,
+      "affine_shear": 5,
       "color_jitter": {
-        "brightness": 0.2,
-        "contrast": 0.2
-      }
+        "brightness": 0.3,
+        "contrast": 0.3,
+        "saturation": 0.05
+      },
+      "gaussian_blur_prob": 0.2,
+      "random_erasing_prob": 0.1
     },
     "labels": [
       "Atelectasis",
@@ -37,15 +42,17 @@
     "hf_dataset_revision": "c4e9a86b38de3b1604afa6e9f514d156eb9d20bf"
   },
   "training": {
-    "epochs": 50,
+    "epochs": 100,
     "batch_size": 32,
     "learning_rate": 0.001,
     "weight_decay": 0.0001,
     "optimizer": "adamw",
     "scheduler": "cosine",
     "warmup_epochs": 5,
-    "early_stopping_patience": 10,
+    "early_stopping_patience": 15,
     "gradient_clip_norm": 1.0,
+    "use_amp": true,
+    "grad_accum_steps": 4,
     "multilabel_weight": 1.0,
     "binary_weight": 0.5
   },
@@ -72,10 +79,10 @@
       "input_channels": 3,
       "initial_filters": 64,
       "block_config": [
-        2,
-        2,
-        2,
-        2
+        3,
+        4,
+        6,
+        3
       ],
       "filter_sizes": [
         64,
@@ -86,7 +93,8 @@
       "dropout": 0.5,
       "activation": "relu",
       "use_batch_norm": true,
-      "global_avg_pool": true
+      "global_avg_pool": true,
+      "use_se": true
     },
     "heads": {
       "multilabel": {