vit_rand_rvl-cdip_N1K

jordyvl/vit_rand_rvl-cdip_N1K

vit_rand_rvl-cdip_N1K 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: vit
Access: Public
Created: Nov 7, 2023
Updated: Nov 8, 2023
SHA: c68f61fc

Model card

From the Hugging Face model README

vit_rand_rvl-cdip_N1K

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

Loss: 3.9745
Accuracy: 0.551
Brier Loss: 0.8083
Nll: 3.9609
F1 Micro: 0.551
F1 Macro: 0.5474
Ece: 0.3805
Aurc: 0.2338

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	1.0	250	2.6207	0.171	0.9078	5.8097	0.171	0.1129	0.0606	0.7132
2.6241	2.0	500	2.4608	0.1727	0.8843	4.0297	0.1727	0.1156	0.0641	0.6991
2.6241	3.0	750	2.4182	0.2177	0.8659	4.1324	0.2177	0.1603	0.0802	0.6191
2.3655	4.0	1000	2.2066	0.2828	0.8237	3.3597	0.2828	0.2456	0.0597	0.5384
2.3655	5.0	1250	2.0873	0.3322	0.7923	3.2747	0.3322	0.2940	0.0613	0.4790
2.0557	6.0	1500	1.9178	0.398	0.7392	3.1146	0.398	0.3639	0.0589	0.3937
2.0557	7.0	1750	1.7861	0.458	0.7025	2.9045	0.458	0.4450	0.0778	0.3497
1.7262	8.0	2000	1.7288	0.4535	0.6821	2.9955	0.4535	0.4322	0.0528	0.3262
1.7262	9.0	2250	1.6881	0.472	0.6673	2.8844	0.472	0.4561	0.0563	0.3120
1.4846	10.0	2500	1.6912	0.4688	0.6633	2.8541	0.4688	0.4540	0.0718	0.3006
1.4846	11.0	2750	1.6094	0.5022	0.6353	2.8239	0.5022	0.4859	0.0759	0.2724
1.1972	12.0	3000	1.5364	0.535	0.6084	2.7911	0.535	0.5162	0.0905	0.2413
1.1972	13.0	3250	1.5683	0.521	0.6228	2.7486	0.521	0.5268	0.1003	0.2559
0.8678	14.0	3500	1.6246	0.5325	0.6246	2.8388	0.5325	0.5295	0.1304	0.2486
0.8678	15.0	3750	1.7502	0.5138	0.6555	2.9705	0.5138	0.5093	0.1750	0.2547
0.5268	16.0	4000	1.8375	0.5215	0.6677	2.9906	0.5215	0.5186	0.2099	0.2535
0.5268	17.0	4250	1.9606	0.524	0.6895	3.2415	0.524	0.5174	0.2425	0.2488
0.2667	18.0	4500	2.0553	0.5305	0.6953	3.2430	0.5305	0.5223	0.2554	0.2434
0.2667	19.0	4750	2.3400	0.5228	0.7369	3.5472	0.5228	0.5101	0.2871	0.2605
0.1513	20.0	5000	2.3720	0.5192	0.7472	3.4681	0.5192	0.5178	0.2982	0.2674
0.1513	21.0	5250	2.4935	0.52	0.7588	3.4578	0.52	0.5104	0.3101	0.2586
0.1164	22.0	5500	2.4916	0.5155	0.7625	3.3908	0.5155	0.5090	0.3129	0.2634
0.1164	23.0	5750	2.5740	0.523	0.7647	3.4298	0.523	0.5235	0.3220	0.2601
0.0883	24.0	6000	2.5887	0.5305	0.7598	3.4432	0.5305	0.5307	0.3194	0.2571
0.0883	25.0	6250	2.7429	0.52	0.7747	3.7692	0.52	0.5132	0.3291	0.2696
0.0739	26.0	6500	2.7728	0.5235	0.7828	3.4718	0.5235	0.5271	0.3399	0.2679
0.0739	27.0	6750	2.7862	0.5335	0.7680	3.5774	0.5335	0.5352	0.3256	0.2651
0.0619	28.0	7000	2.9449	0.5222	0.7964	3.6659	0.5222	0.5165	0.3503	0.2697
0.0619	29.0	7250	2.8872	0.5345	0.7714	3.5298	0.5345	0.5310	0.3376	0.2545
0.0531	30.0	7500	2.9649	0.5232	0.7994	3.6119	0.5232	0.5191	0.3527	0.2714
0.0531	31.0	7750	3.1024	0.5182	0.8112	3.6716	0.5182	0.5206	0.3639	0.2748
0.0446	32.0	8000	3.0895	0.5218	0.8036	3.6731	0.5218	0.5226	0.3609	0.2669
0.0446	33.0	8250	3.1813	0.5202	0.8130	3.6839	0.5202	0.5236	0.3675	0.2637
0.0368	34.0	8500	3.2535	0.5335	0.8011	3.6982	0.5335	0.5302	0.3653	0.2572
0.0368	35.0	8750	3.1969	0.5265	0.8021	3.7238	0.5265	0.5239	0.3649	0.2558
0.0364	36.0	9000	3.3875	0.5165	0.8174	4.0335	0.5165	0.5051	0.3675	0.2645
0.0364	37.0	9250	3.3883	0.5248	0.8168	3.8867	0.5248	0.5152	0.3768	0.2529
0.0338	38.0	9500	3.3876	0.5255	0.8198	3.6397	0.5255	0.5278	0.3791	0.2679
0.0338	39.0	9750	3.3675	0.5282	0.8201	3.7412	0.5282	0.5317	0.3774	0.2561
0.0277	40.0	10000	3.6788	0.5005	0.8597	4.1427	0.5005	0.4880	0.3966	0.2757
0.0277	41.0	10250	3.5608	0.522	0.8299	3.7769	0.522	0.5230	0.3828	0.2749
0.0177	42.0	10500	3.6388	0.5275	0.8242	4.0808	0.5275	0.5134	0.3817	0.2508
0.0177	43.0	10750	3.7068	0.532	0.8199	4.1084	0.532	0.5198	0.3809	0.2480
0.018	44.0	11000	3.7589	0.5258	0.8315	3.9264	0.5258	0.5172	0.3877	0.2624
0.018	45.0	11250	3.7492	0.518	0.8437	3.9257	0.518	0.5180	0.3951	0.2684
0.0186	46.0	11500	3.7641	0.5275	0.8306	3.9749	0.5275	0.5277	0.3877	0.2595
0.0186	47.0	11750	3.8842	0.52	0.8491	4.1807	0.52	0.5182	0.3949	0.2658
0.0159	48.0	12000	3.8731	0.5292	0.8318	3.9345	0.5292	0.5250	0.3902	0.2618
0.0159	49.0	12250	4.0101	0.519	0.8552	4.0796	0.519	0.5198	0.4025	0.2713
0.0118	50.0	12500	3.8631	0.5255	0.8288	4.0855	0.5255	0.5245	0.3891	0.2600
0.0118	51.0	12750	3.7895	0.5415	0.8143	3.9602	0.5415	0.5441	0.3809	0.2506
0.0125	52.0	13000	3.9434	0.523	0.8385	4.2268	0.523	0.5136	0.3951	0.2623
0.0125	53.0	13250	3.9239	0.5275	0.8391	4.0398	0.5275	0.5255	0.3952	0.2632
0.0087	54.0	13500	3.9463	0.5323	0.8307	4.1080	0.5323	0.5275	0.3905	0.2580
0.0087	55.0	13750	3.8462	0.5367	0.8210	3.9693	0.5367	0.5375	0.3825	0.2595
0.0093	56.0	14000	4.0603	0.5208	0.8449	4.2501	0.5208	0.5181	0.4019	0.2683
0.0093	57.0	14250	3.9614	0.5323	0.8240	4.1335	0.5323	0.5265	0.3863	0.2517
0.0082	58.0	14500	3.9553	0.548	0.8125	4.0319	0.548	0.5412	0.3822	0.2414
0.0082	59.0	14750	3.9586	0.5335	0.8325	4.0338	0.5335	0.5314	0.3902	0.2582
0.0069	60.0	15000	4.1072	0.531	0.8422	4.0678	0.531	0.5250	0.3997	0.2574
0.0069	61.0	15250	4.0455	0.5425	0.8173	4.0318	0.5425	0.5415	0.3881	0.2480
0.0054	62.0	15500	4.0208	0.531	0.8325	4.1704	0.531	0.5261	0.3912	0.2517
0.0054	63.0	15750	4.1167	0.5345	0.8325	4.2352	0.5345	0.5292	0.3926	0.2537
0.0054	64.0	16000	4.0246	0.5323	0.8339	4.0084	0.5323	0.5319	0.3940	0.2536
0.0054	65.0	16250	4.0535	0.5417	0.8203	4.1167	0.5417	0.5340	0.3875	0.2464
0.0048	66.0	16500	4.1987	0.5325	0.8371	4.2901	0.5325	0.5215	0.3979	0.2529
0.0048	67.0	16750	4.0956	0.5355	0.8264	4.3477	0.5355	0.5239	0.3889	0.2449
0.004	68.0	17000	3.9999	0.5423	0.8186	4.0645	0.5423	0.5453	0.3877	0.2487
0.004	69.0	17250	4.0824	0.538	0.8229	4.1670	0.538	0.5350	0.3887	0.2461
0.0053	70.0	17500	4.2158	0.5305	0.8479	4.2136	0.5305	0.5287	0.4002	0.2572
0.0053	71.0	17750	4.1586	0.533	0.8355	4.1576	0.533	0.5261	0.3942	0.2512
0.0041	72.0	18000	4.0781	0.5375	0.8296	4.1218	0.5375	0.5341	0.3930	0.2427
0.0041	73.0	18250	4.1389	0.5413	0.8229	4.0890	0.5413	0.5347	0.3918	0.2437
0.0028	74.0	18500	4.0675	0.5415	0.8212	4.0429	0.5415	0.5404	0.3920	0.2415
0.0028	75.0	18750	4.1044	0.5377	0.8294	4.1268	0.5377	0.5335	0.3955	0.2439
0.0027	76.0	19000	4.0731	0.5435	0.8193	4.0913	0.5435	0.5396	0.3892	0.2411
0.0027	77.0	19250	4.0768	0.5455	0.8158	4.0784	0.5455	0.5398	0.3885	0.2389
0.0028	78.0	19500	4.0665	0.5447	0.8187	4.0719	0.5447	0.5390	0.3876	0.2392
0.0028	79.0	19750	4.0475	0.5413	0.8204	4.0408	0.5413	0.5361	0.3927	0.2376
0.0026	80.0	20000	4.0176	0.5457	0.8101	4.0504	0.5457	0.5424	0.3844	0.2376
0.0026	81.0	20250	4.0408	0.5427	0.8181	4.0458	0.5427	0.5385	0.3888	0.2385
0.0027	82.0	20500	4.0392	0.5427	0.8207	4.0317	0.5427	0.5387	0.3897	0.2392
0.0027	83.0	20750	4.0163	0.545	0.8145	4.0292	0.545	0.5403	0.3868	0.2375
0.0026	84.0	21000	4.0057	0.5437	0.8165	4.0096	0.5437	0.5404	0.3867	0.2380
0.0026	85.0	21250	4.0096	0.544	0.8140	4.0733	0.544	0.5404	0.3861	0.2368
0.0026	86.0	21500	3.9696	0.5487	0.8087	4.0527	0.5487	0.5435	0.3824	0.2352
0.0026	87.0	21750	3.9826	0.5495	0.8103	4.0353	0.5495	0.5460	0.3820	0.2362
0.0025	88.0	22000	4.0171	0.5455	0.8147	4.0540	0.5455	0.5402	0.3865	0.2359
0.0025	89.0	22250	3.9745	0.5455	0.8138	3.9683	0.5455	0.5439	0.3867	0.2357
0.0025	90.0	22500	3.9811	0.5473	0.8098	3.9749	0.5473	0.5437	0.3842	0.2346
0.0025	91.0	22750	3.9800	0.5475	0.8122	3.9502	0.5475	0.5450	0.3839	0.2353
0.0025	92.0	23000	3.9844	0.5473	0.8103	3.9825	0.5473	0.5425	0.3840	0.2347
0.0025	93.0	23250	3.9876	0.5485	0.8107	3.9624	0.5485	0.5441	0.3826	0.2343
0.0025	94.0	23500	3.9751	0.5485	0.8086	3.9791	0.5485	0.5450	0.3831	0.2337
0.0025	95.0	23750	3.9765	0.548	0.8087	3.9863	0.548	0.5440	0.3839	0.2336
0.0024	96.0	24000	3.9764	0.5507	0.8077	3.9676	0.5507	0.5473	0.3807	0.2339
0.0024	97.0	24250	3.9695	0.549	0.8082	3.9494	0.549	0.5456	0.3819	0.2346
0.0023	98.0	24500	3.9733	0.5497	0.8080	3.9599	0.5497	0.5462	0.3815	0.2338
0.0023	99.0	24750	3.9727	0.5505	0.8081	3.9563	0.5505	0.5469	0.3807	0.2339
0.0023	100.0	25000	3.9745	0.551	0.8083	3.9609	0.551	0.5474	0.3805	0.2338

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: vit
Created: Nov 7, 2023
Updated: Nov 8, 2023