Image Classification
Transformers
Safetensors
English
metaclip_2
text-generation-inference
open-scene
Instructions to use prithivMLmods/MetaCLIP-2-Open-Scene with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/MetaCLIP-2-Open-Scene with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/MetaCLIP-2-Open-Scene") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/MetaCLIP-2-Open-Scene") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/MetaCLIP-2-Open-Scene") - Notebooks
- Google Colab
- Kaggle
File size: 1,297 Bytes
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"best_metric": 0.18303145468235016,
"best_model_checkpoint": "metaclip-2-image-classification/checkpoint-511",
"epoch": 1.0,
"eval_steps": 500,
"global_step": 511,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
"epoch": 0.9784735812133072,
"grad_norm": 13.377490043640137,
"learning_rate": 1.5496489468405215e-05,
"loss": 0.3912,
"step": 500
},
{
"epoch": 1.0,
"eval_accuracy": 0.933435301315387,
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"eval_model_preparation_time": 0.013,
"eval_runtime": 81.9678,
"eval_samples_per_second": 199.408,
"eval_steps_per_second": 24.937,
"step": 511
}
],
"logging_steps": 500,
"max_steps": 2044,
"num_input_tokens_seen": 0,
"num_train_epochs": 4,
"save_steps": 500,
"stateful_callbacks": {
"TrainerControl": {
"args": {
"should_epoch_stop": false,
"should_evaluate": false,
"should_log": false,
"should_save": true,
"should_training_stop": false
},
"attributes": {}
}
},
"total_flos": 3.187576517078016e+17,
"train_batch_size": 32,
"trial_name": null,
"trial_params": null
}
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