Upload trained artifacts for CheXVision-ResNet_best

CheXVision-ResNet_best.pth

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README.md

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+---
+license: mit
+language:
+- en
+library_name: pytorch
+pipeline_tag: image-classification
+tags:
+- chexvision
+- medical-imaging
+- chest-xray
+- radiology
+- pytorch
+datasets:
+- HlexNC/chest-xray-14
+---
+
+# CheXVision-ResNet
+
+## Model Details
+
+- 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`
+
+## 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`
+
+## 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.
+
+## 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.
+
+## Training Procedure
+
+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.

training_config.json

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+{
+  "seed": 42,
+  "data": {
+    "dataset_name": "HlexNC/chest-xray-14",
+    "image_size": 224,
+    "num_workers": 4,
+    "pin_memory": true,
+    "train_split": 0.7,
+    "val_split": 0.1,
+    "test_split": 0.2,
+    "augmentation": {
+      "horizontal_flip": true,
+      "rotation_degrees": 10,
+      "color_jitter": {
+        "brightness": 0.2,
+        "contrast": 0.2
+      }
+    },
+    "labels": [
+      "Atelectasis",
+      "Cardiomegaly",
+      "Effusion",
+      "Infiltration",
+      "Mass",
+      "Nodule",
+      "Pneumonia",
+      "Pneumothorax",
+      "Consolidation",
+      "Edema",
+      "Emphysema",
+      "Fibrosis",
+      "Pleural_Thickening",
+      "Hernia"
+    ],
+    "data_dir": "/kaggle/working/data",
+    "hf_dataset_repo": "HlexNC/chest-xray-14",
+    "hf_dataset_revision": "c4e9a86b38de3b1604afa6e9f514d156eb9d20bf"
+  },
+  "training": {
+    "epochs": 50,
+    "batch_size": 32,
+    "learning_rate": 0.001,
+    "weight_decay": 0.0001,
+    "optimizer": "adamw",
+    "scheduler": "cosine",
+    "warmup_epochs": 5,
+    "early_stopping_patience": 10,
+    "gradient_clip_norm": 1.0,
+    "multilabel_weight": 1.0,
+    "binary_weight": 0.5
+  },
+  "evaluation": {
+    "primary_metric": "auc_roc_macro",
+    "threshold": 0.5
+  },
+  "logging": {
+    "log_dir": "logs/",
+    "checkpoint_dir": "/kaggle/working/checkpoints",
+    "save_every_n_epochs": 5,
+    "log_every_n_steps": 100
+  },
+  "huggingface": {
+    "dataset_repo": "HlexNC/chest-xray-14",
+    "scratch_model_repo": "HlexNC/chexvision-scratch",
+    "transfer_model_repo": "HlexNC/chexvision-densenet",
+    "space_repo": "HlexNC/chexvision-demo"
+  },
+  "model": {
+    "type": "scratch",
+    "name": "CheXVision-ResNet",
+    "architecture": {
+      "input_channels": 3,
+      "initial_filters": 64,
+      "block_config": [
+        2,
+        2,
+        2,
+        2
+      ],
+      "filter_sizes": [
+        64,
+        128,
+        256,
+        512
+      ],
+      "dropout": 0.5,
+      "activation": "relu",
+      "use_batch_norm": true,
+      "global_avg_pool": true
+    },
+    "heads": {
+      "multilabel": {
+        "num_classes": 14,
+        "activation": "sigmoid"
+      },
+      "binary": {
+        "num_classes": 1,
+        "activation": "sigmoid"
+      }
+    }
+  }
+}