Datasets:

uv-scripts
/

vlm-object-detection

@@ -143,9 +143,9 @@ Run `uv run qwen3vl-detect.py --help` for the full flag set. Most-tuned flags:
 ## Worked examples
-![Qwen3.6-35B-A3B detecting an American Black Bear in a camera-trap photo (left: prediction, right: ground truth)](./example-ena24-bear.png)
-*Zero-shot detection on `davanstrien/ena24-detection` (camera-trap wildlife) — left panel shows the model's `Black_Bear` prediction with a tight box; right shows ground truth at the same coordinates. See [`davanstrien/qwen3.6-ena24-20samples`](https://huggingface.co/datasets/davanstrien/qwen3.6-ena24-20samples) for the full 20-row output dataset.*
 These public datasets were generated with the recommended config (Qwen3.6-35B-A3B on a100-large via vllm/vllm-openai:latest, 20 sampled rows each, shuffle seed 42) and can be inspected as reference:

 ## Worked examples
+![Qwen3.6-35B-A3B detecting a Red Fox in a camera-trap photo (left: prediction, right: ground truth)](./example-ena24-fox.png)
+*Zero-shot detection on `davanstrien/ena24-detection` (camera-trap wildlife) — left panel shows the model's `Red Fox` prediction with a tight box; right shows ground truth at the same coordinates. See [`davanstrien/qwen3.6-ena24-20samples`](https://huggingface.co/datasets/davanstrien/qwen3.6-ena24-20samples) for the full 20-row output dataset.*
 These public datasets were generated with the recommended config (Qwen3.6-35B-A3B on a100-large via vllm/vllm-openai:latest, 20 sampled rows each, shuffle seed 42) and can be inspected as reference: