Instructions to use lamnt2008/car_brands_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lamnt2008/car_brands_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="lamnt2008/car_brands_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("lamnt2008/car_brands_classification") model = AutoModelForImageClassification.from_pretrained("lamnt2008/car_brands_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Delete train_args.yaml
Browse files- train_args.yaml +0 -130
train_args.yaml
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aa: rand-m10-inc1-mstd101
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amp: true
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amp_dtype: float16
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amp_impl: native
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aot_autograd: false
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apex_amp: false
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aug_repeats: 0
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aug_splits: 0
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batch_size: 96
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bce_loss: false
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bce_target_thresh: null
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bn_eps: null
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bn_momentum: null
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channels_last: false
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checkpoint_hist: 10
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class_map: ''
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clip_grad: 3.0
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clip_mode: norm
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color_jitter: 0.4
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cooldown_epochs: 10
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crop_pct: 1.0
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cutmix: 0.0
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cutmix_minmax: null
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data_dir: /data/imagenet/
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dataset: ''
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dataset_download: false
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decay_epochs: 100
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decay_milestones:
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- 30
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- 60
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decay_rate: 0.1
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dist_bn: reduce
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drop: 0.0
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drop_block: null
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drop_connect: null
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drop_path: 0.1
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dynamo: false
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dynamo_backend: null
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epoch_repeats: 0.0
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epochs: 50
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eval_metric: top1
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experiment: ''
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fast_norm: false
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fuser: ''
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gp: null
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grad_checkpointing: true
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hflip: 0.5
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img_size: 384
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in_chans: null
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initial_checkpoint: ''
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input_size: null
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interpolation: ''
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jsd_loss: false
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layer_decay: 0.75
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local_rank: 0
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log_interval: 50
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log_wandb: false
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lr: 0.0
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lr_base: 0.0001
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lr_base_scale: ''
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lr_base_size: 4096
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lr_cycle_decay: 0.5
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lr_cycle_limit: 1
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lr_cycle_mul: 1.0
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lr_k_decay: 1.0
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lr_noise:
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- 0.1
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- 1.0
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lr_noise_pct: 0.67
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lr_noise_std: 1.0
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mean: null
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min_lr: 5.0e-07
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mixup: 0.0
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mixup_mode: batch
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mixup_off_epoch: 0
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mixup_prob: 1.0
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mixup_switch_prob: 0.5
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model: vit_base_patch16_clip_224.openai_ft_in12k
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model_ema: true
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model_ema_decay: 0.9998
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model_ema_force_cpu: false
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momentum: 0.9
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native_amp: false
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no_aug: false
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no_ddp_bb: false
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no_prefetcher: false
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no_resume_opt: false
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num_classes: 107
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opt: adamw
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opt_betas: null
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opt_eps: null
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output: ''
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patience_epochs: 10
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pin_mem: false
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pretrained: true
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ratio:
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- 0.75
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- 1.3333333333333333
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recount: 1
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recovery_interval: 0
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remode: pixel
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reprob: 0.3
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resplit: false
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resume: ''
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save_images: false
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scale:
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- 0.08
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- 1.0
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sched: cosine
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sched_on_updates: false
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seed: 42
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smoothing: 0.1
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split_bn: false
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start_epoch: null
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std: null
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sync_bn: false
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torchscript: false
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train_interpolation: random
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train_split: train
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tta: 0
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use_multi_epochs_loader: false
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val_split: validation
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validation_batch_size: null
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vflip: 0.0
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warmup_epochs: 10
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warmup_lr: 1.0e-06
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warmup_prefix: false
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weight_decay: 0.05
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worker_seeding: all
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workers: 8
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