Update README.md

README.md

@@ -2,16 +2,22 @@
 license: other
 pretty_name: FARBench Docker Images
 tags:
-- benchmark
-- docker-image
-- research-agent
+  - benchmark
+  - docker-image
+  - research-agent
 ---
 
 # FARBench Docker Images
 
 Per-task Docker images, saved with `docker save | gzip` and uploaded as plain LFS files. Each tarball is a fully self-contained image (CUDA + Python + task deps + baked task data); load it with `docker load` and the resulting image carries the canonical tag `farbench/farbench:<task>-<cuda>`.
 
-Files are at the repo root with the flat naming convention **`<task>-<cuda>.docker.tar.gz`** (e.g. `mnist_classification-cu118.docker.tar.gz`).
+Files are at the repo root with the flat naming convention **`<task>-<cuda>.docker.tar.gz`** (e.g. `domainnet_quickdraw-cu118.docker.tar.gz`).
+
+## Website
+
+Explore the FARBench leaderboard, agent trajectories, and capability analysis at:
+
+- https://anomyous4.github.io/anomyous-website/#/
 
 ## Quick start
 
@@ -19,15 +25,15 @@ Files are at the repo root with the flat naming convention **`<task>-<cuda>.dock
 # 1. Download the tarball you need (single task, cu118 example).
 huggingface-cli download \
     FARBenchAnonymous/FARBench \
-    mnist_classification-cu118.docker.tar.gz \
+    domainnet_quickdraw-cu118.docker.tar.gz \
     --repo-type dataset \
     --local-dir ./farbench-images
 
 # 2. Load into your Docker daemon.
-docker load -i ./farbench-images/mnist_classification-cu118.docker.tar.gz
+docker load -i ./farbench-images/domainnet_quickdraw-cu118.docker.tar.gz
 
 # 3. The image is now available locally as e.g.
-#    farbench/farbench:mnist_classification-cu118
+#    farbench/farbench:domainnet_quickdraw-cu118
 docker images | grep farbench
 ```
 
@@ -63,7 +69,6 @@ docker load -i <task>-<cuda>.docker.tar.gz
 | `ljspeech_tts` | Audio/Speech | `utmos` | [ljspeech_tts-cu118](./ljspeech_tts-cu118.docker.tar.gz) | [ljspeech_tts-cu128](./ljspeech_tts-cu128.docker.tar.gz) |
 | `metrla_traffic` | AI for science | `mae_60min` | [metrla_traffic-cu118](./metrla_traffic-cu118.docker.tar.gz) | [metrla_traffic-cu128](./metrla_traffic-cu128.docker.tar.gz) |
 | `minigrid` | robotics | `success_rate` | [minigrid-cu118](./minigrid-cu118.docker.tar.gz) | [minigrid-cu128](./minigrid-cu128.docker.tar.gz) |
-| `mnist_classification` | Computer Vision | `accuracy` | [mnist_classification-cu118](./mnist_classification-cu118.docker.tar.gz) | [mnist_classification-cu128](./mnist_classification-cu128.docker.tar.gz) |
 | `objaverse_3dgen` | computer vision | `lpips` | [objaverse_3dgen-cu118](./objaverse_3dgen-cu118.docker.tar.gz) | [objaverse_3dgen-cu128](./objaverse_3dgen-cu128.docker.tar.gz) |
 | `ogbg_molpcba` | AI for science | `avg_precision` | [ogbg_molpcba-cu118](./ogbg_molpcba-cu118.docker.tar.gz) | [ogbg_molpcba-cu128](./ogbg_molpcba-cu128.docker.tar.gz) |
 | `qlib_stock` | natural language processing | `ic_mean` | [qlib_stock-cu118](./qlib_stock-cu118.docker.tar.gz) | [qlib_stock-cu128](./qlib_stock-cu128.docker.tar.gz) |
@@ -86,4 +91,4 @@ Use `cu118` unless your GPU requires CUDA 12.x kernels. Both variants produce id
 
 ## License
 
-The RABench framework is released under Apache-2.0. The bundled datasets and pre-cached model weights are redistributed from their original sources and retain the original licenses; see each task's README in the data repository.
+The FARBench framework is released under the MIT License. The bundled datasets and pre-cached model weights are redistributed from their original sources and retain the original licenses; see each task's README in the data repository.