Video Classification
Transformers
TensorBoard
Safetensors
PyTorch
vjepa2
computer-vision
video-understanding
fine-tuned
Instructions to use eagle0504/vjepa2-vitl-fpc16-256-ssv2-ucf101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eagle0504/vjepa2-vitl-fpc16-256-ssv2-ucf101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="eagle0504/vjepa2-vitl-fpc16-256-ssv2-ucf101")# Load model directly from transformers import AutoTokenizer, AutoModelForVideoClassification tokenizer = AutoTokenizer.from_pretrained("eagle0504/vjepa2-vitl-fpc16-256-ssv2-ucf101") model = AutoModelForVideoClassification.from_pretrained("eagle0504/vjepa2-vitl-fpc16-256-ssv2-ucf101", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "VJEPA2ForVideoClassification" | |
| ], | |
| "attention_dropout": 0.0, | |
| "attention_probs_dropout_prob": 0.0, | |
| "crop_size": 256, | |
| "drop_path_rate": 0.0, | |
| "frames_per_clip": 16, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "BenchPress", | |
| "1": "BasketballDunk", | |
| "2": "Archery", | |
| "3": "BaseballPitch", | |
| "4": "BandMarching", | |
| "5": "ApplyEyeMakeup", | |
| "6": "BalanceBeam", | |
| "7": "ApplyLipstick", | |
| "8": "Basketball", | |
| "9": "BabyCrawling" | |
| }, | |
| "image_size": 256, | |
| "in_chans": 3, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "ApplyEyeMakeup": 5, | |
| "ApplyLipstick": 7, | |
| "Archery": 2, | |
| "BabyCrawling": 9, | |
| "BalanceBeam": 6, | |
| "BandMarching": 4, | |
| "BaseballPitch": 3, | |
| "Basketball": 8, | |
| "BasketballDunk": 1, | |
| "BenchPress": 0 | |
| }, | |
| "layer_norm_eps": 1e-06, | |
| "mlp_ratio": 4, | |
| "model_type": "vjepa2", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "num_pooler_layers": 3, | |
| "patch_size": 16, | |
| "pred_hidden_size": 384, | |
| "pred_mlp_ratio": 4.0, | |
| "pred_num_attention_heads": 12, | |
| "pred_num_hidden_layers": 12, | |
| "pred_num_mask_tokens": 10, | |
| "pred_zero_init_mask_tokens": true, | |
| "problem_type": "single_label_classification", | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.53.0", | |
| "tubelet_size": 2, | |
| "use_SiLU": false, | |
| "wide_SiLU": true | |
| } | |