dit-small_tobacco3482_kd_MSE

jordyvl/dit-small_tobacco3482_kd_MSE

dit-small_tobacco3482_kd_MSE is a image classification model from jordyvl. Use it when you need a label for an image. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
Model type: beit
Access: Public
Created: Jul 7, 2023
Updated: Jul 7, 2023
SHA: 5922620f

Model card

From the Hugging Face model README

dit-small_tobacco3482_kd_MSE

This model is a fine-tuned version of microsoft/dit-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 6.7275
Accuracy: 0.21
Brier Loss: 0.8834
Nll: 6.7677
F1 Micro: 0.2100
F1 Macro: 0.1146
Ece: 0.2647
Aurc: 0.7666

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	0.96	3	7.1014	0.06	0.9055	7.9056	0.06	0.0114	0.1732	0.9050
No log	1.96	6	6.9659	0.125	0.8970	10.1253	0.125	0.0631	0.2010	0.8465
No log	2.96	9	6.8528	0.075	0.8954	7.0315	0.075	0.0258	0.1912	0.8871
No log	3.96	12	6.8522	0.205	0.8955	7.0990	0.205	0.0776	0.2426	0.7588
No log	4.96	15	6.8465	0.19	0.8959	7.1340	0.19	0.0627	0.2308	0.7536
No log	5.96	18	6.8246	0.205	0.8937	7.1101	0.205	0.0867	0.2410	0.7354
No log	6.96	21	6.8054	0.085	0.8918	7.0215	0.085	0.0435	0.1847	0.8289
No log	7.96	24	6.8025	0.22	0.8879	6.8272	0.22	0.0967	0.2487	0.7438
No log	8.96	27	6.8045	0.21	0.8871	6.3740	0.2100	0.0992	0.2412	0.7634
No log	9.96	30	6.8013	0.22	0.8869	6.9538	0.22	0.1016	0.2495	0.7633
No log	10.96	33	6.7920	0.215	0.8865	6.9670	0.2150	0.0968	0.2549	0.7577
No log	11.96	36	6.7817	0.22	0.8867	6.9953	0.22	0.1004	0.2455	0.7437
No log	12.96	39	6.7729	0.17	0.8884	6.9738	0.17	0.0891	0.2277	0.7865
No log	13.96	42	6.7632	0.2	0.8873	6.9622	0.2000	0.0998	0.2393	0.7413
No log	14.96	45	6.7548	0.215	0.8860	6.9576	0.2150	0.1010	0.2635	0.7189
No log	15.96	48	6.7489	0.22	0.8857	6.8386	0.22	0.1024	0.2665	0.7098
No log	16.96	51	6.7457	0.23	0.8855	6.8730	0.23	0.1129	0.2506	0.7217
No log	17.96	54	6.7455	0.215	0.8864	6.8688	0.2150	0.1058	0.2576	0.7528
No log	18.96	57	6.7424	0.16	0.8861	6.8631	0.16	0.0843	0.2281	0.8036
No log	19.96	60	6.7380	0.155	0.8850	6.8443	0.155	0.0871	0.2315	0.7937
No log	20.96	63	6.7348	0.195	0.8841	6.7769	0.195	0.0949	0.2501	0.7799
No log	21.96	66	6.7317	0.175	0.8838	6.7692	0.175	0.1025	0.2421	0.7797
No log	22.96	69	6.7293	0.175	0.8836	6.7682	0.175	0.1012	0.2452	0.7799
No log	23.96	72	6.7281	0.205	0.8834	6.7672	0.205	0.1132	0.2566	0.7679
No log	24.96	75	6.7275	0.21	0.8834	6.7677	0.2100	0.1146	0.2647	0.7666

Framework versions

Transformers 4.26.1
Pytorch 1.13.1.post200
Datasets 2.9.0
Tokenizers 0.13.2

Task: Image Classification
Library: transformers
Type: beit
Created: Jul 7, 2023
Updated: Jul 7, 2023