This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the github.com/dzinampini/poultry-excreta-dataset-curation dataset. It achieves the following results on the evaluation set:

Loss: 0.0573
Accuracy: 0.9879
Precision: 0.9879
Recall: 0.9879
F1: 0.9879

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: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.5946	0.0403	50	0.6729	0.7011	0.8113	0.7011	0.6739
0.4068	0.0806	100	0.4977	0.8265	0.8558	0.8265	0.8195
0.3157	0.1210	150	0.2205	0.9391	0.9406	0.9391	0.9357
0.2821	0.1613	200	0.1711	0.9500	0.9506	0.9500	0.9476
0.1202	0.2016	250	0.1623	0.9552	0.9563	0.9552	0.9549
0.2011	0.2419	300	0.1324	0.9601	0.9605	0.9601	0.9602
0.1711	0.2823	350	0.1820	0.9399	0.9412	0.9399	0.9375
0.174	0.3226	400	0.1264	0.9649	0.9655	0.9649	0.9650
0.1809	0.3629	450	0.1286	0.9649	0.9654	0.9649	0.9647
0.1593	0.4032	500	0.1612	0.9528	0.9534	0.9528	0.9516
0.1234	0.4435	550	0.2338	0.9270	0.9337	0.9270	0.9266
0.1306	0.4839	600	0.1261	0.9633	0.9642	0.9633	0.9632
0.083	0.5242	650	0.1240	0.9645	0.9650	0.9645	0.9645
0.1636	0.5645	700	0.1364	0.9613	0.9621	0.9613	0.9594
0.1104	0.6048	750	0.0908	0.9754	0.9756	0.9754	0.9754
0.0844	0.6452	800	0.1125	0.9706	0.9709	0.9706	0.9704
0.2462	0.6855	850	0.1051	0.9673	0.9704	0.9673	0.9682
0.1365	0.7258	900	0.0847	0.9794	0.9795	0.9794	0.9794
0.1524	0.7661	950	0.1066	0.9742	0.9744	0.9742	0.9741
0.1102	0.8065	1000	0.2405	0.9322	0.9501	0.9322	0.9362
0.0807	0.8468	1050	0.0895	0.9786	0.9788	0.9786	0.9786
0.1069	0.8871	1100	0.0799	0.9798	0.9799	0.9798	0.9798
0.1386	0.9274	1150	0.0794	0.9790	0.9792	0.9790	0.9791
0.0975	0.9677	1200	0.0741	0.9798	0.9814	0.9798	0.9803
0.0478	1.0081	1250	0.0974	0.9758	0.9762	0.9758	0.9758
0.0266	1.0484	1300	0.1165	0.9730	0.9734	0.9730	0.9728
0.0597	1.0887	1350	0.0800	0.9786	0.9787	0.9786	0.9785
0.0277	1.1290	1400	0.0969	0.9766	0.9766	0.9766	0.9765
0.1093	1.1694	1450	0.1120	0.9697	0.9709	0.9697	0.9697
0.0123	1.2097	1500	0.0832	0.9790	0.9800	0.9790	0.9793
0.0741	1.25	1550	0.0812	0.9782	0.9784	0.9782	0.9782
0.0707	1.2903	1600	0.0805	0.9806	0.9807	0.9806	0.9805
0.0395	1.3306	1650	0.0717	0.9827	0.9827	0.9827	0.9826
0.0054	1.3710	1700	0.0827	0.9786	0.9788	0.9786	0.9786
0.0569	1.4113	1750	0.0697	0.9818	0.9819	0.9818	0.9818
0.0589	1.4516	1800	0.0975	0.9754	0.9758	0.9754	0.9750
0.0338	1.4919	1850	0.0765	0.9794	0.9796	0.9794	0.9794
0.0664	1.5323	1900	0.0848	0.9810	0.9819	0.9810	0.9812
0.0739	1.5726	1950	0.0751	0.9806	0.9815	0.9806	0.9809
0.0112	1.6129	2000	0.0677	0.9835	0.9835	0.9835	0.9834
0.1157	1.6532	2050	0.0705	0.9818	0.9819	0.9818	0.9818
0.0408	1.6935	2100	0.1215	0.9685	0.9691	0.9685	0.9685
0.053	1.7339	2150	0.0891	0.9794	0.9795	0.9794	0.9794
0.025	1.7742	2200	0.0765	0.9794	0.9796	0.9794	0.9792
0.0658	1.8145	2250	0.0692	0.9843	0.9844	0.9843	0.9841
0.0134	1.8548	2300	0.0640	0.9831	0.9830	0.9831	0.9830
0.0075	1.8952	2350	0.0630	0.9847	0.9847	0.9847	0.9847
0.0518	1.9355	2400	0.0929	0.9770	0.9803	0.9770	0.9778
0.0824	1.9758	2450	0.0848	0.9762	0.9770	0.9762	0.9763
0.0643	2.0161	2500	0.0618	0.9839	0.9840	0.9839	0.9838
0.0166	2.0565	2550	0.0563	0.9863	0.9863	0.9863	0.9863
0.0397	2.0968	2600	0.0695	0.9827	0.9828	0.9827	0.9826
0.0044	2.1371	2650	0.0612	0.9847	0.9847	0.9847	0.9846
0.0624	2.1774	2700	0.0681	0.9855	0.9856	0.9855	0.9854
0.0163	2.2177	2750	0.0791	0.9806	0.9807	0.9806	0.9806
0.0042	2.2581	2800	0.1051	0.9782	0.9784	0.9782	0.9783
0.0037	2.2984	2850	0.0835	0.9814	0.9816	0.9814	0.9815
0.0043	2.3387	2900	0.0779	0.9798	0.9806	0.9798	0.9800
0.0166	2.3790	2950	0.0758	0.9839	0.9839	0.9839	0.9838
0.0222	2.4194	3000	0.0602	0.9863	0.9863	0.9863	0.9863
0.0025	2.4597	3050	0.0714	0.9823	0.9824	0.9823	0.9822
0.0481	2.5	3100	0.0575	0.9859	0.9859	0.9859	0.9858
0.0037	2.5403	3150	0.0592	0.9863	0.9863	0.9863	0.9863
0.003	2.5806	3200	0.0568	0.9867	0.9867	0.9867	0.9867
0.0094	2.6210	3250	0.0635	0.9871	0.9871	0.9871	0.9871
0.0061	2.6613	3300	0.0549	0.9875	0.9875	0.9875	0.9875
0.052	2.7016	3350	0.0650	0.9871	0.9871	0.9871	0.9870
0.0084	2.7419	3400	0.0595	0.9871	0.9871	0.9871	0.9871
0.0295	2.7823	3450	0.0678	0.9851	0.9851	0.9851	0.9851
0.0021	2.8226	3500	0.0584	0.9863	0.9863	0.9863	0.9863
0.0383	2.8629	3550	0.0908	0.9810	0.9813	0.9810	0.9810
0.0067	2.9032	3600	0.0573	0.9879	0.9879	0.9879	0.9879
0.0102	2.9435	3650	0.0608	0.9851	0.9851	0.9851	0.9850
0.0089	2.9839	3700	0.0652	0.9863	0.9863	0.9863	0.9863
0.0015	3.0242	3750	0.0602	0.9875	0.9875	0.9875	0.9875
0.0112	3.0645	3800	0.0609	0.9879	0.9879	0.9879	0.9879
0.0316	3.1048	3850	0.0489	0.9879	0.9879	0.9879	0.9879
0.0021	3.1452	3900	0.0529	0.9859	0.9859	0.9859	0.9859
0.0026	3.1855	3950	0.0567	0.9871	0.9872	0.9871	0.9871
0.0061	3.2258	4000	0.0593	0.9871	0.9871	0.9871	0.9871
0.0017	3.2661	4050	0.0595	0.9871	0.9871	0.9871	0.9871
0.0012	3.3065	4100	0.0623	0.9867	0.9867	0.9867	0.9867
0.0162	3.3468	4150	0.0579	0.9871	0.9871	0.9871	0.9871
0.0017	3.3871	4200	0.0591	0.9863	0.9863	0.9863	0.9863
0.021	3.4274	4250	0.0601	0.9863	0.9863	0.9863	0.9863
0.0008	3.4677	4300	0.0597	0.9871	0.9871	0.9871	0.9871
0.0072	3.5081	4350	0.0587	0.9871	0.9871	0.9871	0.9871
0.0031	3.5484	4400	0.0656	0.9863	0.9863	0.9863	0.9863
0.005	3.5887	4450	0.0686	0.9859	0.9859	0.9859	0.9859
0.0107	3.6290	4500	0.0643	0.9859	0.9859	0.9859	0.9859
0.0007	3.6694	4550	0.0657	0.9855	0.9855	0.9855	0.9855
0.0009	3.7097	4600	0.0632	0.9863	0.9863	0.9863	0.9863
0.0249	3.75	4650	0.0610	0.9859	0.9859	0.9859	0.9859
0.0019	3.7903	4700	0.0609	0.9859	0.9859	0.9859	0.9859
0.0011	3.8306	4750	0.0617	0.9863	0.9863	0.9863	0.9863
0.0009	3.8710	4800	0.0609	0.9867	0.9867	0.9867	0.9867
0.0009	3.9113	4850	0.0608	0.9867	0.9867	0.9867	0.9867
0.0007	3.9516	4900	0.0608	0.9867	0.9867	0.9867	0.9867
0.004	3.9919	4950	0.0608	0.9863	0.9863	0.9863	0.9863

Framework versions

Transformers 4.55.0
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.4

dzinampini/finetuned-vit-for-poultry-excreta-classification

finetuned-vit-for-poultry-excreta-classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dzinampini/
finetuned-vit-for-poultry-excreta-classification