ConvNeXtV2 Basketball Round-State Classifier
This model classifies a single basketball broadcast frame as either inside or outside an active round/play segment. It was locally fine-tuned from timm/convnextv2_base.fcmae_ft_in22k_in1k.
Model details
- Architecture: ConvNeXtV2 Base
- Task: binary image classification
- Resolution: 224 × 224 RGB
- Classes:
0 = not_in_round, 1 = in_round
- Best validation accuracy: 89.21%
- First epoch reaching the best score: epoch 17
- Base model license: CC BY-NC 4.0
The reported accuracy is from the local validation split and has not been independently reproduced on a public benchmark.
Intended use
Non-commercial research and prototyping for basketball broadcast segmentation and editing assistance. Commercial use is not permitted under the inherited CC BY-NC 4.0 license without obtaining any additional permission required from the relevant rights holders.
Training procedure
The original 1,000-class classification head was replaced with a two-class head. Training used staged backbone freezing/unfreezing, AdamW and cosine learning-rate scheduling.
- Batch size: 32
- Initial learning rate:
1e-4
- Weight decay:
1e-4
- Image size: 224
- Normalization mean:
[0.485, 0.456, 0.406]
- Normalization standard deviation:
[0.229, 0.224, 0.225]
Training-data disclosure
Training images are not included. They were locally extracted from basketball broadcast footage and grouped into not_in_round and in_round. Some footage originated from publicly accessible Bilibili videos. Public accessibility is not a grant of redistribution rights, so this repository does not contain the source footage, frames, audio, subtitles, uploader information or platform metadata.
The source material has not undergone complete work-by-work copyright clearance. Users are responsible for assessing compliance with copyright, privacy, publicity and platform rules. Rights holders may request review or removal through the repository discussion/contact channel.
Usage
1pip install -r requirements.txt
2python inference.py path/to/frame.jpg --checkpoint model.pth
The PyTorch checkpoint contains model_state_dict plus training metadata. Load pickle-based files only from trusted sources.
Limitations
- A single frame may not contain enough temporal information to determine play state.
- Broadcast overlays, replays and close-up shots may reduce accuracy.
- Generalization to unfamiliar leagues, production styles and camera systems is unverified.
- The model may learn broadcaster-specific visual patterns.
- Do not use it for surveillance or identity-related decisions.
License
This derivative is released under Creative Commons Attribution-NonCommercial 4.0 International, consistent with the base model's current Hugging Face license metadata. Attribution must identify the timm base model and original ConvNeXtV2 work. See LICENSE.md.
