chore: require transformers >= 5.4.0 for shortest_edge resize support

The preprocessor_config uses shortest_edge: 440 which requires
transformers >= 5.4.0. Older versions will crash with a ValueError
demanding height/width keys instead.

AGENTS.md

@@ -23,9 +23,10 @@ processor = ViTImageProcessor.from_pretrained("buildborderless/CommunityForensic
 The custom wrapper (`modeling_vit_classifier.py`) uses `timm.create_model` with a sigmoid output and `pretrained_weights/model_v11_ViT_384_base_ckpt.pt`. This is for standalone (non-HF-pipeline) inference requiring both `timm` and `transformers`.
 
 ## Dependencies
-- `transformers` >= 4.50.0
-- `timm` (for the custom ViTClassifier wrapper only)
+- `transformers >= 5.4.0` (required — older versions lack `shortest_edge` resize and will squash images)
+- `timm` (for the deprecated ViTClassifier wrapper only)
 - `torch`, `torchvision`, `Pillow`
+- `onnxruntime >= 1.27` (for ONNX models)
 
 ## Scripts (in `scripts/`)
 Data processing utilities for the eval dataset — not needed for inference:

README.md

@@ -66,7 +66,11 @@ The separate [`buildborderless/CommunityForensics-DeepfakeDet-ViT-ONNX`](https:/
 
 ---
 
-## Quick Start
+## Dependencies
+
+- `transformers >= 5.4.0` (required for correct image resize — versions < 5.4.0 will squash non-square images)
+- `torch`, `torchvision`, `Pillow`
+- `onnxruntime >= 1.27` (for ONNX models)
 
 ```python
 from transformers import ViTForImageClassification, ViTImageProcessor

config.json

@@ -25,5 +25,5 @@
   "patch_size": 16,
   "qkv_bias": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.50.0"
+  "transformers_version": "5.4.0"
 }