Analyzing Fast, Frequent, and Fine-grained (
The code is tested in Linux (Ubuntu 22.04) with the dependency versions in requirements.txt.
Refer to the READMEs in the data directory for pre-processing and setup instructions.
We also introduce a general annotation pipeline and tool to help the community of other domain experts create new
To train all baseline models, use python3 train_f3set_baselines.py <dataset_name> <frame_dir> -s <save_dir> -m <model_arch> -t <head_arch>.
<dataset_name>: name of the dataset (e.g., f3set-tennis)<frame_dir>: path to the extracted frames<save_dir>: path to save logs, checkpoints, and inference results<model_arch>: feature extractor architecture (e.g., rny002, rny002_tsm, slowfast)<head_arch>: head module architecture (e.g., mstcn, asformer, gcn, actionformer, gru)
Similarly, we also provide the code train_f3set_f3ed.py for training using python3 train_f3set_f3ed.py <dataset_name> <frame_dir> -s <save_dir> -m <model_arch> -t <head_arch> --use_ctx. You can enable the Contextual Module (CTX) using --use_ctx. If --use_ctx is not specified, the model will only contains the Event Localizer (LCL) and Multi-Label Classifier (MLC).
Training will produce checkpoints, predictions for the val split, and predictions for the test split on the best validation epoch.
Models and configurations can be found in f3set-model. Place the checkpoint file and config.json file in the same directory.
To perform inference with an already trained model, use python3 test_f3set_baselines.py <model_dir> <frame_dir> -s <split> or python3 test_f3set_f3ed.py <model_dir> <frame_dir> -s <split>. This will output results for 3 evaluation metrics (event-wise mean F1 score, element-wise mean F1 score, and edit score).
Each dataset has plaintext files that contain the list of event types events.txt and elements: elements.txt
This is a list of the event names, one per line: {split}.json
This file contains entries for each video and its contained events.
[
{
"fps": 25,
"height": 720,
"width": 1280,
"num_frames": 342, // number of frames in this clip
"video": "20210909-W-US_Open-SF-Aryna_Sabalenka-Leylah_Fernandez_170943_171285", // "video name"_"start frame of the clip"_"end frame of the clip"
"far_name": "Leylah Fernandez", // far-end player's name
"far_hand": "LH", // far-end player's handedness
"far_set": 1, // far-end player's set score
"far_game": 2, // far-end player's game score
"far_point": 2, // far-end player's point score
"near_name": "Aryna Sabalenka", // near-end player's name
"near_hand": "RH", // near-end player's handedness
"near_se
54E2
t": 1, // near-end player's set score
"near_game": 2, // near-end player's game score
"near_point": 0, // near-end player's point score
"events": [
{
"frame": 100, // Frame
"label": EVENT_NAME, // Event type
},
...
],
},
...
]
Frame directory
We assume pre-extracted frames, that have been resized to 224 pixels high or similar. The organization of the frames is expected to be <frame_dir>/<video_id>/<frame_number>.jpg. For example,
video1/
├─ 000000.jpg
├─ 000001.jpg
├─ 000002.jpg
├─ ...
video2/
├─ 000000.jpg
├─ ...
Similar format applies to the frames containing objects of interest.