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Chess_Images

Image Classification
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Safetensors
vit
Generated from Trainer
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  1. README.md +127 -0
  2. config.json +40 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +22 -0
  5. runs/Apr02_15-08-32_612341cc7ac0/events.out.tfevents.1712070513.612341cc7ac0.413.0 +3 -0
  6. runs/Apr02_15-17-28_612341cc7ac0/events.out.tfevents.1712071055.612341cc7ac0.413.1 +3 -0
  7. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224-in21k
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: Chess_Images
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9333333333333333
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # Chess_Images
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2460
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+ - Accuracy: 0.9333
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 50
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 2 | 0.3365 | 0.9333 |
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+ | No log | 2.0 | 4 | 0.3018 | 0.9333 |
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+ | No log | 3.0 | 6 | 0.3443 | 0.9667 |
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+ | No log | 4.0 | 8 | 0.2189 | 1.0 |
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+ | 0.213 | 5.0 | 10 | 0.3188 | 0.9667 |
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+ | 0.213 | 6.0 | 12 | 0.2903 | 0.9333 |
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+ | 0.213 | 7.0 | 14 | 0.3398 | 0.9 |
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+ | 0.213 | 8.0 | 16 | 0.3879 | 0.8667 |
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+ | 0.213 | 9.0 | 18 | 0.3023 | 0.9333 |
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+ | 0.2116 | 10.0 | 20 | 0.1857 | 1.0 |
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+ | 0.2116 | 11.0 | 22 | 0.2737 | 0.9667 |
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+ | 0.2116 | 12.0 | 24 | 0.2675 | 1.0 |
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+ | 0.2116 | 13.0 | 26 | 0.2817 | 0.9333 |
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+ | 0.2116 | 14.0 | 28 | 0.4394 | 0.8667 |
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+ | 0.1837 | 15.0 | 30 | 0.3167 | 0.9 |
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+ | 0.1837 | 16.0 | 32 | 0.2795 | 0.9333 |
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+ | 0.1837 | 17.0 | 34 | 0.2315 | 0.9333 |
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+ | 0.1837 | 18.0 | 36 | 0.2266 | 0.9667 |
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+ | 0.1837 | 19.0 | 38 | 0.3199 | 0.9333 |
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+ | 0.1726 | 20.0 | 40 | 0.2553 | 0.9667 |
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+ | 0.1726 | 21.0 | 42 | 0.3804 | 0.9 |
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+ | 0.1726 | 22.0 | 44 | 0.2118 | 0.9667 |
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+ | 0.1726 | 23.0 | 46 | 0.1784 | 1.0 |
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+ | 0.1726 | 24.0 | 48 | 0.2098 | 0.9667 |
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+ | 0.1529 | 25.0 | 50 | 0.1676 | 1.0 |
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+ | 0.1529 | 26.0 | 52 | 0.2980 | 0.9 |
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+ | 0.1529 | 27.0 | 54 | 0.2726 | 0.9667 |
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+ | 0.1529 | 28.0 | 56 | 0.1756 | 1.0 |
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+ | 0.1529 | 29.0 | 58 | 0.2266 | 0.9667 |
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+ | 0.1335 | 30.0 | 60 | 0.3161 | 0.9333 |
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+ | 0.1335 | 31.0 | 62 | 0.2872 | 0.9333 |
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+ | 0.1335 | 32.0 | 64 | 0.2030 | 1.0 |
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+ | 0.1335 | 33.0 | 66 | 0.2297 | 0.9333 |
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+ | 0.1335 | 34.0 | 68 | 0.2876 | 0.9333 |
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+ | 0.1228 | 35.0 | 70 | 0.1432 | 1.0 |
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+ | 0.1228 | 36.0 | 72 | 0.2194 | 0.9667 |
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+ | 0.1228 | 37.0 | 74 | 0.1387 | 1.0 |
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+ | 0.1228 | 38.0 | 76 | 0.1381 | 1.0 |
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+ | 0.1228 | 39.0 | 78 | 0.1540 | 1.0 |
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+ | 0.1324 | 40.0 | 80 | 0.3075 | 0.8667 |
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+ | 0.1324 | 41.0 | 82 | 0.1892 | 1.0 |
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+ | 0.1324 | 42.0 | 84 | 0.1487 | 1.0 |
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+ | 0.1324 | 43.0 | 86 | 0.1515 | 1.0 |
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+ | 0.1324 | 44.0 | 88 | 0.2617 | 0.9333 |
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+ | 0.136 | 45.0 | 90 | 0.1719 | 0.9667 |
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+ | 0.136 | 46.0 | 92 | 0.2501 | 0.9 |
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+ | 0.136 | 47.0 | 94 | 0.1618 | 1.0 |
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+ | 0.136 | 48.0 | 96 | 0.2175 | 0.9667 |
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+ | 0.136 | 49.0 | 98 | 0.2039 | 0.9667 |
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+ | 0.1226 | 50.0 | 100 | 0.2460 | 0.9333 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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+ {
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+ "_name_or_path": "google/vit-base-patch16-224-in21k",
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+ "architectures": [
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+ "ViTForImageClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "encoder_stride": 16,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.0,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "Black king",
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+ "1": "Black knight",
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+ "2": "Black queen",
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+ "3": "White king",
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+ "4": "White knight",
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+ "5": "White queen"
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+ "image_size": 224,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "White knight": "4",
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+ "White queen": "5"
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "vit",
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+ "num_attention_heads": 12,
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+ "num_channels": 3,
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+ "num_hidden_layers": 12,
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+ "patch_size": 16,
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+ "problem_type": "single_label_classification",
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+ "qkv_bias": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.38.2"
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+ }
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