Upload README.md with huggingface_hub

README.md

@@ -39,7 +39,22 @@ Serving them from the HF Hub is reliable from HF's build infrastructure.
 
 ## Usage
 
-Download (no auth needed — public repo):
+### 1. Get the model code from GitHub
+
+The model definitions (`model.py`, `encoder.py`, and the `clip/` package) are
+**not** in this weights repo — they live in the training repo
+[**`aitf-its-tim3-dfk/david`**](https://github.com/aitf-its-tim3-dfk/david)
+(branch `feat-cddb`). Clone it first and run from inside it:
+
+```bash
+git clone -b feat-cddb https://github.com/aitf-its-tim3-dfk/david
+cd david
+pip install -r requirements.txt
+```
+
+This is what makes `from model import ...` and `from encoder import ...` below work.
+
+### 2. Download the checkpoints (no auth needed — public repo)
 
 ```python
 from huggingface_hub import hf_hub_download
@@ -49,13 +64,12 @@ encoder_ckpt    = hf_hub_download(REPO, "k400_clip_complete_finetuned_30_epochs.
 classifier_ckpt = hf_hub_download(REPO, "best_detector_model.pt")
 ```
 
-Load with the DAViD modules (from the training repo
-[`aitf-its-tim3-dfk/david`](https://github.com/aitf-its-tim3-dfk/david)):
+### 3. Load and run
 
 ```python
 import torch
-from encoder import load_feature_extractor
-from model import ClassificationHead
+from encoder import load_feature_extractor   # from the cloned GitHub repo
+from model import ClassificationHead          # from the cloned GitHub repo
 
 feature_extractor = load_feature_extractor(
     arch="ViT-B/16",