Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use dingusagar/vit-base-avengers-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dingusagar/vit-base-avengers-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dingusagar/vit-base-avengers-v2") 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("dingusagar/vit-base-avengers-v2") model = AutoModelForImageClassification.from_pretrained("dingusagar/vit-base-avengers-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 4.0, | |
| "eval_steps": 100, | |
| "global_step": 80, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.5, | |
| "learning_rate": 0.000175, | |
| "loss": 1.2435, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "learning_rate": 0.00015000000000000001, | |
| "loss": 0.7842, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 1.5, | |
| "learning_rate": 0.000125, | |
| "loss": 0.3928, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "learning_rate": 0.0001, | |
| "loss": 0.2174, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 2.5, | |
| "learning_rate": 7.500000000000001e-05, | |
| "loss": 0.1137, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "learning_rate": 5e-05, | |
| "loss": 0.0931, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 3.5, | |
| "learning_rate": 2.5e-05, | |
| "loss": 0.0725, | |
| "step": 70 | |
| }, | |
| { | |
| "epoch": 4.0, | |
| "learning_rate": 0.0, | |
| "loss": 0.0704, | |
| "step": 80 | |
| }, | |
| { | |
| "epoch": 4.0, | |
| "step": 80, | |
| "total_flos": 9.547184254235443e+16, | |
| "train_loss": 0.37343859672546387, | |
| "train_runtime": 76.2656, | |
| "train_samples_per_second": 16.154, | |
| "train_steps_per_second": 1.049 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 80, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 4, | |
| "save_steps": 100, | |
| "total_flos": 9.547184254235443e+16, | |
| "train_batch_size": 16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |