TensorRT
ONNX
Safetensors
PyTorch
nvidia
tao
video-text-retrieval
video-embeddings
physical-ai
anomaly-detection
lora
fine-tuned
Instructions to use nvidia/InternVideo2-CLIP-anomaly-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use nvidia/InternVideo2-CLIP-anomaly-detection with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Commit History
2 days ago
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Add InternVideo2-CLIP-anomaly-detection release files (TAO fine-tune, research release)
Verified signatureAdds the InternVideo2-CLIP-anomaly-detection release files: the TAO `video_clip` fine-tune of OpenGVLab InternVideo2-CLIP-L14 (vision LoRA, frozen MobileCLIP-B-LT text encoder) for text-to-video event and anomaly retrieval, released for non-commercial research under the NVIDIA Internal Scientific Research and Development Model License already set on this repository. **Contents** - `model.safetensors` — fine-tuned weights with the LoRA adapters merged (FP32, TAO state-dict key names) + `model_config.yaml`. - `model.pth` — TAO checkpoint with the LoRA adapters unmerged (state dict only). - `onnx/` — FP32 vision and text towers (opset 23, dynamic batch, `.onnx` + `.onnx.data`), FP16 towers for strongly typed TensorRT builds, CLIP BPE tokenizer. - `README.md` — model card, `MANIFEST.sha256`. **Verification before upload** - `model.safetensors` loads `strict=True` into a `peft`-disabled `VideoCLIPPlModel`; `model.pth` loads with `load_from_checkpoint`; both reproduce the reference embeddi…ca3d907