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+---
+title: "ViT Base Patch16 384 – GI Endoscopy Classifier"
+tags:
+  - vision-transformer
+  - vit
+  - image-classification
+  - medical-imaging
+  - gastrointestinal
+  - pytorch
+  - timm
+library_name: timm
+license: other
+language: en
+pipeline_tag: image-classification
+---
+
+## ViT Base (384px) for GI Endoscopy
+23-class gastrointestinal endoscopy classifier built on ViT Base Patch16 384. Trained with timm, strong augmentation (MixUp + Albumentations), and evaluated with TTA. Includes traced TorchScript weights `vit_best_traced.pt` for drop-in PyTorch inference.
+
+### Highlights
+- Val: **92.18%** (epoch 21 best); Test (TTA): **93.25% Acc / 92.19% Prec / 93.25% Rec / 92.59% F1**
+- Robust pipeline: MixUp, CoarseDropout, and TTA for generalization
+- Ready to serve: TorchScript traced weights; standard 384x384 ImageNet normalization
+- Scope: 23 GI classes; input RGB 384x384
+
+### Performance
+| Split | Accuracy | Precision | Recall | F1 | Notes |
+| --- | --- | --- | --- | --- | --- |
+| Validation (best) | 92.18% | – | – | – | Best at epoch 21 |
+| Test (TTA) | 93.25% | 92.19% | 93.25% | 92.59% | 800 batches, TTA enabled |
+
+### Model Card
+- Backbone: `vit_base_patch16_384` (≈86.1M params)
+- Classes: 23
+- Input: 384x384 RGB, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+- Training recipe:
+  - Epochs: 25; batch size: 2
+  - LR: 1e-5 base with warmup schedule
+  - Augment: MixUp; Albumentations with CoarseDropout
+  - Eval: TTA on validation/test; best checkpoint saved each epoch (best at epoch 21)
+- Data scale (from logs): 7,463 train, 1,599 val, 1,600 test images across 23 classes
+
+### Inference (PyTorch TorchScript)
+```python
+import torch
+from PIL import Image
+from torchvision import transforms
+
+model = torch.jit.load("vit_best_traced.pt")
+model.eval()
+
+preprocess = transforms.Compose([
+    transforms.Resize((384, 384)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+img = Image.open("path/to/image.jpg").convert("RGB")
+tensor = preprocess(img).unsqueeze(0)
+with torch.no_grad():
+    probs = model(tensor).softmax(dim=1)
+    pred = probs.argmax(dim=1).item()
+
+print("Predicted class index:", pred)
+```
+
+### Training Log (key checkpoints)
+- Epoch 8: Val Acc 90.56%
+- Epoch 12: Val Acc 91.62%
+- Epoch 16: Val Acc 91.74%
+- Epoch 18: Val Acc 92.06%
+- Epoch 20: Val Acc 92.12%
+- Epoch 21: **Val Acc 92.18%** (best)
+- Test (TTA): Acc 93.25%, Precision 92.19%, Recall 93.25%, F1 92.59%
+
+### Files
+- `vit_best_traced.pt` — TorchScript traced weights for ViT Base Patch16 384 (best checkpoint)
+
+### Notes & Responsible Use
+- Trainer: custom `AdvancedMemoryEfficientTrainer` with MixUp and Albumentations TTA.
+- Warnings during training: Albumentations recommends `Affine` over `ShiftScaleRotate`; CoarseDropout args adjusted. Metrics unaffected.
+- Medical context: research-only; not a regulated medical device. Validate clinically and keep a human in the loop.
+
+### Reproduce / Fine-tune Tips
+- Keep preprocessing (384 resize + ImageNet normalization) consistent to match reported metrics.
+- Start from `vit_base_patch16_384` in timm; use MixUp and similar augmentations.
+- Enable TTA for best accuracy; disable for faster inference.
+
+### Changelog
+- 2025-12-29: Initial public release with traced weights and metrics.