README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: plant-seedlings-model-ConvNet-all-train
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9429097605893186

plant-seedlings-model-ConvNet-all-train

This model is a fine-tuned version of facebook/convnext-tiny-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.2056
• Accuracy: 0.9429

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 14
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4109	0.25	100	0.5246	0.8195
0.248	0.49	200	0.4594	0.8459
0.3389	0.74	300	0.4443	0.8551
0.4217	0.98	400	0.4500	0.8490
0.2815	1.23	500	0.3939	0.8588
0.3077	1.47	600	0.3813	0.8643
0.5098	1.72	700	0.4276	0.8576
0.3191	1.97	800	0.4218	0.8570
0.2761	2.21	900	0.3404	0.8883
0.2184	2.46	1000	0.3226	0.8889
0.3106	2.7	1100	0.3621	0.8729
0.3118	2.95	1200	0.3656	0.8797
0.2857	3.19	1300	0.3123	0.9012
0.2193	3.44	1400	0.2907	0.9048
0.2959	3.69	1500	0.3544	0.8840
0.3176	3.93	1600	0.3389	0.8877
0.2927	4.18	1700	0.3418	0.8864
0.2719	4.42	1800	0.3558	0.8821
0.2176	4.67	1900	0.3374	0.8981
0.1912	4.91	2000	0.3092	0.8999
0.2272	5.16	2100	0.2902	0.9128
0.175	5.41	2200	0.3002	0.9134
0.1513	5.65	2300	0.3356	0.8999
0.1439	5.9	2400	0.2954	0.9061
0.2341	6.14	2500	0.3343	0.8993
0.2178	6.39	2600	0.2891	0.9122
0.1731	6.63	2700	0.3235	0.9030
0.19	6.88	2800	0.2938	0.9042
0.1168	7.13	2900	0.2937	0.9110
0.1528	7.37	3000	0.2963	0.9104
0.1374	7.62	3100	0.2929	0.9085
0.2204	7.86	3200	0.3257	0.9048
0.1519	8.11	3300	0.2683	0.9171
0.0711	8.35	3400	0.2609	0.9251
0.1019	8.6	3500	0.2523	0.9251
0.1764	8.85	3600	0.2769	0.9202
0.0849	9.09	3700	0.2668	0.9214
0.2077	9.34	3800	0.2914	0.9165
0.2543	9.58	3900	0.2507	0.9251
0.0347	9.83	4000	0.2333	0.9269
0.0731	10.07	4100	0.2598	0.9269
0.238	10.32	4200	0.2675	0.9294
0.1114	10.57	4300	0.2317	0.9269
0.0836	10.81	4400	0.2344	0.9288
0.0598	11.06	4500	0.2499	0.9276
0.0488	11.3	4600	0.2361	0.9288
0.1437	11.55	4700	0.2551	0.9282
0.0773	11.79	4800	0.2276	0.9294
0.1013	12.04	4900	0.2537	0.9288
0.0943	12.29	5000	0.2368	0.9331
0.0538	12.53	5100	0.2157	0.9349
0.0425	12.78	5200	0.2330	0.9411
0.1301	13.02	5300	0.2564	0.9331
0.062	13.27	5400	0.2193	0.9417
0.1012	13.51	5500	0.1873	0.9466
0.1643	13.76	5600	0.2056	0.9429

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3