Instructions to use CVPROJ25/FINETUNED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CVPROJ25/FINETUNED with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="CVPROJ25/FINETUNED") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("CVPROJ25/FINETUNED") model = AutoModelForImageClassification.from_pretrained("CVPROJ25/FINETUNED", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 2, checkpoint
Browse files
last-checkpoint/model.safetensors
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last-checkpoint/trainer_state.json
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"best_global_step": 16,
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"best_metric": 0.8204972743988037,
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"best_model_checkpoint": "./finetuning/checkpoint-16",
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"epoch":
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"eval_samples_per_second": 19.738,
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"step": 16
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"logging_steps": 10,
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"TrainerControl": {
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"attributes": {}
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"best_global_step": 16,
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"best_metric": 0.8204972743988037,
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"best_model_checkpoint": "./finetuning/checkpoint-16",
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"epoch": 2.0,
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"eval_steps": 500,
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"global_step": 32,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"eval_samples_per_second": 19.738,
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"eval_steps_per_second": 1.234,
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"step": 16
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{
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"epoch": 1.25,
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"grad_norm": 1.2275031805038452,
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"learning_rate": 4.40625e-05,
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"loss": 0.5976850986480713,
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"eval_accuracy": 0.784,
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"eval_loss": 0.837196409702301,
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"eval_runtime": 98.979,
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"eval_samples_per_second": 20.206,
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"eval_steps_per_second": 1.263,
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"step": 32
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"logging_steps": 10,
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"early_stopping_threshold": 0.0
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"attributes": {
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"early_stopping_patience_counter": 1
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"TrainerControl": {
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"attributes": {}
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"total_flos": 1.250962187747328e+18,
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"train_batch_size": 512,
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