Instructions to use TanAlexanderlz/RALL_RGBCROP_5e6-poly_test_eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TanAlexanderlz/RALL_RGBCROP_5e6-poly_test_eval with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="TanAlexanderlz/RALL_RGBCROP_5e6-poly_test_eval")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("TanAlexanderlz/RALL_RGBCROP_5e6-poly_test_eval") model = AutoModelForVideoClassification.from_pretrained("TanAlexanderlz/RALL_RGBCROP_5e6-poly_test_eval") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "VideoMAEForVideoClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "decoder_hidden_size": 384, | |
| "decoder_intermediate_size": 1536, | |
| "decoder_num_attention_heads": 6, | |
| "decoder_num_hidden_layers": 4, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Normal", | |
| "1": "Shoplifting" | |
| }, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "Normal": 0, | |
| "Shoplifting": 1 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "videomae", | |
| "norm_pix_loss": false, | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_frames": 16, | |
| "num_hidden_layers": 12, | |
| "patch_size": 16, | |
| "problem_type": "single_label_classification", | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.52.4", | |
| "tubelet_size": 2, | |
| "use_mean_pooling": true | |
| } | |