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seldinger-videomae-lap-chole-broad128
txmedai/seldinger-videomae-lap-chole-broad128
seldinger-videomae-lap-chole-broad128 is a machine learning model from txmedai. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
Self-supervised VideoMAE pretraining checkpoint adapted to a mirrored laparoscopic cholecystectomy video corpus.
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.safetensors377 MB · 100%
At a glance
- Library
- transformers
- License
- other
- Model type
- videomae
- Access
- Public
- Created
- Jul 14, 2026
- Updated
- Jul 14, 2026
- SHA
- 485eeb34
Base models
Model card
From the Hugging Face model README
Seldinger VideoMAE Lap Chole Broad128
Self-supervised VideoMAE pretraining checkpoint adapted to a mirrored laparoscopic cholecystectomy video corpus.
Training Lineage
- Base checkpoint:
MCG-NJU/videomae-base - Strict adaptation run: 30 train videos, 200 steps
- Broad continuation run: 128 train videos, 500 steps
- Final broad metrics: loss_last
0.27357321977615356, loss_mean0.4516991448639892 - Full checkpoint archive with intermediate checkpoints:
gs://seldinger-datasets-raw/_runner/surgery-video-training/surgery-videomae-pretrained-broad128-20260714T173724Z/model.tgz
Files
config.jsonandmodel.safetensors: finalVideoMAEForPreTrainingcheckpointtrain_metrics.json: final training metricstraining_manifest.jsonl: 128-row broad training manifest used for the continuation run
Intended Use
This is a self-supervised video representation checkpoint for research and downstream surgical-video experimentation. It is not a clinical decision model and should not be used for patient care without downstream validation.
- Library
- transformers
- Type
- videomae
- License
- other
- Created
- Jul 14, 2026
- Updated
- Jul 14, 2026