|
191 | 191 | }
|
192 | 192 | ],
|
193 | 193 | "source": [
|
194 |
| - "filename_path = 'hub://activeloop/optical-handwritten-digits-train'\n", |
| 194 | + "filename_path = 'hub://<username>/optical-handwritten-digits-train'\n", |
195 | 195 | "ds = hub.dataset(filename_path)\n",
|
196 | 196 | "\n",
|
197 | 197 | "with ds: \n",
|
|
225 | 225 | }
|
226 | 226 | ],
|
227 | 227 | "source": [
|
228 |
| - "filename_path = 'hub://activeloop/optical-handwritten-digits-test'\n", |
| 228 | + "filename_path = 'hub://<username>/optical-handwritten-digits-test'\n", |
229 | 229 | "ds = hub.dataset(filename_path)\n",
|
230 | 230 | "\n",
|
231 | 231 | "with ds: \n",
|
|
287 | 287 | },
|
288 | 288 | "outputs": [],
|
289 | 289 | "source": [
|
290 |
| - "train_path = 'hub://activeloop/optical-handwritten-digits-train'\n", |
291 |
| - "test_path = 'hub://activeloop/optical-handwritten-digits-test'" |
| 290 | + "train_path = 'hub://<username>/optical-handwritten-digits-train'\n", |
| 291 | + "test_path = 'hub://<username>/optical-handwritten-digits-test'" |
292 | 292 | ]
|
293 | 293 | },
|
294 | 294 | {
|
295 | 295 | "cell_type": "code",
|
296 |
| - "execution_count": 40, |
| 296 | + "execution_count": null, |
297 | 297 | "metadata": {
|
298 | 298 | "colab": {
|
299 | 299 | "base_uri": "https://localhost:8080/",
|
|
302 | 302 | "id": "uD1PMqeb4NTX",
|
303 | 303 | "outputId": "df6eb312-2444-4d68-86e1-25704153c289"
|
304 | 304 | },
|
305 |
| - "outputs": [ |
306 |
| - { |
307 |
| - "name": "stdout", |
308 |
| - "output_type": "stream", |
309 |
| - "text": [ |
310 |
| - "hub://activeloop/optical-handwritten-digits-train loaded successfully.\n", |
311 |
| - "This dataset can be visualized at https://app.activeloop.ai/activeloop/optical-handwritten-digits-train.\n" |
312 |
| - ] |
313 |
| - }, |
314 |
| - { |
315 |
| - "data": { |
316 |
| - "image/png": "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", |
317 |
| - "text/plain": [ |
318 |
| - "<Figure size 432x288 with 1 Axes>" |
319 |
| - ] |
320 |
| - }, |
321 |
| - "metadata": { |
322 |
| - "needs_background": "light" |
323 |
| - }, |
324 |
| - "output_type": "display_data" |
325 |
| - } |
326 |
| - ], |
| 305 | + "outputs": [], |
327 | 306 | "source": [
|
328 | 307 | "ds = hub.dataset(train_path)\n",
|
329 | 308 | "image = ds.images[0].numpy()\n",
|
|
335 | 314 | },
|
336 | 315 | {
|
337 | 316 | "cell_type": "code",
|
338 |
| - "execution_count": 41, |
| 317 | + "execution_count": null, |
339 | 318 | "metadata": {
|
340 | 319 | "colab": {
|
341 | 320 | "base_uri": "https://localhost:8080/",
|
|
344 | 323 | "id": "Qj6catLfDeXN",
|
345 | 324 | "outputId": "c594777a-9090-48d7-cb35-73f9220d83d5"
|
346 | 325 | },
|
347 |
| - "outputs": [ |
348 |
| - { |
349 |
| - "name": "stdout", |
350 |
| - "output_type": "stream", |
351 |
| - "text": [ |
352 |
| - "hub://activeloop/optical-handwritten-digits-test loaded successfully.\n", |
353 |
| - "This dataset can be visualized at https://app.activeloop.ai/activeloop/optical-handwritten-digits-test.\n" |
354 |
| - ] |
355 |
| - }, |
356 |
| - { |
357 |
| - "data": { |
358 |
| - "image/png": 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|
359 |
| - "text/plain": [ |
360 |
| - "<Figure size 432x288 with 1 Axes>" |
361 |
| - ] |
362 |
| - }, |
363 |
| - "metadata": { |
364 |
| - "needs_background": "light" |
365 |
| - }, |
366 |
| - "output_type": "display_data" |
367 |
| - } |
368 |
| - ], |
| 326 | + "outputs": [], |
369 | 327 | "source": [
|
370 | 328 | "ds = hub.dataset(test_path)\n",
|
371 | 329 | "image = ds.images[0].numpy()\n",
|
|
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