Instructions to use ahmedmohamed55/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedmohamed55/checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="ahmedmohamed55/checkpoints")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("ahmedmohamed55/checkpoints") model = AutoModelForVideoClassification.from_pretrained("ahmedmohamed55/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 912 Bytes
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"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,
"dtype": "float32",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": "no_event",
"1": "ball_hit",
"2": "ball_bounced"
},
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"ball_bounced": 2,
"ball_hit": 1,
"no_event": 0
},
"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,
"qkv_bias": true,
"transformers_version": "5.13.1",
"tubelet_size": 2,
"use_cache": false,
"use_mean_pooling": true
}
|