Image Feature Extraction
Transformers
Safetensors
English
keural_vision
vision
vision-encoder
image-text
contrastive-learning
knowledge-distillation
adaptive-tokenization
Eval Results (legacy)
Instructions to use mkd-dev/keural-vision-encoder-mid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkd-dev/keural-vision-encoder-mid with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="mkd-dev/keural-vision-encoder-mid")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mkd-dev/keural-vision-encoder-mid", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,995 Bytes
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"architectures": [
"KeuralVisionModel"
],
"default_token_budget": 512,
"depth": 24,
"dtype": "float32",
"embed_dim": 768,
"keural_config": {
"data": {
"captions_path": "/home/work/keural-model/vision-encoder-dataset/indexes/datacomp_train.tsv",
"dataset_name": "cc3m",
"extra_sources": [],
"image_buckets": [
[
384,
384
],
[
448,
448
]
],
"images_root": "/home/work/keural-model/vision-encoder-dataset/datacomp_medium",
"max_caption_length": 77,
"num_workers": 6,
"pixel_mean": [
0.485,
0.456,
0.406
],
"pixel_std": [
0.229,
0.224,
0.225
],
"text_encoder_name": "openai/clip-vit-large-patch14"
},
"distillation": {
"enabled": true,
"internvit_embed_dim": 1024,
"internvit_model": "OpenGVLab/InternViT-300M-448px",
"lambda_internvit": 0.5,
"lambda_siglip": 0.5,
"siglip_embed_dim": 1152,
"siglip_model": "google/siglip-so400m-patch14-384"
},
"loss": {
"lambda_distill": 0.5,
"lambda_hct": 1.0,
"lambda_sal": 0.5,
"learnable_bias_init": -10.0,
"learnable_temperature_init": 2.3,
"primary": "sigmoid_siglip",
"saliency_beta": 0.005,
"saliency_lambda_smooth": 4.0
},
"name": "keural-mid",
"saliency": {
"hidden_channels": 192
},
"spatial": {
"depth": 24,
"embed_dim": 768,
"head_dim": 64,
"mlp_ratio": 4,
"norm_eps": 1e-06,
"num_heads": 12,
"rope_base": 10000.0
},
"stem": {
"activation": "gelu",
"channels": [
128,
256,
512,
768
],
"kernel_size": 3
},
"tokenizer": {
"budget_floors": {
"detail": 0,
"global": 8,
"region": 16
},
"budget_split": {
"detail": 0.7,
"global": 0.05,
"region": 0.25
},
"default_token_budget": 512,
"gumbel_temperature_end": 0.05,
"gumbel_temperature_start": 1.0,
"max_token_budget": 2048,
"nms_radius": {
"detail": 1,
"region": 3
},
"saliency_smooth_kernel": {
"detail": 3,
"region": 7
},
"temperature_anneal_steps": 10000,
"warmup_steps_fixed_grid": 4000
},
"training": {
"batch_size_per_device": 768,
"betas": [
0.9,
0.95
],
"gradient_accumulation": 2,
"gradient_checkpointing": true,
"log_every": 100,
"lr": 0.0005,
"lr_schedule": "cosine_with_warmup",
"max_steps": 18000,
"optimizer": "adamw",
"precision": "bfloat16",
"save_every": 1000,
"seed": 42,
"warmup_steps": 2000,
"weight_decay": 0.05
}
},
"max_token_budget": 2048,
"model_type": "keural_vision",
"num_heads": 12,
"transformers_version": "4.57.6"
}
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