Improve model card: Add pipeline tag, library name and link to github (#1)

- Improve model card: Add pipeline tag, library name and link to github (4612a305d3799605b85352ead3517e649df9a35f)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,3 +1,50 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+pipeline_tag: video-classification
+library_name: pytorch
+---
+
+# EAR-WACV25-DAKiet-TSM
+
+The model was presented in the paper [](https://huggingface.co/papers/2503.07821).
+
+This model is a Temporal Shift Module (TSM) based video classification model with a resnext50_32x4d backbone.
+
+**Github Repository:** https://github.com/fdfyaytkt/EAR-WACV25-DAKiet-TSM
+
+## Data
+The model was trained on a combination of datasets:
+
+*   **Toyota Smarthome dataset:** Used for activity recognition.
+*   **ETRI-Activity3D:** RGB videos (specific subsets or full dataset used depending on configuration).
+*   **ETRI-Activity3D-LivingLab:** RGB videos (specific subsets or full dataset used depending on configuration).
+
+Two configurations are detailed below, with their respective public leaderboard scores:
+
+### Config 1 (Public Leaderboard: 0.84402)
+
+*   Toyota Smarthome dataset
+*   ETRI-Activity3D - RGB videos (RGB\_P091-P100)
+*   ETRI-Activity3D-LivingLab - RGB videos (RGB(P201-P230))
+
+### Config 2 (Public Leaderboard: 0.78856)
+
+*   Toyota Smarthome dataset
+*   ETRI-Activity3D - RGB videos (full)
+*   ETRI-Activity3D-LivingLab - RGB videos (full)
+
+## Running
+
+Example training and evaluation commands are provided below. Refer to the repository for complete details and options:
+
+### Train
+
+```console
+python main.py elderly RGB --arch resnext50_32x4d --num_segments 8 --gd 20 --lr 0.001 --wd 1e-4 --lr_steps 20 40 --epochs 100 --batch-size 4 -j 32 --dropout 0.5 --consensus_type=avg --eval-freq=1 --shift --shift_div=8 --shift_place=blockres --npb
+```
+
+### Eval
+
+```console
+python generate_submission.py elderly --arch=resnext50_32x4d --csv_file=submission.csv  --weights=checkpoint/TSM_elderly_RGB_resnext50_32x4d_shift8_blockres_avg_segment8_e100/ckpt.best.pth.tar --test_segments=8 --batch_size=1 --test_crops=1
+```