Instructions to use karim155/convnext-tiny-224-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karim155/convnext-tiny-224-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="karim155/convnext-tiny-224-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("karim155/convnext-tiny-224-finetuned") model = AutoModelForImageClassification.from_pretrained("karim155/convnext-tiny-224-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 1.1360527276992798, | |
| "best_model_checkpoint": "convnext-tiny-224-finetuned/checkpoint-36", | |
| "epoch": 2.88, | |
| "eval_steps": 500, | |
| "global_step": 36, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.8, | |
| "grad_norm": 2.1323318481445312, | |
| "learning_rate": 4.0625000000000005e-05, | |
| "loss": 1.5112, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 0.96, | |
| "eval_accuracy": { | |
| "accuracy": 0.4525 | |
| }, | |
| "eval_logLoss": 1.3027347326278687, | |
| "eval_loss": 1.3027344942092896, | |
| "eval_runtime": 97.0576, | |
| "eval_samples_per_second": 4.121, | |
| "eval_steps_per_second": 0.134, | |
| "step": 12 | |
| }, | |
| { | |
| "epoch": 1.6, | |
| "grad_norm": 1.6718777418136597, | |
| "learning_rate": 2.5e-05, | |
| "loss": 1.278, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": { | |
| "accuracy": 0.51 | |
| }, | |
| "eval_logLoss": 1.161118984222412, | |
| "eval_loss": 1.161118984222412, | |
| "eval_runtime": 9.8941, | |
| "eval_samples_per_second": 40.428, | |
| "eval_steps_per_second": 1.314, | |
| "step": 25 | |
| }, | |
| { | |
| "epoch": 2.4, | |
| "grad_norm": 1.342809796333313, | |
| "learning_rate": 9.375000000000001e-06, | |
| "loss": 1.18, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 2.88, | |
| "eval_accuracy": { | |
| "accuracy": 0.52 | |
| }, | |
| "eval_logLoss": 1.1360526084899902, | |
| "eval_loss": 1.1360527276992798, | |
| "eval_runtime": 11.1011, | |
| "eval_samples_per_second": 36.033, | |
| "eval_steps_per_second": 1.171, | |
| "step": 36 | |
| }, | |
| { | |
| "epoch": 2.88, | |
| "step": 36, | |
| "total_flos": 1.1579775919010611e+17, | |
| "train_loss": 1.2937633593877156, | |
| "train_runtime": 628.6336, | |
| "train_samples_per_second": 7.636, | |
| "train_steps_per_second": 0.057 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 36, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 1.1579775919010611e+17, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |