Instructions to use jatinmehra/Accident-Detection-using-Dashcam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jatinmehra/Accident-Detection-using-Dashcam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="jatinmehra/Accident-Detection-using-Dashcam")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("jatinmehra/Accident-Detection-using-Dashcam") model = AutoModelForVideoClassification.from_pretrained("jatinmehra/Accident-Detection-using-Dashcam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "MCG-NJU/videomae-large-finetuned-kinetics", | |
| "architectures": [ | |
| "VideoMAEForVideoClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "decoder_hidden_size": 512, | |
| "decoder_intermediate_size": 2048, | |
| "decoder_num_attention_heads": 8, | |
| "decoder_num_hidden_layers": 12, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 1024, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "videomae", | |
| "norm_pix_loss": true, | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_frames": 16, | |
| "num_hidden_layers": 24, | |
| "patch_size": 16, | |
| "problem_type": "single_label_classification", | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.47.0", | |
| "tubelet_size": 2, | |
| "use_mean_pooling": true | |
| } | |