Feature Extraction
Transformers
Safetensors
PerceptionEncoder
English
pe_audio_video
audio-text-retrieval
image-text-retrieval
zero-shot-audio-classification
multimodal-embeddings
Eval Results (legacy)
Instructions to use MC7ever/pe-single-L14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MC7ever/pe-single-L14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MC7ever/pe-single-L14")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("MC7ever/pe-single-L14") model = AutoModel.from_pretrained("MC7ever/pe-single-L14", device_map="auto") - PerceptionEncoder
How to use MC7ever/pe-single-L14 with PerceptionEncoder:
# Use any PE model as a vision encoder import core.vision_encoder.pe as pe model = pe.VisionTransformer.from_config("MC7ever/pe-single-L14", pretrained=True) - Notebooks
- Google Colab
- Kaggle
File size: 850 Bytes
5f5a478 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"feature_extractor": {
"feature_extractor_type": "PeAudioFeatureExtractor",
"feature_size": 1,
"hop_length": 1920,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": true,
"sampling_rate": 48000
},
"processor_class": "PeAudioVideoProcessor",
"video_processor": {
"data_format": "channels_first",
"default_to_square": true,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"do_sample_frames": false,
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"return_metadata": false,
"size": {
"height": 336,
"width": 336
},
"video_processor_type": "PeVideoVideoProcessor"
}
}
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